Hi everyone, my name is Ethan. I'm the CEO and co-founder of TexQL and we build agents uh for really really really big data.

This presentation is called building AI that can query 100,000 tables with pedabytes of data and zero configuration because uh I I was told that being very specific generally helps to make people understand what you actually do.

And so what do we actually do?

Historically, there are kind of like two types of like AI for data that everyone's kind of heard of, right?

There's like uh there's like the classic like historical infrastructure for like really big data. This is data like that can't fit on your computer.

We're talking about roughly at around like 16 gigs is when like your computer would shut down if you tried to do really big manipulation on that.

And so you need very specialized systems uh Oracle like ERP, CRM, HR systems, data warehouses, big like you know uh BI tools.

All these things have very very high configuration costs. You have to like migrate and like when they offer an E1 AI product that only works for the data within their environments.

And then there's the other class of uh data products and AI products that everyone's probably played with today here uh which are you know cla notion glean.

These are platforms that like do really well with powerpoints and presentations and uh and spreadsheets and PDFs and word docs that fit on your computer and uh do not let you really access data that is pabytes because there's a lot of work that goes into that.

And so that's what we try to do.

We try to make it possible to deploy AI that you can ask questions like uh can you connect my CRM data to my snowflake data to like all the emails that I sent last week and tell me like which of my customers are most likely to churn.

Or can you validate that all these numbers are correct and then you know even commit like a PR to GitHub to make sure that the uh that the SQL migrations that we're doing are all like correct.

Um this is a very very hard amount of overhead.

We work with like very big companies.

Uh we work with like very uh compliant like environments. we deploy into like hospitals, financial services is a super onrem like infra heavy like environment.

And we work with some of the largest AI labs because uh that that's how hard it is to build out all this infrastructure.

More use cases we do financial services and healthcare like predominantly and and we do a lot of like transformations um around uh making sure people can build pipelines move data around really like work with big data for lack of a better word.

You might be thinking why do I care about this?

Well, right now if the unit cost of like you doing like a data request and asking like, hey, can you like, you know, figure out what the most likely customer to turn is? Uh, if that costs you a month of time of a Stanford PhD in data science, like you pay $300,000 a year to, that sets a kind of a floor on how valuable the opportunity has to be for you to analyze that.

Obviously, if you can bring down the cost of that analysis, if you can train an agent to do that for one/10enth, 100th, 1/1,000th of the cost, um, it unlocks a lot more opportunities, right?

It lets you like take analysis that you're doing today at like the like you're trying to figure out like which of our customers is going to churn per month per state and you want to do that analysis really like per city per week per uh per zip code per day.

Uh really like per per LinkedIn post from your champion at a particular account who is leaving and says I'm very sad and you read from that that they were fired and therefore all their initiatives are going to shut down and you want to like anticipate that in advance or something.

That's like a very very low latency thing that's very hard. It's very expensive.

That's kind of what we do.

So to walk you through an example uh let's say uh there's a finance guy on your team uh Dylan who is in the audience here with us today who uh we have to do some board reporting.

It's five minutes until the board meeting and he sent me this uh thing and he says uh can you look at this like thing uh spreadsheet there's an image on it. I have no idea where these numbers came from. Can you double check that these are correct?

And I don't happen to have a Stanford PhD that I can like accelerate a month of work into two 20 minutes for.

So this is actually the uh this is like slightly like this is our real like prod data. Uh this is on top of a data warehouse of I think about like 10,000ish tables.

Uh it worked the first time with no configuration.

Now it's kind of cached so it won't be as sexy.

Um so if I take this and I go to I need a prepar meeting and I go uh hey Anna can you double check if this is true. Uh Matt told me this is correct. Uh the man on my team.

Um and uh and uh I'm going to be really mad if it's not correct because the board is going to fire me.

um uh tell me if they will be happy or not and if not um what I can do about it to drive the usage to go up.

I used to run a data team uh at a startup uh with like 10 people.

I was responsible for every single one of these requests.

If I if I got a request like this, I would want to gouge my eyes out because uh there's no context.

There is a image. I have no idea what table this came from.

There's no lineage. There's no infrastructure. There's no metadata documentation whatsoever.

I have no idea what table they pulled it from. I don't know if it was like segment blogs or snowflake or something else.

But uh you'll see Anna start to reply and start telling me the work that she is doing.

Uh so if I go into the environment, what you'll see is uh you'll see the uh agent has received this.

Um I can you know ask follow-up questions and it'll like once it's done replying it'll like generate a report, send it back to Slack and uh I actually didn't think this far ahead because if I had I would have realized now we're going to stare at this run for a little bit maybe to like walk through like how we managed to do this and like the kind of things that it's happening under the hood.

um how can I like do this over hundreds of thousands of tables without knowing anything about your data warehouse?

Well, the first thing it does is uh it looks at a context repository that we maintain.

Uh that's actually um a GitHub uh context uh repo that we like let it like write PRs to.

And this way it kind of has this like memory concept um that is like you can scale this to infinite amounts.

It'll just run like 50 uh if anyone's an engineer here like GPS uh against the thing to try to find like all the like keywords that you're looking for um without using ve there's no vectors.

is entirely like syntactically oriented. So it can like you know be very like meiy in its approach to finding its content.

Um and like it has a ton of like you know infinitely complex calculations or anything else like built into it example queries all the cache stuff and at the end of any session you can tell the agent just save this to your context make sure it's always there and it'll write a PR and uh you can merge that and uh you will kind of just keep getting smarter.

Now when it gets into the SQL mode uh this is kind of the thing that we really do very differently from every other company in the world uh who claims to have something like this which is we don't try to oneshot the SQL.

What that means is we don't try to write SQL hit run and like make sure the query is perfectly correct because anybody who in the room has who has ever written SQL before knows that if I give you 10,000 pages of documentation and a SQL terminal and I tell you hey can you help me figure out which of our customers are going to turn uh and you can only hit the run button once you're going to get the wrong answer.

It is like literally impossible. It is not how you go about generating SQL.

And so for us, we have the agent explore the database.

It it it's looking at the information schema for anyone who doesn't isn't SQL literate. What this means is it is literally saying tell me every table table you have. Um and then tell me every column you have.

Um and then like it's going to scan through the whole thing and then like anything that's not relevant, it's going to dump out of context.

Um and so it can like run for an incredibly long amount of time as it like tries tries to step through all of this stuff.

Now, um I guess while it's running this, yeah, it's like like pulling the it's pulling the data. It's forming hypotheses.

It might like look at a daytime field like data warehouses are incredibly messed up environments.

Every enterprise has like 50 of them.

Every enterprise has another 30 different BI tools from like the past like 35 years.

Um three different ERPs, 17 different CRM.

And so there's a lot of like hypothesis testing that like the agent steps through as it's exploring.

It has to like go, oh, like plot the daytime columns. Oh, cool. Uh there's a drop here. There's a data pipeline that was probably broken.

Um so it's a lot of it's a lot of like hypothesis testing so it can like recover from all of the things and all the pieces.

Um I think it's a written some like uh you know written some plots, written visualizations.

Uh eventually now it's like referencing our CRM uh and so it can directly plug into any other data sources that you have uh that is very fast to configure because well it's making an API call the exact same way that any one thing can make an API call.

Um, and yeah, while it's doing all this, I can show you uh what happens after I complete this, which is um I would take like a completed playbook or like a report or something like for example this daily customer upsell analysis that tells me which customer I should reach out to and ask them for more money.

Uh, and once I know that, you know, it steps through like 50 steps and it'll give me a good result, I can schedule this to run every day, every week, every time a customer sends me an email um to give me a new report like this.

Right? This is how you um kind of go from like the uh ask questions and it'll tell you what is this number to eventually ask questions. It'll tell you why did this number go up and eventually uh ask questions why did this go up? Okay, given why that you know why it went up tell me what I can do to make the number go up or down depending on if it's a good number or a bad number.

And that's like a very very useful thing because once you do that you make way more money having the thing uh I guess like you know run on a loop and like just find you more money.

Okay, it's done.

So it's finished the final report. it came back.

There's like a whole thing. It says uh you know the uh the the expected stuff is all correct.

There's a like 3% delta I think because we changed the way we count something.

Um and also the August number was like way off because uh this this like thing was like presented to me in August.

If you read the text, it says, you know, chart displayed August to date for August represented approximately August 13th, which is probably correct.

If I go back and check when he sent me this uh or I'm going assume it's correct because there's no reason for it to not be correct.

Um and then it'll tell me like uh present actually. Okay, fine fine fine. I'll check I'll double check that Matt uh Matt Matt actually sent me this at that time.

This is uh August 14th. It's we're off by a date. It said approximately 13. It's actually 14.

Um and the uh and the and the thing comes out to uh revenue growth ACU usage uh something something corresponds to the thing.

Uh we should tell Matt's data is partially correct, incomplete.

I likely working with midmon data, exceptional performance, celebration worthy data, something something.

This is great. I'm not going to get fired.

Uh and uh now I'm really happy.

And yeah, the that's approximately uh the demo.

Uh like we uh for people who like wanted are curious about how deeper how much deeper we go like there's a lot of infrastructure that we had to build out to make this possible.

We've like rebuilt this product for the first two and a half years of this company. We rebuilt this product seven times over, right?

We tried like cataloges, fine-tuning, SQL terminals, vector stores um like like cataloges with vector stores and sandboxes and SQL stores and all these things.

like we added like ductb we added polers we added um like we started with the sandbox so we could execute and we're as as I've been recently reminded uh we're the first company to publicly release a version of code interpreter uh that wasn't in beta and uh and then we were like oh the data is getting really big okay well we need something that's not just pandas in python it's going to be polars then we were like okay the data got even bigger okay we need like DB got even bigger passed down to the data warehouse got even bigger and we needed joints on the fly serverless click house infrastructure then we had to roll a semantic layer for because some people wanted deterministic queries to come out every time then some of those queries took a really long time so we added caching at acceleration and then some people had Tableau and looker and powerbi and all those things so we had to build our own MCP servers for all those platforms uh because uh data bricks and snowflake and Tableau's MCP servers are moderately maybe not the most usable things in the world in summary uh if If you if you want to roll this yourself at your own company uh company like ramp or with exceptional engineers or something you should do that uh this is totally doable now with today's technology in GPD5 all you have to do is build your own uh compute format uh table format uh that is natively integrated with your compute format that can do this across multiple different large data sources.

Build your own MCP servers for the Tableaus, 17 different ways of ingesting data.

Four out of four of them break. We actually found like a roundabout way to actually get this to work.

So much so that even the Tableau team currently tax us into accounts to try to I guess like to figure out their AI things for customers who want to use Tableau AI but can't get it to work.

We rolled our own semantic layer. It's compilable, transpilable from like all existing semantic layers um like look ML cube metric flow and uh and and like a really cool like guey that looks kind of wild when you put in over a thousand objects onto the same thing uh at the same time.

and uh and our own agent builder which you know this is like very different from like because we're running like a more non-deterministic agent uh like uh like glide code modality wise it looks very very different like the right UI builder for something that's going to be less if else statements and more use best judgment and run for like 2 hours or 10 hours as like test time compute like kicks in for like the latest like generation of models you generally want like more logic on the trigger way more uh instructions on the prompt environment and then like an easier iteration cycle for testing because you're going to be running these models for like hours and hours.

Now, this is not the best UI in the world, but um generally pretty bearish on um I guess like the canvas based like UI for like agent building.

We rolled our own I guess we we have a semantic layer which means we need tools to query the semantic layer.

We rolled our own version of um we we took like lookers like metric explorer and palunteers object explorer and combined them onto something that works on the same surface area.

Because sometimes business users want to ask get me all the blanks like customers or something with all these attributes and that's like an object question because every row is a noun and sometimes people ask show me how x y and z metrics change over time and uh break it out by 17 different objects that come from 35 different tables and that's going to be also a horrible thing but that's a totally different type of math um to get correct.

I know a lot of people in the world offer the uh offer like AI for SQL, AI for big data, AI for data warehouses.

We work with a lot of customers who've been, you know, two and a half years in on building semantic models for Snowflake and they can get it to work for exactly 20 tables.

Um, and you know, we can for anyone here who cares about asking questions of their data and getting decision intelligence and all that stuff and building AI that can work with really big data.

Like if I can't make sense, if our agent can't make sense of your data warehouse with, you know, over 100,000 tables even um, in any data environment with no configuration time and it can't build its own semantic model and like do all the data integration for itself in the same loop and like do all the PRs and change your DBTs and everything else required to like get it to a place where it can analyze all your data. I will buy your entire whole team data team uh, NOU with our VC dollars.

Uh we've made this offer I think like 20 times. Uh nobody's taken up up on it.

And uh maybe one day uh we will come across a horrible horrible I'm guessing it's probably going to be like a manufacturing company with like IoT devices um like with like infinite like joint complexity.

Uh but uh until then uh yeah I'd love to you know thank thank you for watching.