ABA on Tap is proud to present Mr. Adam Ventura. (Part 2 of 2)
Adam Ventura, MS, BCBA, is a leader in the integration of artificial intelligence and behavior science. He is the Founder and CEO of Intraverbal AI, a company dedicated to transforming applied behavior analysis (ABA) through AI-powered tools that support ethical decision-making, supervision, and clinical efficiency. Adam's current focus is on leveraging technology to make data-driven insights and innovative systems more accessible to behavior analysts across settings. A graduate of Florida International University (FIU) and a long-standing adjunct professor, Adam blends academic rigor with cutting-edge application. Adam is an established author and speaker, he has contributed extensively to the literature on ethical practices, leadership, and now the responsible integration of AI in behavior analysis.
Adam speaks eloquently about his past experiences leading to his current project and heart work at Intraverbal AI. He has done it all in the field of ABA, and he forges forward with the most modern of technologies at hand. This is an innovative and refreshing brew you can enjoy over and over again, and it's only the first of two full pours. Sit back, enjoy this. tall, cold one, and always analyze responsibly.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_03 (00:00):
Welcome to ABA on Tap, where our goal is to find
(00:14):
the best recipe to brew thesmoothest, coldest, and best
tasting ABA around.
I'm Dan Lowry with Mike Rubio,and join us on our journey as we
look back into the ingredientsto form the best concoction of
ABA on tap.
In this podcast, we will talkabout the history of the ABA
(00:35):
brew, how much to consume toachieve the optimum buzz while
not getting too drunk, and therecommended pairings to bring to
the table.
So without further ado, sitback, relax, and always analyze
responsibly.
All
SPEAKER_02 (00:52):
right, all right.
This is your ever-gratefulco-host, Mike Rubio.
Thank you for coming back.
This is part two with Mr.
Adam Ventura of Intraverbal AI.
Enjoy.
SPEAKER_00 (01:04):
Yeah, that's tough.
And I feel like you had twoquestions there.
So I'll start with the firstone.
So I'm assuming when you sayKPM, key performance metrics?
SPEAKER_03 (01:16):
Correct, yeah.
And like what to evaluate peopleon and say the program's
successful.
Because my experience withworking with private equity was
always just about likeefficiency base.
SPEAKER_00 (01:26):
Yeah.
And so that just becomes ascorecard, right?
And I think it should be abalanced scorecard.
And this was one of the keylessons that I learned from work
doing OBM consulting for a fewyears is the importance of
employee and organizationalscorecards being balanced.
You know, so yes, you can't, doyou need profit measures on your
(01:52):
scorecard?
Yeah, you do.
If you're not making a profit,you can't be in business.
And people need to be able tosay that out loud without
getting pilloried foridentifying the obvious.
However, when you create thesescorecards, that can't be the
only metric on there.
So yes, net revenue isimportant, but you also need to
(02:13):
have...
measures of social validity onthere are the families happy
more or less maybe that's thewrong word but are the families
satisfied with the service thatyou are producing then you get
into and I think Mike you justbrought it up like how do you
define quality service and Ithink there's some folks in our
(02:36):
field that don't behave likehonest brokers when we have
those discussions are there somefair points that yeah it's
really hard to measure becausefor example you can't or you're
going to have trouble using apercentage measure because some
behaviors are uh much moredeleterious to the health and
(03:01):
wellness of the client thanothers so you can say oh i
reduced um some i i reducedstereotypy by 80 okay that's
great but if you have another umtopography like elopement where
the client is running out intothe middle of the street, I
reduce that by 80%.
That's good.
(03:22):
But if he's still running outinto the middle of a busy
intersection, 20% of the time,that's still a life threatening
behavior.
So coming up with a, and I'mworking in technology now, so
I'll call it an algorithm.
Coming up with an algorithm forthese insurance companies to use
is a big challenge.
(03:42):
However, I don't think it's aninsurmountable challenge.
I think it's something that ifyou get some of the smartest
behavior analysts in our fieldtogether, I think they could
figure out.
And are there gonna be some edgecases?
Yeah, there is.
But I think if we don't wanna doharm to our science, and that's
(04:04):
really kind of my professionalmission is disseminating and
moving forward the science, Ithink we need to be very careful
with this because if people aredoing bad ABA therapy out there,
it's going to reflect poorly onour science and then we're gonna
start regressing scientificallyand as a field more broadly.
SPEAKER_03 (04:28):
That makes a lot of sense.
My last question about yourbusiness and then maybe we can
transition or I can pass it toyou, Mike.
I'm sorry, I've been asking alot of questions here.
So you created this business.
had these conversations,expanded to 300 employees, and
then you did sell, you said, toprivate equity.
So can you talk about maybe yourthought process with that, your
(04:49):
thoughts on them being able tomaintain your vision and
mission, or even if that was aconcern, and maybe if they were
able to be successful with thator not?
SPEAKER_00 (04:57):
Yeah.
I didn't really speak publiclyuntil recently on the Beyond the
Science podcast about why I soldmy company, but I had a...
Family tragedy at the time.
I had no interest in selling mycompany.
I wanted to continue to grow it.
It was my life's purpose.
And I had a family tragedy.
(05:19):
Somebody in my family passedaway unexpectedly.
No, no, no.
It was seven years ago, eightyears ago, whatever it was.
And I realized that I didn'thave...
I didn't have a greatrelationship with my family at
all.
There were some people in myimmediate family that I hadn't
spoken to in a decade.
(05:40):
And I realigned my priorities.
I really felt like my legacy wasmy business.
And when that happened, I madethe decision that I needed to
have a relationship with myfamily.
I mean, I hadn't...
I hadn't spoken to my dad in 12years.
I hadn't spoken to my sisters inlike nine years.
(06:02):
I hadn't spoken to my mom in, Idon't know, five, six, seven
years, something like that.
And while the tragedy wastragic, it opened up an
opportunity for me to besuccessful personally.
And all I could think about, allthat mattered to me was
professional success.
(06:23):
I had no relationship successeither.
I was a mess professionally.
personally dating.
I was a hot mess dating.
I had no relationship with myfamily.
And I just made a transition atthat point when that tragedy
struck to grow up personally.
(06:43):
And I really made thattransition and discovered that
my immortality project, mylegacy wasn't my business.
It was my family.
It was having kids.
And it was getting married.
It was having a stablerelationship.
And when I made that transition,I realized that I couldn't do
(07:06):
that myself.
I needed help.
And I tried to transitionsomebody into the CEO role so
that I could address what hadjust happened.
Because when you have a familytragedy, a lot of people need
help.
And I needed to be there morefor my family.
Whenever something terriblehappens and somebody has to step
(07:27):
down from leadership role in anyindustry, they're like, oh, I
have to spend more time with myfamily.
I literally had to spend moretime with my family.
And I put somebody else in therole.
And as soon as I did that, wegot a Medicaid audit in Florida.
And it was a mess.
And as it turned out, Medicaiddecided, Florida Medicaid at
(07:49):
that time decided not to pay usfor about five months.
And they owed us seven figuresof money.
And I didn't have that money inthe couch cushions in my home.
So luckily, we managed to sendeverything over that they asked
(08:10):
for, got through it.
Funny enough, I always liketelling this story.
After we sold the company,Private Equity went to Florida
Medicaid and said, listen, we'rebuying Adam's company.
And they told them at the time,they're like, oh, yeah, we
audited his company and he's oneof our best providers.
And I wanted to strangle thembecause it would have been nice
(08:31):
if they would have realized thatearlier.
But that's OK.
So I the company that bought us,we were their platform company
and the first company that theybought and everything that they
promised that they would do,they did.
And.
They grew to be a very bigcompany and then ended up
(08:55):
selling again after that.
I understand how the businessworks, but I hold no ill will
towards the notion of privateequity.
For as many bad private equitystories that we hear, I know
personally of 20, 25 storiesthat a small ABA provider that
(09:18):
doesn't know how to run abusiness that was this close to
going under, private equityswooped in and saved them from
their business going under.
So I don't have that animositytowards private equity.
Are there some bad behavingactors in private equity?
(09:38):
Yeah.
Are there some private equityplatforms that shouldn't own an
ABA business?
Sure.
But is there somethinginherently wrong with that
business model?
No, I don't think so.
Dan, to your point earlier, theyneed to refine their scorecards,
(09:59):
I think some of them, to includeclinical quality, to include
some of those measures of socialvalidity.
And that's where if they getbehavior analysts to work with
them, like I know some ofprivate equity companies do
right now, then things getbetter.
And I guess that's my overallthoughts on private equity and
(10:20):
their involvement in ADA.
UNKNOWN (10:21):
Yeah.
SPEAKER_03 (10:22):
Well, thank you for sharing that story.
That was very insightful, and Iappreciate that.
I was going to
SPEAKER_02 (10:27):
say the same thing.
Thanks for sharing the personalside and then getting into the
private equity piece, and we'revery glad for your experience
there, and very glad to hearsomebody share a positive
experience on private equity,because those aren't the stories
you hear, right?
The stories you hear are all thenegatives.
What is it you think really goeswrong in those moments?
Is it Is it a bad private equityactor usually?
(10:48):
Is it people just not knowinghow to deal with private equity
when they're going through theseinteractions?
What would you tell somebody outthere who is maybe getting an
offer from private equity ormight need help to stay alive?
What would you caution themabout?
What would be your best advice?
SPEAKER_00 (11:07):
There's a lot of...
So I'll answer the last questionfirst.
There's a lot of consultants outthere that will help walk
somebody through a sale of theirbusiness.
Don't feel the other thing Iwould say to those folks is
don't feel bad about wanting tosell your business.
And, you know, I thought I wasgoing to get a lot of pushback
from people because I sold mybusiness in 2018.
(11:30):
And that was really in thebeginning of private equity and
how that stuff happened.
And I actually made the decisionto sell my business in late
2017.
My family tragedy happened inOctober of 2017.
And I made the decision reallywithin a month and a half, two
months, something like that,that I was going to have to step
(11:52):
down.
And I really expected a lot ofpushback.
I really did.
I thought people were going tovilify me and I was going to be
pilloried from a publicstandpoint in our field.
And there was a lot of people, alot of colleagues that I had
that worked in different partsof our field that when I told
(12:12):
them, their response was,congratulations, good for you.
And I couldn't believe I heardit, you know, and I felt like it
was kind of reassuring.
They're like, look, you know, Iguess because this was people,
these were people that knew me.
I lived and breathed my company.
(12:32):
It was the only thing thatmattered to me.
And That turned out to be partof the problem with my personal
life is that's all that matteredto me.
But they knew that for the fewpeople that knew what was going
on in my life, they understoodwhy I was doing it.
So I would say the first thingthat I would say to those folks
(12:52):
is don't feel bad about doingit.
Second thing I would say is finda consultant that can explain
everything to you, howeverything works.
I think that that's reallyhelpful.
I would say part of the reasonwhy this goes wrong so many of
these times is because a lot ofthese private equity companies
bring a medical model into ABA.
(13:16):
I don't agree with the idea thatwe should be making ABA the
medical model that insurancecompanies are used to dealing
with.
I think there's a lot ofproblems with that.
And I think private equitycompanies just swoop in and say,
this is just like the...
the string of dentist locationsthat we purchased last year and
(13:37):
then sold for a profit.
We can run it the same way as wedo You know, a dentist's office
or an orthopedist's office orsomething like that.
And it just doesn't work thatway.
We have a very unique field witha tiered service delivery model
that doesn't exist in otherfields.
So I think the smartest privateequity companies are the ones
(14:01):
that hire behavior analysts tohelp them make better decisions.
I think if they don't, that'swhere the problems come in.
I think most behavior analysts,especially seasoned behavior
analysts that have been in thefield for a while, that care
about the clients, that careabout the science will sit down
with the private equitycompanies and say, yes, you need
(14:22):
to make a profit.
To make a profit, you need to bemore efficient.
Yes, agreed on both of those,but you also need to provide a
good service, and here's what Imean by a good service, and here
are the outcomes that you needto look for.
And if those behavior analystscan communicate that
appropriately and the privateequity companies are willing to
(14:43):
listen, then you get goodoutcomes.
If those two things don'thappen, you get bad outcomes.
SPEAKER_03 (14:48):
So let me ask you this, and please correct me if
I'm wrong, because like I said,I'm not too well-versed in
private equity, but from somethings that I've talked about
with people, they've said thatthat does make sense, but since
private equity a lot of timesisn't there for the long run...
by the time that they're goingto have to pay the piper for the
lack of clinical quality, theywill have already sold and it
(15:10):
won't be their problem anymore.
It'll be on to somebody else.
So if they can just make it moreefficient, regardless of the
clinical quality, get themetrics up and sell, that'll be
on somebody else's doorstep.
SPEAKER_00 (15:22):
I don't know how often that happens now.
At least I think that they'rebecoming, these private equity
companies have become They'vebeen doing it now for almost a
decade.
So they're starting to figurethings out, number one.
Number two, if you sell acompany, there's a whole bunch
of provisions.
And I learned about this when Iwas selling my company.
(15:44):
There's a whole bunch ofprovisions in those agreements
that say that if things fallapart three months in, you're in
a lot of trouble as a seller.
So I think...
Private equity companies arebecoming wise to this and
realizing that it's not just athree-year turnaround anymore,
that it's a three- to five-yearturnaround, maybe five- to
(16:06):
seven-year turnaround, and thatit's bad business to run a bad
business.
So if you're running a businessthat all you care about is
billable hours, and then all ofa sudden audits show up, clients
get hurt, social media plays abig role in this too.
If you run a crappy company...
(16:27):
Behavior analysts are going tomake it well known in those
Facebook groups and on LinkedInand things like that.
And you become, your brandbecomes known for that.
And anybody that's looking toacquire you, a bigger private
equity company or whomever itis, they're going to do their
due diligence.
They're going to not just lookat your books.
(16:48):
They're going to look at yourbrand.
They're going to look at howpeople have rated you on
Glassdoor.
So I don't think this is 2018,2019, 2020 anymore.
This is 2025.
And those people that run thosebillion dollar private equity
funds, they didn't becomebillionaires by making stupid
(17:12):
decisions.
And I think that they look backand say, you know what?
We saw this company or thatcompany collapse.
Why did that happen?
In a lot of cases, it's becausethey didn't care about clinical
quality or treated theiremployees like crap.
SPEAKER_03 (17:28):
Thank you for clarifying.
I appreciate that.
Like I said, I'm pretty ignorantto the whole thing, so I'm just
saying what I hear, and thankyou for clarifying that.
That was very insightful.
SPEAKER_02 (17:35):
Yeah, very good information.
So you sold, and I'm going toskip over a few things here just
in the interest of time.
But now you're delving intoartificial intelligence and how
that's going to fit into thefuture of ABA, whether that's
autism treatment or whateverthat may present.
And I think it behooves us todiversify more and more in how
(17:58):
we're actually employingbehavior analysis to help
people.
Talk to us a little bit aboutwhat you're doing now, what got
you interested in that overtime, and then anything else you
want to share and we'll help youunpack it and discuss it.
Yes, that was
SPEAKER_00 (18:15):
great.
Thank you for the question.
Actually, one of the businessesthat I moved into after I sold
my ABA therapy company wasopening a practice management
software company.
I made a lot of mistakes withthat company.
I learned what I like doing andwhat I don't like doing.
(18:36):
When we were talking maybe about30 or 40 minutes ago, I talked
about how much I hate ABA admin.
One of the things you have to dowhen you start an ABA practice
management software company isyou have to deal with a lot of
admin and you have to reallybecome a medical billing expert.
It's interesting because I havea software team that I've been
(18:56):
working with for 10 years.
Something that our leaddeveloper explained to me a few
years ago and he's like, Adam,medical billing software is its
own specialty because it's socomplicated.
You have to know so much aboutthat industry and you have to
merge two fields together,software and insurance
(19:18):
companies.
It's very difficult.
Um, so I realized that, um, Ididn't want to do practice
management.
It just wasn't something that Iwanted to do, but I liked
technology a lot, a lot.
And when AI really startedcoming on the scene, uh, really
even before, uh, chat GPT,everybody knows chat GPT and it
(19:40):
kind of burst onto the scene afew years ago or a couple of
years ago.
Um, I started talking a lot tomy development team about.
where we can move forward andthe answer that they always gave
me was ai and i thought aboutwhat my professional mission is
and my profession professionalmission is to move the science
(20:02):
and practice of behavioranalysis forward that's what
matters to me we were talkingabout the science earlier it
really matters to me i taught atuniversity for 12 years i enjoy
teaching the science i enjoydisseminating it and when some
of those large language modelsreally burst onto the scene in
2022, we started having thosediscussions about how I can
(20:27):
merge my professional missionwith this new emerging
technology.
And where I kind of landed asour initial product offering was
I wanted to make the practice ofbehavior analysis more
evidence-based.
So what do I mean by that?
When we go into a home, aclinic, a group home, a school
(20:50):
to work with a particular kiddo,there's almost exactly zero
behavior analysts that show upwith a binder full of journal
articles.
It's just not going to happen.
And even if you did, it's notpractical.
If you've got a kiddo that'sengaging in that spaghetti
tantrum thing, you can't...
(21:12):
response block that with onehand and page three in the idea
that, because there's tens ofthousands of journal articles
out there, you know, that you'regoing to find the one and the
intervention that's there, it'sjust not going to happen.
So one of the things that wewanted to do was bring, with
(21:33):
this technology, bring thescience into the practice.
And something that we starteddoing was setting up deals with
publishing houses to connectthese AI systems to that
information and making itavailable in the moment.
(21:54):
So you can literally pick up oneof these mobile devices and say,
my client's engaging in this,what should I do?
Now, I want to make this veryclear that, especially if you're
an RBT listening to this, Youneed to do what your behavior
analyst tells you to do, notwhat an AI system tells you to
(22:14):
do.
However, if you're a BCBA andyou're looking for ideas on how
to deal with a unique form ofSIB or you want to set up toilet
training in a unique setting andyou need some ideas, you need a
sounding board to bounce ideasoff of, these types of tools are
(22:35):
the way to do it.
We set up our company calledIntroverbal AI, and we feel like
the name of the company isappropriate because you plug a
whole bunch of mans into oursystem.
You ask questions, you makerequests, and our system gives
answers back.
And that's why we came up withthe
SPEAKER_02 (22:55):
name.
I love it.
That's fantastic.
SPEAKER_00 (22:58):
Thank you.
Thank you.
And with our system, oursystem...
allows you to search throughtens of thousands of journal
articles to find the answers tonot just clinical questions, but
academic questions.
And we took this base model andwe started building on top of
(23:19):
it.
And we created a goals productthat we have that helps you
create goals for your clients.
And then we ended up adding like10,000 client goals or 10,000
clients worth of goals.
It turned into like a millionactual individual goals that it
(23:39):
kind of pulls from.
We developed a functionalbehavior assessment product that
touches on not only those goals,but also the literature as well.
So not to get too deep into theproducts, our system was really
meant to improve the science andpractice of behavior analysis
and we tried to do that with umstarting off with the foundation
(24:04):
of the literature you know whatwere we all taught what was
drilled into our brains ingraduate school do
evidence-based practice alwaysreference the literature you
know if you were in a goodgraduate program you were
reading eight to ten journalarticles a week you know and you
really had to dive in do deepdives into these articles to
(24:27):
make sure you understood whythings were done the way that
they were done and recognizinggood research from bad research.
And that was a big impetus as towhat we wanted to do and I'll
never forget the first time ourlead developer came to me and he
said, okay, we connected the twohere and then I just started
asking questions and it gave meanswers instantaneously and it
(24:50):
gave the in-text citation andthen it gave a full reference
list And it did it in like fourseconds.
And I was like, holy crap, youknow, like just imagine the
difference that we can make withall of this.
And I think that was myintroduction to AI and why I
kind of fell in love with whatwe can do with this technology
(25:14):
and how it can augment, notreplace our practice.
SPEAKER_03 (25:19):
Sold.
I was going to say, I can vibewith that, but you are speaking
this guy's language 100%.
SPEAKER_02 (25:25):
So just, I'm already having visions of, you know, in
all fairness, I can be very,I've been very critical on the
show and in the pastprofessionally about, you know,
you've got these staff meetingsand you end up taking time away
from clients to sit down for twohours and talk about billable
matters and, you know,efficiency.
And you're not, training anybodyyou're not professionally
(25:48):
developing anybody truly in thebread and butter of the the
service delivery and and thatknowledge so the idea that i
mean i think a lot of that whythat happens is it takes time to
develop those training materialsso as a bcba as a manager or
whatever title you have ifyou're in a position of inciting
that professional development itcan be very cumbersome to go and
(26:09):
find those articles that can bevery cumbersome to read through
those articles and what you'representing here is going to
really make that a lot easiermuch more accessible and that's
super exciting
SPEAKER_03 (26:19):
you mentioned read through the articles when I was
doing BCBA study groups becausewe would do those a lot for
people in their courseworkstudying to get the BCBA you put
a journal article in front ofthem and it might have been
might as well have been MandarinChinese like people did not want
to read it didn't understandwhat it said and I mean to be
fair like the way that they'reset up isn't you know it's very
lab heavy it's not great to readit's not like a Exciting.
(26:42):
Well, exciting isn't the bestword.
It's not an easy read a lot
SPEAKER_02 (26:44):
of times.
No, no, no, it's not.
It's not easy to translate or toapply.
So you end up coming away withknowledge that you may not fully
understand, that you have toprocess, that you have to
discuss with other people, thatyou might need a language model
to do some translation for you,to do some summary for you.
That sounds fantastic.
Tell us a little bit about someof the other features that
you're looking at.
So I would imagine that's onefeature that's part of your
(27:06):
system.
What else are you developing?
SPEAKER_00 (27:09):
Yeah, absolutely.
Thanks for asking about it.
Yeah, that's really a greatsystem.
So some of the other products,our goal, our clinical goal
right now is to go through theentire life cycle of the client.
So from intake to discharge.
So we're going to be releasingour assessment product coming up
(27:34):
soon.
And I'll use that as an example.
We're super excited about that.
So our assessment product allowspeople to upload all of that.
You know, like when you go to doan assessment, you get a ton of
information, let's say, fromyour office.
Let's say you're a VCBA and youget a ton of information from
your office.
You get a diagnostic report.
You get somebody else's behaviorplan or re-auth or assessment or
(27:58):
something like that.
And you're expected to readthrough 40 pages of
SPEAKER_01 (28:01):
documents.
Yep.
SPEAKER_00 (28:02):
before you go to see the client and good luck with
that.
Nobody's gonna be able to readthrough all of that.
So the very first step in ourassessment cycle is somebody at
the organization uploads thatdocumentation into our system.
Our system processes all of thatinformation provides a summary,
(28:23):
and then suggested questions forthe BCBA to ask during the
interview portion of theassessment, and emails it to
them and says, here, for thisparticular kiddo, here's a
summary.
This is everything that you'regoing to need to know, and
here's some questions that werecommend asking.
So when the BCBAs come in,they're armed with that
(28:45):
information.
Now, once they get in, the nextstep, and we're...
Part of our goal, we have atwo-pronged goal at Intraverbal
AI.
We want to solve a practicalproblem and do good science.
Those are our two main values orpillars or aims at our
(29:07):
organization.
So we want to solve a practicalproblem.
And another practical problemthat you have when you're doing
an assessment is you have a penand you have your paper and
you're furiously taking notes,trying to listen, trying to ask
questions, and that's a hugechallenge.
So what we set up ispractitioners can do this,
(29:28):
whether they're doing telehealthor they're in-person, they can
grab their mobile device or theycan do it on their desktop.
Our system transcribes theentire interaction between the
practitioners and the familiesand it takes all that
information in, and it allowsthem to press a button, and our
(29:52):
system reviews the interview upto that point, and then checks
the journal articles, theliterature, and says, based on
these two buckets ofinformation, here are some
questions that you should askbefore you leave.
So that happens to all of us.
(30:13):
When you do an assessment maybe,You finish the assessment, you
get in the car, you drive downthe street and then you're like,
crap, I forgot to ask abouttoilet training or I forgot to
ask what medication the kiddosare on or something like that.
Our system handles that for youand provides questions and says,
hey, you forgot to ask aboutmedications or hey, you forgot
(30:36):
to ask about this.
Once that's done, that's theindirect portion of the
assessment.
the assessment.
So I was always taught that agood FBA involves three distinct
sections, an indirectassessment, a direct assessment,
and then a functional analysisif needed.
(30:56):
So that takes care of theindirect assessment.
Then our system does the directassessment piece, where it
records all of the data andobservations that you take
during the direct observationportion, allows you to upload
data sheets into it.
And then when you get to thelast piece, which is the report,
(31:19):
you go in, you sit down and youpress a button and our system
takes all of that information,the initial intake, the indirect
assessment transcription, thedirect assessment piece, puts it
all together into a insuranceready report.
And the way that we set this upis we hired BCBA peer reviewers
(31:43):
that work at the insurancecompanies to evaluate the
reports that we were creating.
And we created a scorecard forthem and they told us approve,
not approve, approve, notapprove, and here's why.
And then we made the changesover the course of a few
different iterations so thatwhen our system generates the
(32:05):
report, all that really needs tobe done at that point is the
behavior analyst needs to puttheir signature on it.
And I don't mean just theiractual signature, but make the
report their own.
And that's where this is reallyaugmented intelligence, not
artificial.
(32:26):
We joke around that it'sinsurance ready, but it's really
not.
It requires that last step forthe behavior analyst to come in
and say, well, This isn't reallyin my tone of voice.
I don't think maybe we shouldhave this recommended
intervention.
I really think that the functionof this behavior, the
(32:47):
hypothesized function of thisbehavior is really this, and
they come in and do that piece.
But our tool solves a hugeproblem.
It literally saves eight to 10hours of report writing, and it
does good behavior analysis.
Sorry if I dragged on there, butI really wanted to capture the
essence of what we're trying todo.
SPEAKER_03 (33:10):
So does your report, does that report also come up
with hypothesized goals as well?
SPEAKER_00 (33:18):
Oh, yeah, absolutely.
So what we've really created inIntraverbal AI is an ecosystem.
And something else that I thinkis very important to share is
all of these products, I donot...
I don't get 100% of the profitof these.
And the reason that I'm bringingthat up is because I've set up
(33:42):
partnerships with a whole bunchof behavior analysts.
And what Introverbal AI is, it'sreally an AI ecosystem for
behavior analysis.
And what we've done is we'vepartnered with a whole bunch of
subject matter experts to putout different products and
tools.
(34:02):
We've got, and I know it's along answer to your simple
question there about the goals,but we've partnered with a
subject matter expert forassessments, somebody for
supervision, somebody for BACBtest prep, which is going to be
coming out at the end of thesummer.
And we've partnered withsomebody for goals to generate
goals within our system.
(34:23):
So we've got a whole tool thatjust helps to generate goals.
And that connects with ourassessment system so that when
the assessment report isgenerated, it generates
suggested goals based on theassessment tools that you used
during the assessment cycle.
So whether you used a Vinelandor a VB Map or an ABLES or
(34:46):
something like that, it takesall of that into consideration.
and the subject matter expertsthat we've worked with to create
that report.
And you can see it, we veryproudly put that on our About Us
section of our website, thedifferent people that we
partnered with, and we've got awhole bunch more partners that
we're gonna be announcing.
(35:07):
And I do wanna say this becauseI feel like it's very important
to the mission and the values ofour company is, What we wanted
to create is a collectiveconsciousness of information and
behavior analysis.
And we wanted to not just getall of the information from so
many smart people that wrotethose journal articles and did
(35:31):
that research, but we alsowanted to get the practitioners
and the peer reviewers and setup all of these partnerships.
So I am not the sole owner ofIntraverbal AI.
We are a community of behavioranalysts that all have an
ownership stake in the productsthat we're putting out here so
(35:52):
that we can create this digitalcommunity of experts and
expertise.
Does that make
SPEAKER_02 (36:00):
sense?
A lot of sense.
That's exciting.
That's very exciting.
And thank you for taking thetime to explain all the elements
there.
I think it's very important.
Starting with the idea, so Ithink that for me, a phrase that
gets kicked around, especiallyin the old days when we did
paper and pencil, it was veryeasy to create templates.
And then before, we even talkabout it at our company now,
(36:21):
it's one of those words, I heartemplate and I'm like, oh,
you're trying to save yourselfAnd you're running the risk of
being lazy and just replicatingtreatment plans across clients
because it's similar enough.
And obviously, there's a lot ofproblems to be had there.
So when you first hear aboutsort of artificial intelligence,
(36:42):
if you will, generating thesethings immediately, especially
somebody of my age might belike, well, wait a minute, we're
going to run into that problem.
But the way you describe itclearly avoids that in terms of
generating a wealth of ideas.
And then I love the way you putthe onus.
on the professional.
Make that your own.
You're going to get a big lumpof clay.
You better sculpt that down tomake sure it reflects your
client or your purpose becauseotherwise then you do run that
(37:05):
risk of going back to just atemplate, a very generalized
cookie cutter template.
The way you've described itthough really presents a lot of
safeguards and you're promotingpeople make this information
their own.
So yeah, let the computer pumpout a bunch of possibilities and
then you apply it to yourparticular client.
SPEAKER_00 (37:25):
Yeah, absolutely.
It's interesting because when Igot into the field, I think in
2004, 2005, you know what weused to call treatment plans?
They were called IVPs,individualized behavior
SPEAKER_01 (37:36):
plans.
And
SPEAKER_00 (37:38):
somehow along the way, the I got removed to rather
just behavior plans or treatmentplans.
And I don't know what happenedto the I, but they should be
individualized treatment plans.
And the great part about thisnew technology is so much of it
is based on and there's so manyfactors in the technology that
so much of it is based on yourdata set.
(37:59):
What do you have in your dataset?
And where is your data comingfrom?
And how many different,exemplars and non-exemplars do
you have that it can referenceto get ideas from?
And that's part of what makes itquote unquote smart.
So I think that that's reallyimportant.
(38:20):
And yes, absolutely.
We've all seen, we've all workedat companies where we've seen
that, where people just have thecookie cutter behavior plans.
Yeah, the gold banks.
Yeah, the gold banks.
And look, I don't want to hateon the gold banks.
I think the gold banks, can behelpful and something I say all
the time because I use AI 25, 30times a day.
(38:43):
And I almost never take what AIgives me and use it.
But, and I said this in anothertalk that I did, that I use,
let's say, ChatGPT or Clod orGrok to come up with names for
projects that I'm working on.
I have never once actually usedthe name It's just given me a
(39:06):
sounding board and said, hey,you know what?
Here's a whole bunch of ideas.
And then I read it.
I'm like, that's a great waythat I can think of a name.
And I didn't think that I couldcome up with a name for this
project in this format.
And then I take that and I buildon it and I come up with
something of my own.
And I think...
(39:26):
That's really what our systemsare meant to do.
Does it solve problems?
Does it save the BCBA eighthours of writing a treatment
plan and fighting with, I don'tknow how long you all have been
in the field, but when I firstgot into the field and Microsoft
Office, if you try to grab agraph from Excel, copy and paste
it into Word, it would literallyexplode on the Word template.
(39:51):
Like it would go from a graphthis big to a graph this
SPEAKER_03 (39:54):
big.
SPEAKER_00 (39:54):
Yep, been there.
Yeah.
And do we solve those problems?
Absolutely.
Do we save you frustration?
Absolutely.
But you as a BCBA are anintricate part of the service
and you have to be because atIntrovert AI, we do not do
artificial intelligence.
(40:14):
We do augmented intelligence.
We partner with the scientistsand the practitioners in our
field to improve AI the scienceand practice of behavior
analysis.
Sorry, I don't mean to soundlike a commercial here.
I'm passionate about what we'redoing, so it's exciting.
SPEAKER_02 (40:30):
It's coming across, and we appreciate your passion.
We are very excited about whatyou're talking about.
SPEAKER_03 (40:36):
That is super exciting.
Are you getting an echo on yourend, Adam?
SPEAKER_00 (40:42):
No, I don't hear an echo on my end.
SPEAKER_03 (40:43):
Do you get an echo?
Yeah, I'm hearing it
SPEAKER_00 (40:45):
a little bit.
It's okay.
SPEAKER_03 (40:50):
Okay, the echo's gone.
So what I'll do when I ask you aquestion, Adam, I'll just mute
you for one second and then I'llbecause we just get an echo on
our end and then I'll open itright up to you.
But man, that is so, so excitingabout like bringing the whole
ecosystem together and alsobringing like the legitimacy of
the research back in, becausethat's something that I feel
(41:11):
like is as long as people aregetting reimbursed and the
actual the.
behavior service delivery, we'vegone so far away from the
researcher.
We've lost our research base.
That is so
SPEAKER_02 (41:20):
exciting.
I love the way it all comestogether.
One of my favorite things to doin terms of analysis and
creating a treatment plan, forexample, is brainstorming.
And it's also one of the moretaxing things that I do because
it takes a lot of time and ittakes a lot of energy.
Energy.
It's back.
(41:41):
But what you're talking about isgoing to save us that.
It's going to create, it's goingto generate the brainstorm in a
sense.
And then again, I can't stressenough how important it is that
you are saying, take it, take itand make it your
SPEAKER_00 (41:52):
own.
Yeah, absolutely.
And I think that's a criticalpiece here is, is it better
SPEAKER_02 (41:59):
now?
You're good.
You're good.
Awesome.
SPEAKER_00 (42:01):
Yeah.
That's a critical piece of whatwe're doing here because I know
that there's a lot of fear thatpeople are going to lose their
jobs to AI.
And I've been saying this fromthe beginning and I'll say it
again now, you're not going tolose your job to AI.
You're going to lose your job tosomebody that's using AI.
(42:22):
And that's kind of the toughlove that I like to give is you
have to start using these toolsand it's expected.
Like at our organization, it isexpected for you to use AI.
When we send documents to eachother, you know, you can always
tell if something's generated byAI, there's certain markers that
(42:45):
you can tell.
When I see them, I don't getupset.
I get happy in a sense because Iknow that they're making the
most of their time and they'recoming up with better quality
policies and procedures or a newway for us to add a cool feature
to our system or something likethat but we work in in aba in a
(43:11):
one of the most human fieldsimaginable and the way that i
see this is you need humans toteach other humans how to be
better humans and that's at thefoundation of what we do in
behavior analysis Some of thoserobots that companies are
working on, I don't know if youall have seen like the Boston
(43:32):
Dynamics videos or the Teslarobot videos.
They're super cool.
But if I've got a kid that'sdiagnosed with autism, I don't
want him working with a robot.
I want him working with anotherhuman being because there's so
many nuances that are involvedin being a human and being a
(43:52):
good human.
that can only come from otherhuman beings.
But I also want the humans thatwork with my family or work with
any family to be able to makethe most of their time.
So be efficient, number one.
And number two, and moreimportantly, be effective.
And that's where connecting withthe literature comes in is you
(44:16):
can, yes, Our system, forexample, makes reports easier to
write.
That's great.
But I also want you to do betterpractice.
And that's where the literaturecomes in.
SPEAKER_03 (44:26):
I love that.
We actually spoke with MichaelDowd from Alpaca Health.
Have you heard of him before?
SPEAKER_00 (44:32):
Oh, yeah.
Yeah.
Michael and I did a paneltogether on AI, I would say
maybe about five months ago at aconference in New York.
Yeah.
And they were working on he'sgot a great understanding of AI
and I think they were working ona Progress Notes AI.
Yeah.
SPEAKER_03 (44:49):
Yeah.
So that was going to be myquestion.
Yeah, I guess.
Feel free to unmute yourself atany time.
Just get rid of the echo.
But like Mike said, my bigconcern from AI was that it's
what I've seen from a lot ofpractice management software now
is that it makes things moreefficient, but It goes from
(45:12):
helping people in ABA to now ABAdevelops all of their programs
based around that.
to fit into whatever thepractice management software is.
So it's not necessarily evenhelping anymore.
It's now guiding and it's alljust based on efficiency.
And when I spoke with Michaeland then now yourself, Adam,
it's really enlightening tothink about the new actual
(45:32):
individualization that AI canactually provide to the service
delivery model.
And it's actually kind of almostthe inverse of a gold bank
because it's taking all of thisinformation that is given to it
in this FBA and the indirect anddirect assessment And
individualizing it for theclient.
So it's almost kind of theopposite of a gold bank, which
is really exciting.
I do have one question for you.
(45:54):
So feel free to anything youwant to add on to that.
One thing that Mike has alwaystalked about is with data.
And you mentioned it, too, withthe paper and pencil and
something that for I've been inthe field almost 20 years,
Mike's been in longer.
that we've always tried tograpple with, being able to take
data while engaging theindividual that you're working
with.
It's kind of like texting anddriving.
(46:15):
You can't really do both at thesame time.
So while we take data at ourcompany, we really try to focus
on the interaction versusrunning 100,000 trials to get
data points.
Is there a way in your programor something you're trying to
develop, something you'vebrought up, a way of kind of
recording a session or somethinglike that and basically being
(46:36):
able to take and quantify andtrack programs without actually
taking data based on keywordsthat are heard or answers or
things like that?
SPEAKER_00 (46:49):
Yes, absolutely.
So that's a big part of whatwe're working on now.
And that's a component of ourdirect assessment piece right
now.
We're going to be expanding onthat as part of our roadmap
later on this year.
But I love what you talked aboutthere with regards to
multitasking, whether you're insession or you're doing an
(47:10):
assessment or whatever it is.
If you're trying to take dataand trying to maybe interact
with a client, it's kind of animpossible task, to be honest
with you.
And there's so much cooltechnology that's coming out
soon that involves capturing.
(47:31):
So there's technology that we'redoing research with right now
called computer vision that cantake a video of something that's
happening in a particularsetting and identify everything
that's happening there, thenumber of times that it's
happening, how long it happensfor.
(47:52):
Yeah, that's something you'reinterested in, Mike?
SPEAKER_02 (47:54):
Very much.
Very much.
SPEAKER_00 (47:56):
Yeah, yeah, and that's just really going to
change the nature of ABA datacollection.
So the way that the direction Ithink this is going is we're
going to change from, we'regoing to go from data collectors
to IOA experts.
So we're going to go in and say,AI system, collect data, and
(48:19):
then it's going to collect thedata and it's going to report
the data back to us.
And then we're going to sit downand say, is this what we, is
this the spirit and actuality ofwhat we want to capture and
reference to the data.
You know what I mean?
And so it's, look, capturingdata is important.
Knowing how to capture data isimportant.
(48:40):
And this is a whole notherdiscussion for another day.
I think graduate students stillneed to learn how to take data
because there's little nuancesthat you learn as part of that
process that you'll need to be agood IOA specialist.
But once you get through all ofthat, there's a lot of
circumstances where it doesn'tmake sense to sit there with
(49:00):
paper and pencil or even amobile device and button smash
and take frequency data onself-stimulatory behavior that
happens 100 times in 20 minutesor something like that.
When you can be intervening oryou can be looking at the
surrounding environment, whatelse is going on during that
(49:22):
time?
And that's what this technologypromises.
And that's part of what we're soexcited about.
SPEAKER_02 (49:29):
One of the things I like to say is we've set up our
implementation as though we'rethe only source of SDs in the
environment.
And what you just said, it opensthat whole game up to really
allow...
other SDs in the environment tonow be noticed.
You can provide reinforcement tothe behavior that results.
(49:54):
Yeah, very exciting what you'retalking about.
So gentlemen, as I suspected,when Adam kindly popped up on
the screen here to start ourtime together, we have breezed
through these two hours.
And we could probably spend alot more time talking, but we've
taken enough of your time andfrom you and your family.
Let's think about some wrap-uppoints.
It sounds like Dan's gotsomething.
SPEAKER_03 (50:15):
Yeah, well, just a question for you, Adam.
And by the way, the echo justhappened late, so it wasn't
something that happened early.
And I apologize for that.
Thank you for following through.
I hate to mute and unmute.
I will open it up to you beforewe wrap up.
Is there anything else thatwasn't covered that you want to
make sure that is...
discussed today?
SPEAKER_00 (50:35):
Yeah, absolutely.
I think I want to stress acouple of things that we've
already discussed, which is theAI is going to allow us as we
progress with this technology tobe better at our jobs and to do
less of the tasks in our jobsthat we dislike.
(50:57):
I think everybody in our fieldlikes interacting with other
human beings.
I think we like the interviewportion of assessments.
I think we like the supervisionmeeting portions of supervision.
I think we like the parentinteraction portion of parent
(51:17):
training.
We like one-to-one humaninteraction.
And what this technologypromises is a lot more of that.
I talked about our assessmentproduct that gives the
practitioner more time tointeract with the parents or the
teacher, whomever it is thatthey're interviewing and connect
(51:41):
with them, build a relationshipwith them on a human level.
But the same goes for, we alsohave a supervision product that
allows the BCBAs to have moretime to interact with their
supervisee and really understandwhat's going on in the
supervisee's life as opposed tositting there furiously taking
(52:04):
notes and identifying is thisdirect or indirect hours and how
many hours did you spend withthe client versus doing indirect
hours and doing all of thosecomplicated formulas to figure
out whatever the BACBrequirements are at the moment.
Our technology, and I'll justspeak for ours at Intraverbal,
(52:27):
Our technology is designed tomake that process easier and
more reinforcing.
So it's not just low responseeffort, it's more engaging, it's
more meaningful.
You get to spend more meaningfultime with your supervisee,
talking about different casescenarios and experience.
It allows you to dive deeperinto the research.
(52:51):
And overall, I think helps ourmission, which is, Improving the
Science and Practice of BehaviorAnalysis.
SPEAKER_02 (53:01):
Right on, right on.
Thank you so much.
Love that.
(53:31):
a plug, where people can findyou, and that way our listeners,
and we'll also include this inthe show description, our
listeners know where to findyour products and where to find
your information.
SPEAKER_00 (53:42):
Yeah, thank you so much for asking.
So if you all are interested,please check out introverbal.ai.
So you can Google us, you canjust go directly to our website,
like I said, which iswww.introverbal.ai.
or you can reach out directly tome.
The only social media platformthat I personally focus on is
(54:03):
LinkedIn.
I'm on LinkedIn pretty much allday.
Feel free to message me anytimeon LinkedIn, or you can use our
contact form on our website.
We do have an Instagram page forIntroverbal and LinkedIn as
well, so feel free to reach us.
through any of those channels orif you see me at a conference I
(54:26):
regularly present at conferencesespecially this year I'm all
over the place presenting feelfree to come up and talk to me
if you'd like a demo of how oursystem works or if you'd just
like to sign up or if you'd liketo just have a discussion with
us about
SPEAKER_01 (54:41):
how
SPEAKER_00 (54:42):
our system works on the back end feel free to reach
out to us we can have a wholediscussion about how we protect
PHI and how HIPPO works in oursystem.
Feel free to contact us at anyof those channels and we'd be
happy to get back to you.
SPEAKER_02 (54:58):
Awesome.
Well, thank you.
Thank you so much.
We'd like to do a few wrap-uppoints here, and you gave me a
lot, so I had to write themdown.
So as we wrap up here, beforeour tagline, what would we learn
from Adam?
Solve the practical problem, dogood science, make the report
your own, augment yourintelligence, don't make it
artificial, and...
SPEAKER_03 (55:17):
Always analyze responsibly.
SPEAKER_02 (55:19):
Cheers.
Thanks a lot, Mr.
Ventura.
Thank you so much.
SPEAKER_03 (55:23):
ABA on
SPEAKER_02 (55:24):
Tap is recorded live and unfiltered.
We're done for today.
You don't have to go home, butyou can't stay here.
See you next time.
Welcome to ABA on Tap, where our goal is to find
(00:14):
the best recipe to brew thesmoothest, coldest, and best
tasting ABA around.
I'm Dan Lowry with Mike Rubio,and join us on our journey as we
look back into the ingredientsto form the best concoction of
ABA on tap.
In this podcast, we will talkabout the history of the ABA
(00:35):
brew, how much to consume toachieve the optimum buzz while
not getting too drunk, and therecommended pairings to bring to
the table.
So without further ado, sitback, relax, and always analyze
responsibly.
All
SPEAKER_02 (00:52):
right, all right.
This is your ever-gratefulco-host, Mike Rubio.
Thank you for coming back.
This is part two with Mr.
Adam Ventura of Intraverbal AI.
Enjoy.
SPEAKER_00 (01:04):
Yeah, that's tough.
And I feel like you had twoquestions there.
So I'll start with the firstone.
So I'm assuming when you sayKPM, key performance metrics?
SPEAKER_03 (01:16):
Correct, yeah.
And like what to evaluate peopleon and say the program's
successful.
Because my experience withworking with private equity was
always just about likeefficiency base.
SPEAKER_00 (01:26):
Yeah.
And so that just becomes ascorecard, right?
And I think it should be abalanced scorecard.
And this was one of the keylessons that I learned from work
doing OBM consulting for a fewyears is the importance of
employee and organizationalscorecards being balanced.
You know, so yes, you can't, doyou need profit measures on your
(01:52):
scorecard?
Yeah, you do.
If you're not making a profit,you can't be in business.
And people need to be able tosay that out loud without
getting pilloried foridentifying the obvious.
However, when you create thesescorecards, that can't be the
only metric on there.
So yes, net revenue isimportant, but you also need to
(02:13):
have...
measures of social validity onthere are the families happy
more or less maybe that's thewrong word but are the families
satisfied with the service thatyou are producing then you get
into and I think Mike you justbrought it up like how do you
define quality service and Ithink there's some folks in our
(02:36):
field that don't behave likehonest brokers when we have
those discussions are there somefair points that yeah it's
really hard to measure becausefor example you can't or you're
going to have trouble using apercentage measure because some
behaviors are uh much moredeleterious to the health and
(03:01):
wellness of the client thanothers so you can say oh i
reduced um some i i reducedstereotypy by 80 okay that's
great but if you have another umtopography like elopement where
the client is running out intothe middle of the street, I
reduce that by 80%.
That's good.
(03:22):
But if he's still running outinto the middle of a busy
intersection, 20% of the time,that's still a life threatening
behavior.
So coming up with a, and I'mworking in technology now, so
I'll call it an algorithm.
Coming up with an algorithm forthese insurance companies to use
is a big challenge.
(03:42):
However, I don't think it's aninsurmountable challenge.
I think it's something that ifyou get some of the smartest
behavior analysts in our fieldtogether, I think they could
figure out.
And are there gonna be some edgecases?
Yeah, there is.
But I think if we don't wanna doharm to our science, and that's
(04:04):
really kind of my professionalmission is disseminating and
moving forward the science, Ithink we need to be very careful
with this because if people aredoing bad ABA therapy out there,
it's going to reflect poorly onour science and then we're gonna
start regressing scientificallyand as a field more broadly.
SPEAKER_03 (04:28):
That makes a lot of sense.
My last question about yourbusiness and then maybe we can
transition or I can pass it toyou, Mike.
I'm sorry, I've been asking alot of questions here.
So you created this business.
had these conversations,expanded to 300 employees, and
then you did sell, you said, toprivate equity.
So can you talk about maybe yourthought process with that, your
(04:49):
thoughts on them being able tomaintain your vision and
mission, or even if that was aconcern, and maybe if they were
able to be successful with thator not?
SPEAKER_00 (04:57):
Yeah.
I didn't really speak publiclyuntil recently on the Beyond the
Science podcast about why I soldmy company, but I had a...
Family tragedy at the time.
I had no interest in selling mycompany.
I wanted to continue to grow it.
It was my life's purpose.
And I had a family tragedy.
(05:19):
Somebody in my family passedaway unexpectedly.
No, no, no.
It was seven years ago, eightyears ago, whatever it was.
And I realized that I didn'thave...
I didn't have a greatrelationship with my family at
all.
There were some people in myimmediate family that I hadn't
spoken to in a decade.
(05:40):
And I realigned my priorities.
I really felt like my legacy wasmy business.
And when that happened, I madethe decision that I needed to
have a relationship with myfamily.
I mean, I hadn't...
I hadn't spoken to my dad in 12years.
I hadn't spoken to my sisters inlike nine years.
(06:02):
I hadn't spoken to my mom in, Idon't know, five, six, seven
years, something like that.
And while the tragedy wastragic, it opened up an
opportunity for me to besuccessful personally.
And all I could think about, allthat mattered to me was
professional success.
(06:23):
I had no relationship successeither.
I was a mess professionally.
personally dating.
I was a hot mess dating.
I had no relationship with myfamily.
And I just made a transition atthat point when that tragedy
struck to grow up personally.
(06:43):
And I really made thattransition and discovered that
my immortality project, mylegacy wasn't my business.
It was my family.
It was having kids.
And it was getting married.
It was having a stablerelationship.
And when I made that transition,I realized that I couldn't do
(07:06):
that myself.
I needed help.
And I tried to transitionsomebody into the CEO role so
that I could address what hadjust happened.
Because when you have a familytragedy, a lot of people need
help.
And I needed to be there morefor my family.
Whenever something terriblehappens and somebody has to step
(07:27):
down from leadership role in anyindustry, they're like, oh, I
have to spend more time with myfamily.
I literally had to spend moretime with my family.
And I put somebody else in therole.
And as soon as I did that, wegot a Medicaid audit in Florida.
And it was a mess.
And as it turned out, Medicaiddecided, Florida Medicaid at
(07:49):
that time decided not to pay usfor about five months.
And they owed us seven figuresof money.
And I didn't have that money inthe couch cushions in my home.
So luckily, we managed to sendeverything over that they asked
(08:10):
for, got through it.
Funny enough, I always liketelling this story.
After we sold the company,Private Equity went to Florida
Medicaid and said, listen, we'rebuying Adam's company.
And they told them at the time,they're like, oh, yeah, we
audited his company and he's oneof our best providers.
And I wanted to strangle thembecause it would have been nice
(08:31):
if they would have realized thatearlier.
But that's OK.
So I the company that bought us,we were their platform company
and the first company that theybought and everything that they
promised that they would do,they did.
And.
They grew to be a very bigcompany and then ended up
(08:55):
selling again after that.
I understand how the businessworks, but I hold no ill will
towards the notion of privateequity.
For as many bad private equitystories that we hear, I know
personally of 20, 25 storiesthat a small ABA provider that
(09:18):
doesn't know how to run abusiness that was this close to
going under, private equityswooped in and saved them from
their business going under.
So I don't have that animositytowards private equity.
Are there some bad behavingactors in private equity?
(09:38):
Yeah.
Are there some private equityplatforms that shouldn't own an
ABA business?
Sure.
But is there somethinginherently wrong with that
business model?
No, I don't think so.
Dan, to your point earlier, theyneed to refine their scorecards,
(09:59):
I think some of them, to includeclinical quality, to include
some of those measures of socialvalidity.
And that's where if they getbehavior analysts to work with
them, like I know some ofprivate equity companies do
right now, then things getbetter.
And I guess that's my overallthoughts on private equity and
(10:20):
their involvement in ADA.
UNKNOWN (10:21):
Yeah.
SPEAKER_03 (10:22):
Well, thank you for sharing that story.
That was very insightful, and Iappreciate that.
I was going to
SPEAKER_02 (10:27):
say the same thing.
Thanks for sharing the personalside and then getting into the
private equity piece, and we'revery glad for your experience
there, and very glad to hearsomebody share a positive
experience on private equity,because those aren't the stories
you hear, right?
The stories you hear are all thenegatives.
What is it you think really goeswrong in those moments?
Is it Is it a bad private equityactor usually?
(10:48):
Is it people just not knowinghow to deal with private equity
when they're going through theseinteractions?
What would you tell somebody outthere who is maybe getting an
offer from private equity ormight need help to stay alive?
What would you caution themabout?
What would be your best advice?
SPEAKER_00 (11:07):
There's a lot of...
So I'll answer the last questionfirst.
There's a lot of consultants outthere that will help walk
somebody through a sale of theirbusiness.
Don't feel the other thing Iwould say to those folks is
don't feel bad about wanting tosell your business.
And, you know, I thought I wasgoing to get a lot of pushback
from people because I sold mybusiness in 2018.
(11:30):
And that was really in thebeginning of private equity and
how that stuff happened.
And I actually made the decisionto sell my business in late
2017.
My family tragedy happened inOctober of 2017.
And I made the decision reallywithin a month and a half, two
months, something like that,that I was going to have to step
(11:52):
down.
And I really expected a lot ofpushback.
I really did.
I thought people were going tovilify me and I was going to be
pilloried from a publicstandpoint in our field.
And there was a lot of people, alot of colleagues that I had
that worked in different partsof our field that when I told
(12:12):
them, their response was,congratulations, good for you.
And I couldn't believe I heardit, you know, and I felt like it
was kind of reassuring.
They're like, look, you know, Iguess because this was people,
these were people that knew me.
I lived and breathed my company.
(12:32):
It was the only thing thatmattered to me.
And That turned out to be partof the problem with my personal
life is that's all that matteredto me.
But they knew that for the fewpeople that knew what was going
on in my life, they understoodwhy I was doing it.
So I would say the first thingthat I would say to those folks
(12:52):
is don't feel bad about doingit.
Second thing I would say is finda consultant that can explain
everything to you, howeverything works.
I think that that's reallyhelpful.
I would say part of the reasonwhy this goes wrong so many of
these times is because a lot ofthese private equity companies
bring a medical model into ABA.
(13:16):
I don't agree with the idea thatwe should be making ABA the
medical model that insurancecompanies are used to dealing
with.
I think there's a lot ofproblems with that.
And I think private equitycompanies just swoop in and say,
this is just like the...
the string of dentist locationsthat we purchased last year and
(13:37):
then sold for a profit.
We can run it the same way as wedo You know, a dentist's office
or an orthopedist's office orsomething like that.
And it just doesn't work thatway.
We have a very unique field witha tiered service delivery model
that doesn't exist in otherfields.
So I think the smartest privateequity companies are the ones
(14:01):
that hire behavior analysts tohelp them make better decisions.
I think if they don't, that'swhere the problems come in.
I think most behavior analysts,especially seasoned behavior
analysts that have been in thefield for a while, that care
about the clients, that careabout the science will sit down
with the private equitycompanies and say, yes, you need
(14:22):
to make a profit.
To make a profit, you need to bemore efficient.
Yes, agreed on both of those,but you also need to provide a
good service, and here's what Imean by a good service, and here
are the outcomes that you needto look for.
And if those behavior analystscan communicate that
appropriately and the privateequity companies are willing to
(14:43):
listen, then you get goodoutcomes.
If those two things don'thappen, you get bad outcomes.
SPEAKER_03 (14:48):
So let me ask you this, and please correct me if
I'm wrong, because like I said,I'm not too well-versed in
private equity, but from somethings that I've talked about
with people, they've said thatthat does make sense, but since
private equity a lot of timesisn't there for the long run...
by the time that they're goingto have to pay the piper for the
lack of clinical quality, theywill have already sold and it
(15:10):
won't be their problem anymore.
It'll be on to somebody else.
So if they can just make it moreefficient, regardless of the
clinical quality, get themetrics up and sell, that'll be
on somebody else's doorstep.
SPEAKER_00 (15:22):
I don't know how often that happens now.
At least I think that they'rebecoming, these private equity
companies have become They'vebeen doing it now for almost a
decade.
So they're starting to figurethings out, number one.
Number two, if you sell acompany, there's a whole bunch
of provisions.
And I learned about this when Iwas selling my company.
(15:44):
There's a whole bunch ofprovisions in those agreements
that say that if things fallapart three months in, you're in
a lot of trouble as a seller.
So I think...
Private equity companies arebecoming wise to this and
realizing that it's not just athree-year turnaround anymore,
that it's a three- to five-yearturnaround, maybe five- to
(16:06):
seven-year turnaround, and thatit's bad business to run a bad
business.
So if you're running a businessthat all you care about is
billable hours, and then all ofa sudden audits show up, clients
get hurt, social media plays abig role in this too.
If you run a crappy company...
(16:27):
Behavior analysts are going tomake it well known in those
Facebook groups and on LinkedInand things like that.
And you become, your brandbecomes known for that.
And anybody that's looking toacquire you, a bigger private
equity company or whomever itis, they're going to do their
due diligence.
They're going to not just lookat your books.
(16:48):
They're going to look at yourbrand.
They're going to look at howpeople have rated you on
Glassdoor.
So I don't think this is 2018,2019, 2020 anymore.
This is 2025.
And those people that run thosebillion dollar private equity
funds, they didn't becomebillionaires by making stupid
(17:12):
decisions.
And I think that they look backand say, you know what?
We saw this company or thatcompany collapse.
Why did that happen?
In a lot of cases, it's becausethey didn't care about clinical
quality or treated theiremployees like crap.
SPEAKER_03 (17:28):
Thank you for clarifying.
I appreciate that.
Like I said, I'm pretty ignorantto the whole thing, so I'm just
saying what I hear, and thankyou for clarifying that.
That was very insightful.
SPEAKER_02 (17:35):
Yeah, very good information.
So you sold, and I'm going toskip over a few things here just
in the interest of time.
But now you're delving intoartificial intelligence and how
that's going to fit into thefuture of ABA, whether that's
autism treatment or whateverthat may present.
And I think it behooves us todiversify more and more in how
(17:58):
we're actually employingbehavior analysis to help
people.
Talk to us a little bit aboutwhat you're doing now, what got
you interested in that overtime, and then anything else you
want to share and we'll help youunpack it and discuss it.
Yes, that was
SPEAKER_00 (18:15):
great.
Thank you for the question.
Actually, one of the businessesthat I moved into after I sold
my ABA therapy company wasopening a practice management
software company.
I made a lot of mistakes withthat company.
I learned what I like doing andwhat I don't like doing.
(18:36):
When we were talking maybe about30 or 40 minutes ago, I talked
about how much I hate ABA admin.
One of the things you have to dowhen you start an ABA practice
management software company isyou have to deal with a lot of
admin and you have to reallybecome a medical billing expert.
It's interesting because I havea software team that I've been
(18:56):
working with for 10 years.
Something that our leaddeveloper explained to me a few
years ago and he's like, Adam,medical billing software is its
own specialty because it's socomplicated.
You have to know so much aboutthat industry and you have to
merge two fields together,software and insurance
(19:18):
companies.
It's very difficult.
Um, so I realized that, um, Ididn't want to do practice
management.
It just wasn't something that Iwanted to do, but I liked
technology a lot, a lot.
And when AI really startedcoming on the scene, uh, really
even before, uh, chat GPT,everybody knows chat GPT and it
(19:40):
kind of burst onto the scene afew years ago or a couple of
years ago.
Um, I started talking a lot tomy development team about.
where we can move forward andthe answer that they always gave
me was ai and i thought aboutwhat my professional mission is
and my profession professionalmission is to move the science
(20:02):
and practice of behavioranalysis forward that's what
matters to me we were talkingabout the science earlier it
really matters to me i taught atuniversity for 12 years i enjoy
teaching the science i enjoydisseminating it and when some
of those large language modelsreally burst onto the scene in
2022, we started having thosediscussions about how I can
(20:27):
merge my professional missionwith this new emerging
technology.
And where I kind of landed asour initial product offering was
I wanted to make the practice ofbehavior analysis more
evidence-based.
So what do I mean by that?
When we go into a home, aclinic, a group home, a school
(20:50):
to work with a particular kiddo,there's almost exactly zero
behavior analysts that show upwith a binder full of journal
articles.
It's just not going to happen.
And even if you did, it's notpractical.
If you've got a kiddo that'sengaging in that spaghetti
tantrum thing, you can't...
(21:12):
response block that with onehand and page three in the idea
that, because there's tens ofthousands of journal articles
out there, you know, that you'regoing to find the one and the
intervention that's there, it'sjust not going to happen.
So one of the things that wewanted to do was bring, with
(21:33):
this technology, bring thescience into the practice.
And something that we starteddoing was setting up deals with
publishing houses to connectthese AI systems to that
information and making itavailable in the moment.
(21:54):
So you can literally pick up oneof these mobile devices and say,
my client's engaging in this,what should I do?
Now, I want to make this veryclear that, especially if you're
an RBT listening to this, Youneed to do what your behavior
analyst tells you to do, notwhat an AI system tells you to
(22:14):
do.
However, if you're a BCBA andyou're looking for ideas on how
to deal with a unique form ofSIB or you want to set up toilet
training in a unique setting andyou need some ideas, you need a
sounding board to bounce ideasoff of, these types of tools are
(22:35):
the way to do it.
We set up our company calledIntroverbal AI, and we feel like
the name of the company isappropriate because you plug a
whole bunch of mans into oursystem.
You ask questions, you makerequests, and our system gives
answers back.
And that's why we came up withthe
SPEAKER_02 (22:55):
name.
I love it.
That's fantastic.
SPEAKER_00 (22:58):
Thank you.
Thank you.
And with our system, oursystem...
allows you to search throughtens of thousands of journal
articles to find the answers tonot just clinical questions, but
academic questions.
And we took this base model andwe started building on top of
(23:19):
it.
And we created a goals productthat we have that helps you
create goals for your clients.
And then we ended up adding like10,000 client goals or 10,000
clients worth of goals.
It turned into like a millionactual individual goals that it
(23:39):
kind of pulls from.
We developed a functionalbehavior assessment product that
touches on not only those goals,but also the literature as well.
So not to get too deep into theproducts, our system was really
meant to improve the science andpractice of behavior analysis
and we tried to do that with umstarting off with the foundation
(24:04):
of the literature you know whatwere we all taught what was
drilled into our brains ingraduate school do
evidence-based practice alwaysreference the literature you
know if you were in a goodgraduate program you were
reading eight to ten journalarticles a week you know and you
really had to dive in do deepdives into these articles to
(24:27):
make sure you understood whythings were done the way that
they were done and recognizinggood research from bad research.
And that was a big impetus as towhat we wanted to do and I'll
never forget the first time ourlead developer came to me and he
said, okay, we connected the twohere and then I just started
asking questions and it gave meanswers instantaneously and it
(24:50):
gave the in-text citation andthen it gave a full reference
list And it did it in like fourseconds.
And I was like, holy crap, youknow, like just imagine the
difference that we can make withall of this.
And I think that was myintroduction to AI and why I
kind of fell in love with whatwe can do with this technology
(25:14):
and how it can augment, notreplace our practice.
SPEAKER_03 (25:19):
Sold.
I was going to say, I can vibewith that, but you are speaking
this guy's language 100%.
SPEAKER_02 (25:25):
So just, I'm already having visions of, you know, in
all fairness, I can be very,I've been very critical on the
show and in the pastprofessionally about, you know,
you've got these staff meetingsand you end up taking time away
from clients to sit down for twohours and talk about billable
matters and, you know,efficiency.
And you're not, training anybodyyou're not professionally
(25:48):
developing anybody truly in thebread and butter of the the
service delivery and and thatknowledge so the idea that i
mean i think a lot of that whythat happens is it takes time to
develop those training materialsso as a bcba as a manager or
whatever title you have ifyou're in a position of inciting
that professional development itcan be very cumbersome to go and
(26:09):
find those articles that can bevery cumbersome to read through
those articles and what you'representing here is going to
really make that a lot easiermuch more accessible and that's
super exciting
SPEAKER_03 (26:19):
you mentioned read through the articles when I was
doing BCBA study groups becausewe would do those a lot for
people in their courseworkstudying to get the BCBA you put
a journal article in front ofthem and it might have been
might as well have been MandarinChinese like people did not want
to read it didn't understandwhat it said and I mean to be
fair like the way that they'reset up isn't you know it's very
lab heavy it's not great to readit's not like a Exciting.
(26:42):
Well, exciting isn't the bestword.
It's not an easy read a lot
SPEAKER_02 (26:44):
of times.
No, no, no, it's not.
It's not easy to translate or toapply.
So you end up coming away withknowledge that you may not fully
understand, that you have toprocess, that you have to
discuss with other people, thatyou might need a language model
to do some translation for you,to do some summary for you.
That sounds fantastic.
Tell us a little bit about someof the other features that
you're looking at.
So I would imagine that's onefeature that's part of your
(27:06):
system.
What else are you developing?
SPEAKER_00 (27:09):
Yeah, absolutely.
Thanks for asking about it.
Yeah, that's really a greatsystem.
So some of the other products,our goal, our clinical goal
right now is to go through theentire life cycle of the client.
So from intake to discharge.
So we're going to be releasingour assessment product coming up
(27:34):
soon.
And I'll use that as an example.
We're super excited about that.
So our assessment product allowspeople to upload all of that.
You know, like when you go to doan assessment, you get a ton of
information, let's say, fromyour office.
Let's say you're a VCBA and youget a ton of information from
your office.
You get a diagnostic report.
You get somebody else's behaviorplan or re-auth or assessment or
(27:58):
something like that.
And you're expected to readthrough 40 pages of
SPEAKER_01 (28:01):
documents.
Yep.
SPEAKER_00 (28:02):
before you go to see the client and good luck with
that.
Nobody's gonna be able to readthrough all of that.
So the very first step in ourassessment cycle is somebody at
the organization uploads thatdocumentation into our system.
Our system processes all of thatinformation provides a summary,
(28:23):
and then suggested questions forthe BCBA to ask during the
interview portion of theassessment, and emails it to
them and says, here, for thisparticular kiddo, here's a
summary.
This is everything that you'regoing to need to know, and
here's some questions that werecommend asking.
So when the BCBAs come in,they're armed with that
(28:45):
information.
Now, once they get in, the nextstep, and we're...
Part of our goal, we have atwo-pronged goal at Intraverbal
AI.
We want to solve a practicalproblem and do good science.
Those are our two main values orpillars or aims at our
(29:07):
organization.
So we want to solve a practicalproblem.
And another practical problemthat you have when you're doing
an assessment is you have a penand you have your paper and
you're furiously taking notes,trying to listen, trying to ask
questions, and that's a hugechallenge.
So what we set up ispractitioners can do this,
(29:28):
whether they're doing telehealthor they're in-person, they can
grab their mobile device or theycan do it on their desktop.
Our system transcribes theentire interaction between the
practitioners and the familiesand it takes all that
information in, and it allowsthem to press a button, and our
(29:52):
system reviews the interview upto that point, and then checks
the journal articles, theliterature, and says, based on
these two buckets ofinformation, here are some
questions that you should askbefore you leave.
So that happens to all of us.
(30:13):
When you do an assessment maybe,You finish the assessment, you
get in the car, you drive downthe street and then you're like,
crap, I forgot to ask abouttoilet training or I forgot to
ask what medication the kiddosare on or something like that.
Our system handles that for youand provides questions and says,
hey, you forgot to ask aboutmedications or hey, you forgot
(30:36):
to ask about this.
Once that's done, that's theindirect portion of the
assessment.
the assessment.
So I was always taught that agood FBA involves three distinct
sections, an indirectassessment, a direct assessment,
and then a functional analysisif needed.
(30:56):
So that takes care of theindirect assessment.
Then our system does the directassessment piece, where it
records all of the data andobservations that you take
during the direct observationportion, allows you to upload
data sheets into it.
And then when you get to thelast piece, which is the report,
(31:19):
you go in, you sit down and youpress a button and our system
takes all of that information,the initial intake, the indirect
assessment transcription, thedirect assessment piece, puts it
all together into a insuranceready report.
And the way that we set this upis we hired BCBA peer reviewers
(31:43):
that work at the insurancecompanies to evaluate the
reports that we were creating.
And we created a scorecard forthem and they told us approve,
not approve, approve, notapprove, and here's why.
And then we made the changesover the course of a few
different iterations so thatwhen our system generates the
(32:05):
report, all that really needs tobe done at that point is the
behavior analyst needs to puttheir signature on it.
And I don't mean just theiractual signature, but make the
report their own.
And that's where this is reallyaugmented intelligence, not
artificial.
(32:26):
We joke around that it'sinsurance ready, but it's really
not.
It requires that last step forthe behavior analyst to come in
and say, well, This isn't reallyin my tone of voice.
I don't think maybe we shouldhave this recommended
intervention.
I really think that the functionof this behavior, the
(32:47):
hypothesized function of thisbehavior is really this, and
they come in and do that piece.
But our tool solves a hugeproblem.
It literally saves eight to 10hours of report writing, and it
does good behavior analysis.
Sorry if I dragged on there, butI really wanted to capture the
essence of what we're trying todo.
SPEAKER_03 (33:10):
So does your report, does that report also come up
with hypothesized goals as well?
SPEAKER_00 (33:18):
Oh, yeah, absolutely.
So what we've really created inIntraverbal AI is an ecosystem.
And something else that I thinkis very important to share is
all of these products, I donot...
I don't get 100% of the profitof these.
And the reason that I'm bringingthat up is because I've set up
(33:42):
partnerships with a whole bunchof behavior analysts.
And what Introverbal AI is, it'sreally an AI ecosystem for
behavior analysis.
And what we've done is we'vepartnered with a whole bunch of
subject matter experts to putout different products and
tools.
(34:02):
We've got, and I know it's along answer to your simple
question there about the goals,but we've partnered with a
subject matter expert forassessments, somebody for
supervision, somebody for BACBtest prep, which is going to be
coming out at the end of thesummer.
And we've partnered withsomebody for goals to generate
goals within our system.
(34:23):
So we've got a whole tool thatjust helps to generate goals.
And that connects with ourassessment system so that when
the assessment report isgenerated, it generates
suggested goals based on theassessment tools that you used
during the assessment cycle.
So whether you used a Vinelandor a VB Map or an ABLES or
(34:46):
something like that, it takesall of that into consideration.
and the subject matter expertsthat we've worked with to create
that report.
And you can see it, we veryproudly put that on our About Us
section of our website, thedifferent people that we
partnered with, and we've got awhole bunch more partners that
we're gonna be announcing.
(35:07):
And I do wanna say this becauseI feel like it's very important
to the mission and the values ofour company is, What we wanted
to create is a collectiveconsciousness of information and
behavior analysis.
And we wanted to not just getall of the information from so
many smart people that wrotethose journal articles and did
(35:31):
that research, but we alsowanted to get the practitioners
and the peer reviewers and setup all of these partnerships.
So I am not the sole owner ofIntraverbal AI.
We are a community of behavioranalysts that all have an
ownership stake in the productsthat we're putting out here so
(35:52):
that we can create this digitalcommunity of experts and
expertise.
Does that make
SPEAKER_02 (36:00):
sense?
A lot of sense.
That's exciting.
That's very exciting.
And thank you for taking thetime to explain all the elements
there.
I think it's very important.
Starting with the idea, so Ithink that for me, a phrase that
gets kicked around, especiallyin the old days when we did
paper and pencil, it was veryeasy to create templates.
And then before, we even talkabout it at our company now,
(36:21):
it's one of those words, I heartemplate and I'm like, oh,
you're trying to save yourselfAnd you're running the risk of
being lazy and just replicatingtreatment plans across clients
because it's similar enough.
And obviously, there's a lot ofproblems to be had there.
So when you first hear aboutsort of artificial intelligence,
(36:42):
if you will, generating thesethings immediately, especially
somebody of my age might belike, well, wait a minute, we're
going to run into that problem.
But the way you describe itclearly avoids that in terms of
generating a wealth of ideas.
And then I love the way you putthe onus.
on the professional.
Make that your own.
You're going to get a big lumpof clay.
You better sculpt that down tomake sure it reflects your
client or your purpose becauseotherwise then you do run that
(37:05):
risk of going back to just atemplate, a very generalized
cookie cutter template.
The way you've described itthough really presents a lot of
safeguards and you're promotingpeople make this information
their own.
So yeah, let the computer pumpout a bunch of possibilities and
then you apply it to yourparticular client.
SPEAKER_00 (37:25):
Yeah, absolutely.
It's interesting because when Igot into the field, I think in
2004, 2005, you know what weused to call treatment plans?
They were called IVPs,individualized behavior
SPEAKER_01 (37:36):
plans.
And
SPEAKER_00 (37:38):
somehow along the way, the I got removed to rather
just behavior plans or treatmentplans.
And I don't know what happenedto the I, but they should be
individualized treatment plans.
And the great part about thisnew technology is so much of it
is based on and there's so manyfactors in the technology that
so much of it is based on yourdata set.
(37:59):
What do you have in your dataset?
And where is your data comingfrom?
And how many different,exemplars and non-exemplars do
you have that it can referenceto get ideas from?
And that's part of what makes itquote unquote smart.
So I think that that's reallyimportant.
(38:20):
And yes, absolutely.
We've all seen, we've all workedat companies where we've seen
that, where people just have thecookie cutter behavior plans.
Yeah, the gold banks.
Yeah, the gold banks.
And look, I don't want to hateon the gold banks.
I think the gold banks, can behelpful and something I say all
the time because I use AI 25, 30times a day.
(38:43):
And I almost never take what AIgives me and use it.
But, and I said this in anothertalk that I did, that I use,
let's say, ChatGPT or Clod orGrok to come up with names for
projects that I'm working on.
I have never once actually usedthe name It's just given me a
(39:06):
sounding board and said, hey,you know what?
Here's a whole bunch of ideas.
And then I read it.
I'm like, that's a great waythat I can think of a name.
And I didn't think that I couldcome up with a name for this
project in this format.
And then I take that and I buildon it and I come up with
something of my own.
And I think...
(39:26):
That's really what our systemsare meant to do.
Does it solve problems?
Does it save the BCBA eighthours of writing a treatment
plan and fighting with, I don'tknow how long you all have been
in the field, but when I firstgot into the field and Microsoft
Office, if you try to grab agraph from Excel, copy and paste
it into Word, it would literallyexplode on the Word template.
(39:51):
Like it would go from a graphthis big to a graph this
SPEAKER_03 (39:54):
big.
SPEAKER_00 (39:54):
Yep, been there.
Yeah.
And do we solve those problems?
Absolutely.
Do we save you frustration?
Absolutely.
But you as a BCBA are anintricate part of the service
and you have to be because atIntrovert AI, we do not do
artificial intelligence.
(40:14):
We do augmented intelligence.
We partner with the scientistsand the practitioners in our
field to improve AI the scienceand practice of behavior
analysis.
Sorry, I don't mean to soundlike a commercial here.
I'm passionate about what we'redoing, so it's exciting.
SPEAKER_02 (40:30):
It's coming across, and we appreciate your passion.
We are very excited about whatyou're talking about.
SPEAKER_03 (40:36):
That is super exciting.
Are you getting an echo on yourend, Adam?
SPEAKER_00 (40:42):
No, I don't hear an echo on my end.
SPEAKER_03 (40:43):
Do you get an echo?
Yeah, I'm hearing it
SPEAKER_00 (40:45):
a little bit.
It's okay.
SPEAKER_03 (40:50):
Okay, the echo's gone.
So what I'll do when I ask you aquestion, Adam, I'll just mute
you for one second and then I'llbecause we just get an echo on
our end and then I'll open itright up to you.
But man, that is so, so excitingabout like bringing the whole
ecosystem together and alsobringing like the legitimacy of
the research back in, becausethat's something that I feel
(41:11):
like is as long as people aregetting reimbursed and the
actual the.
behavior service delivery, we'vegone so far away from the
researcher.
We've lost our research base.
That is so
SPEAKER_02 (41:20):
exciting.
I love the way it all comestogether.
One of my favorite things to doin terms of analysis and
creating a treatment plan, forexample, is brainstorming.
And it's also one of the moretaxing things that I do because
it takes a lot of time and ittakes a lot of energy.
Energy.
It's back.
(41:41):
But what you're talking about isgoing to save us that.
It's going to create, it's goingto generate the brainstorm in a
sense.
And then again, I can't stressenough how important it is that
you are saying, take it, take itand make it your
SPEAKER_00 (41:52):
own.
Yeah, absolutely.
And I think that's a criticalpiece here is, is it better
SPEAKER_02 (41:59):
now?
You're good.
You're good.
Awesome.
SPEAKER_00 (42:01):
Yeah.
That's a critical piece of whatwe're doing here because I know
that there's a lot of fear thatpeople are going to lose their
jobs to AI.
And I've been saying this fromthe beginning and I'll say it
again now, you're not going tolose your job to AI.
You're going to lose your job tosomebody that's using AI.
(42:22):
And that's kind of the toughlove that I like to give is you
have to start using these toolsand it's expected.
Like at our organization, it isexpected for you to use AI.
When we send documents to eachother, you know, you can always
tell if something's generated byAI, there's certain markers that
(42:45):
you can tell.
When I see them, I don't getupset.
I get happy in a sense because Iknow that they're making the
most of their time and they'recoming up with better quality
policies and procedures or a newway for us to add a cool feature
to our system or something likethat but we work in in aba in a
(43:11):
one of the most human fieldsimaginable and the way that i
see this is you need humans toteach other humans how to be
better humans and that's at thefoundation of what we do in
behavior analysis Some of thoserobots that companies are
working on, I don't know if youall have seen like the Boston
(43:32):
Dynamics videos or the Teslarobot videos.
They're super cool.
But if I've got a kid that'sdiagnosed with autism, I don't
want him working with a robot.
I want him working with anotherhuman being because there's so
many nuances that are involvedin being a human and being a
(43:52):
good human.
that can only come from otherhuman beings.
But I also want the humans thatwork with my family or work with
any family to be able to makethe most of their time.
So be efficient, number one.
And number two, and moreimportantly, be effective.
And that's where connecting withthe literature comes in is you
(44:16):
can, yes, Our system, forexample, makes reports easier to
write.
That's great.
But I also want you to do betterpractice.
And that's where the literaturecomes in.
SPEAKER_03 (44:26):
I love that.
We actually spoke with MichaelDowd from Alpaca Health.
Have you heard of him before?
SPEAKER_00 (44:32):
Oh, yeah.
Yeah.
Michael and I did a paneltogether on AI, I would say
maybe about five months ago at aconference in New York.
Yeah.
And they were working on he'sgot a great understanding of AI
and I think they were working ona Progress Notes AI.
Yeah.
SPEAKER_03 (44:49):
Yeah.
So that was going to be myquestion.
Yeah, I guess.
Feel free to unmute yourself atany time.
Just get rid of the echo.
But like Mike said, my bigconcern from AI was that it's
what I've seen from a lot ofpractice management software now
is that it makes things moreefficient, but It goes from
(45:12):
helping people in ABA to now ABAdevelops all of their programs
based around that.
to fit into whatever thepractice management software is.
So it's not necessarily evenhelping anymore.
It's now guiding and it's alljust based on efficiency.
And when I spoke with Michaeland then now yourself, Adam,
it's really enlightening tothink about the new actual
(45:32):
individualization that AI canactually provide to the service
delivery model.
And it's actually kind of almostthe inverse of a gold bank
because it's taking all of thisinformation that is given to it
in this FBA and the indirect anddirect assessment And
individualizing it for theclient.
So it's almost kind of theopposite of a gold bank, which
is really exciting.
I do have one question for you.
(45:54):
So feel free to anything youwant to add on to that.
One thing that Mike has alwaystalked about is with data.
And you mentioned it, too, withthe paper and pencil and
something that for I've been inthe field almost 20 years,
Mike's been in longer.
that we've always tried tograpple with, being able to take
data while engaging theindividual that you're working
with.
It's kind of like texting anddriving.
(46:15):
You can't really do both at thesame time.
So while we take data at ourcompany, we really try to focus
on the interaction versusrunning 100,000 trials to get
data points.
Is there a way in your programor something you're trying to
develop, something you'vebrought up, a way of kind of
recording a session or somethinglike that and basically being
(46:36):
able to take and quantify andtrack programs without actually
taking data based on keywordsthat are heard or answers or
things like that?
SPEAKER_00 (46:49):
Yes, absolutely.
So that's a big part of whatwe're working on now.
And that's a component of ourdirect assessment piece right
now.
We're going to be expanding onthat as part of our roadmap
later on this year.
But I love what you talked aboutthere with regards to
multitasking, whether you're insession or you're doing an
(47:10):
assessment or whatever it is.
If you're trying to take dataand trying to maybe interact
with a client, it's kind of animpossible task, to be honest
with you.
And there's so much cooltechnology that's coming out
soon that involves capturing.
(47:31):
So there's technology that we'redoing research with right now
called computer vision that cantake a video of something that's
happening in a particularsetting and identify everything
that's happening there, thenumber of times that it's
happening, how long it happensfor.
(47:52):
Yeah, that's something you'reinterested in, Mike?
SPEAKER_02 (47:54):
Very much.
Very much.
SPEAKER_00 (47:56):
Yeah, yeah, and that's just really going to
change the nature of ABA datacollection.
So the way that the direction Ithink this is going is we're
going to change from, we'regoing to go from data collectors
to IOA experts.
So we're going to go in and say,AI system, collect data, and
(48:19):
then it's going to collect thedata and it's going to report
the data back to us.
And then we're going to sit downand say, is this what we, is
this the spirit and actuality ofwhat we want to capture and
reference to the data.
You know what I mean?
And so it's, look, capturingdata is important.
Knowing how to capture data isimportant.
(48:40):
And this is a whole notherdiscussion for another day.
I think graduate students stillneed to learn how to take data
because there's little nuancesthat you learn as part of that
process that you'll need to be agood IOA specialist.
But once you get through all ofthat, there's a lot of
circumstances where it doesn'tmake sense to sit there with
(49:00):
paper and pencil or even amobile device and button smash
and take frequency data onself-stimulatory behavior that
happens 100 times in 20 minutesor something like that.
When you can be intervening oryou can be looking at the
surrounding environment, whatelse is going on during that
(49:22):
time?
And that's what this technologypromises.
And that's part of what we're soexcited about.
SPEAKER_02 (49:29):
One of the things I like to say is we've set up our
implementation as though we'rethe only source of SDs in the
environment.
And what you just said, it opensthat whole game up to really
allow...
other SDs in the environment tonow be noticed.
You can provide reinforcement tothe behavior that results.
(49:54):
Yeah, very exciting what you'retalking about.
So gentlemen, as I suspected,when Adam kindly popped up on
the screen here to start ourtime together, we have breezed
through these two hours.
And we could probably spend alot more time talking, but we've
taken enough of your time andfrom you and your family.
Let's think about some wrap-uppoints.
It sounds like Dan's gotsomething.
SPEAKER_03 (50:15):
Yeah, well, just a question for you, Adam.
And by the way, the echo justhappened late, so it wasn't
something that happened early.
And I apologize for that.
Thank you for following through.
I hate to mute and unmute.
I will open it up to you beforewe wrap up.
Is there anything else thatwasn't covered that you want to
make sure that is...
discussed today?
SPEAKER_00 (50:35):
Yeah, absolutely.
I think I want to stress acouple of things that we've
already discussed, which is theAI is going to allow us as we
progress with this technology tobe better at our jobs and to do
less of the tasks in our jobsthat we dislike.
(50:57):
I think everybody in our fieldlikes interacting with other
human beings.
I think we like the interviewportion of assessments.
I think we like the supervisionmeeting portions of supervision.
I think we like the parentinteraction portion of parent
(51:17):
training.
We like one-to-one humaninteraction.
And what this technologypromises is a lot more of that.
I talked about our assessmentproduct that gives the
practitioner more time tointeract with the parents or the
teacher, whomever it is thatthey're interviewing and connect
(51:41):
with them, build a relationshipwith them on a human level.
But the same goes for, we alsohave a supervision product that
allows the BCBAs to have moretime to interact with their
supervisee and really understandwhat's going on in the
supervisee's life as opposed tositting there furiously taking
(52:04):
notes and identifying is thisdirect or indirect hours and how
many hours did you spend withthe client versus doing indirect
hours and doing all of thosecomplicated formulas to figure
out whatever the BACBrequirements are at the moment.
Our technology, and I'll justspeak for ours at Intraverbal,
(52:27):
Our technology is designed tomake that process easier and
more reinforcing.
So it's not just low responseeffort, it's more engaging, it's
more meaningful.
You get to spend more meaningfultime with your supervisee,
talking about different casescenarios and experience.
It allows you to dive deeperinto the research.
(52:51):
And overall, I think helps ourmission, which is, Improving the
Science and Practice of BehaviorAnalysis.
SPEAKER_02 (53:01):
Right on, right on.
Thank you so much.
Love that.
(53:31):
a plug, where people can findyou, and that way our listeners,
and we'll also include this inthe show description, our
listeners know where to findyour products and where to find
your information.
SPEAKER_00 (53:42):
Yeah, thank you so much for asking.
So if you all are interested,please check out introverbal.ai.
So you can Google us, you canjust go directly to our website,
like I said, which iswww.introverbal.ai.
or you can reach out directly tome.
The only social media platformthat I personally focus on is
(54:03):
LinkedIn.
I'm on LinkedIn pretty much allday.
Feel free to message me anytimeon LinkedIn, or you can use our
contact form on our website.
We do have an Instagram page forIntroverbal and LinkedIn as
well, so feel free to reach us.
through any of those channels orif you see me at a conference I
(54:26):
regularly present at conferencesespecially this year I'm all
over the place presenting feelfree to come up and talk to me
if you'd like a demo of how oursystem works or if you'd just
like to sign up or if you'd liketo just have a discussion with
us about
SPEAKER_01 (54:41):
how
SPEAKER_00 (54:42):
our system works on the back end feel free to reach
out to us we can have a wholediscussion about how we protect
PHI and how HIPPO works in oursystem.
Feel free to contact us at anyof those channels and we'd be
happy to get back to you.
SPEAKER_02 (54:58):
Awesome.
Well, thank you.
Thank you so much.
We'd like to do a few wrap-uppoints here, and you gave me a
lot, so I had to write themdown.
So as we wrap up here, beforeour tagline, what would we learn
from Adam?
Solve the practical problem, dogood science, make the report
your own, augment yourintelligence, don't make it
artificial, and...
SPEAKER_03 (55:17):
Always analyze responsibly.
SPEAKER_02 (55:19):
Cheers.
Thanks a lot, Mr.
Ventura.
Thank you so much.
SPEAKER_03 (55:23):
ABA on
SPEAKER_02 (55:24):
Tap is recorded live and unfiltered.
We're done for today.
You don't have to go home, butyou can't stay here.
See you next time.
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