AI Can't Make Up a Number It Can't Look Up - Vinoo Ganesh, Kepler

Hi everyone. My name is Venu Ganesh. Um, excited to be here today. Uh, I'm the CEO and co-founder of Kepler. Um, this is going to be kind of an interesting talk because it comes from what happened 3 months ago. So, 3 months ago, uh, I think there's a little bit of an overlap, but I gave a talk at AIE World Fair, uh, uh, really around verification, um, and why that most institutions need verification in some form of the trust ecosystem of AI. Well, yesterday most of you saw Jack and I were on stage talking about why Jack, who ran Citadel's biggest book, and started Candlestick Capital, still doesn't trust AI. Since that 3-month period, there's been about a 270 million dollars of VC money that got poured into formal verification layers. A VC actually took Kepler's own pitch deck and gave it to a competitor, and produced something that has our same tagline. I'm not bitter, just a little, but the funny thing is they copied the wrong part. And so, what I want to do is actually give you, the practitioners today, you can't just tack verification on at the end. And so, uh, when I gave that chat or that talk a few, um, months ago, uh, Anthropic had just published an article on how Kepler does what it does, and why it's really powerful and and really unique.

So, what I want to do is get into the technical side of how we do the thing that we do, and ultimately this is driven by one very simple insight, Wall Street still doesn't trust AI. Most people think it's because of this. Uh, AI will estimate things. It will hallucinate things. A frontier model will make stuff up. And ultimately it is, and that's a component of it. But the actual core underlying problem is we have a structural problem in this technology that we're all so desperate to use. Meaning, if you actually chat with people that are building rag platforms, most of what they'll say is, "Well, the models will eventually get good or get better to the point they will eliminate hallucination." That is a nonsensical term. You know another word for hallucination? Alpha. Alpha is how a firm makes money. The creativity that you have and that your analysts have is a mechanism through which you can be a unique firm. Otherwise, there should be only one company on Earth. So, when you ask these types of simple questions around where data comes from, there is a piece of this that the model will just make things up, and there's a piece of this that your firm's internal data is never actually reflected in this setup.

So, what are we seeing right now? We're seeing everyone with a checkbook buy everything they can. Every rag platform, some firms are trying to build, some firm some firms are running agent pilots. I had a a customer meeting this morning where someone said they were actually an AI-first firm, which means they have one co-pilot agent running all the stuff. So, why are we not seeing the complete switch of fundamental investing becoming effectively, why are we not all Jane Street? Why Why is that not happened yet? And so, the challenge really comes from this idea of in order for you to produce work product that you believe in, you have to trust it, meaning you have to blame somebody who gets it wrong. So, we see numbers like this all the time, where 80% of finance execs trust numbers, but then 31% find those errors. So, what do we actually need? We need a situation where the reviews that we're getting for AI apps, as builders ourselves, do more than just ragify things.

And And these are quotes we've actually pulled from specific uh I don't I don't want to say our competitors, but other people, where things demo super well and they behave like a really smart intern. Or things are right if you just hook up the right structured information, but that last 10% still gets things wrong. So, what I want to do is we're lucky, we have great partners and folks like Turbo puffer and I gave a similar chat yesterday about this, but how do we actually do the verification? How do we architecturally build a system that does not hallucinate numerically? So, let's talk about it. Now, wrong numbers or wrong facts come from three sources. Either one, you retrieved the wrong document. So, I pulled a document, some random thing from somewhere else. How do you fix this? Well, by retrieving the right document. But, that in itself is complicated. Is the right document my own firm's internal analysis of what this stock is going to be worth? Is it, let's say, uh some analyst who I really trust more than anyone else?

That's piece one. Now, failure two. We get the right source and we pull the wrong number out of that source. Let's say we're trying to figure out what the revenue for, I don't know, Palantir was back in 2023. Well, if I look at the 2025 10K that says Palantir's 2023 revenue was this, I better make sure that that number corresponds to 2023 and not 2025. So, the fix here is really we have a filter that actually checks each citation. But, this is still a problem because that filter only applies after the fact. And formal verification doesn't actually help here because the thing has already been produced. Three is the mental math. Models don't do math. They predict the next token. Now, for those of you who are going to say we have deterministic models, great, we do. We also have one CPU cycle that's good at adding two numbers together. Why don't I use that CPU cycle? So, third failure is let's use deterministic tools that we already have to actually compute numbers and entities rather than relying on some external tool.

So, here's what we do. This is the magic behind Kepler, at least for piece one, there's a whole bunch of other stuff. We are architected fundamentally differently. Code writes every number and what code does in pre-processing is we assign pointers to unique identifiers. Meaning facts. Meaning something like a number in a 10K. This operates in a giant pre-processing and contextualization step. Such that all of this exists, going from unstructured data to a structured data store. Then, the model is effectively a smart analyst. It figures out how to go about solving the problem, but not actually being the thing to solve the problem itself. So, it plans. It says, "I'm going to go here and retrieve this source and retrieve this entity and do this thing." Code and math do the retrieval. By actually pulling a number out of a 10K at like a search time, that's a hugely expensive token cost. And Anthropic and OpenAI should thank you for paying all those tokens in token maxing, but most of your firms don't like that, at least anymore. I know there were people getting bonuses from token maxing for a while.

Um Now, the next piece of this is really once the model's actually read both these pointers, it can't structurally generate numbers. Meaning unless the model actually has a mechanism of dereferencing that pointer, that number or that fact cannot be generated in Kepler's output. Now, we do run a post-processing step, which is the verification step. And this is the piece that was kind of funny where the last company just copied the verification piece, but all the structural stuff beforehand didn't make it. So, what you as a consumer actually see is you see something like the revenue is this amount to this amount, and you physically can click on these chips and see exactly how we got the information. Now, in my past life, I had an opportunity. I I was head of business engineering at Citadel. So, I worked across um the firm to figure out effectively what do we need to produce software that helps us generate alpha. You know the crazy thing with Citadel? PMs are so talented and so smart, but they work when they're paired with deterministic tools and technology.

And that's kind of the magical insight behind fundamental investing. A fundamental investor so good at understanding data, but that data has to be right. And so, when you build a system like Kepler, what you effectively have to do is look up the right information and then be clever about the information you don't have. So, let me give you the parallel. Most folks and most buy-side researchers or buy-side investors try to use tooling or build DCFs on their own and then they find and search for information they don't have. They schedule GLG or Guidepoint calls. They find experts. They speak to management. The same system actually applies here. So, what we do is we pick the path and we store the information deterministically that we can actually store deterministically. When we have information we can't store deterministically, we run similar preprocessing steps to grab information, store that information in a hybrid storage mechanism, which is a combination of Turbo Buffer, which is awesome by the way, and a bunch of other um mechanisms. And then we ultimately use a combination of re-ranking with literally old-school like pointer dereferencing like dot get methods to pull things into our system.

Now, why this matters is a lookup cannot get it wrong. Search can. If you're running the same 10 searches with the same query, the probabilistic nature of models means there's inherent entropy. That's actually an opportunity. And so, that's what we've changed here. Now, how we do this is we break this down and this occurs across of the SEC world as well. Um we are frequently throwing candidate documents away. Meaning we consistently have to look at not only what source is the right source for what information, but also which documents should we stop looking at kind of globally. So, uh we run ANN plus BM25 to get back a set of documents. It's usually up to 200. Uh we do reciprocal uh ranking We effectively get 60 documents back. It's probably the best way to think about this. Um then we do cross-encoders. We try to figure out exactly the query and the actual document chunked together and get those results that way.

What that allows us to do is we can search really wide. You know what LLMs aren't good at doing? Searching really wide. What they try to do is create a plan and find either the fastest or easiest breadth-first search mechanism actually made depth-first search mechanism of actually like getting the first document and moving forward. So we allow this to search really wide and then narrow from there. The reason LLMs don't like searching wide is because it's expensive. It's because looking across a bunch of different sources is significantly worse than getting the reward function back with your first hit and going from there. Okay, so um I want to talk a little bit about security here cuz it's super important. Kepler was built with a security-first mindset and security-first mentality. Now client documents, and this is specifically targeted towards the builders here, this is a thing that no one's chatting about. No one is actually talking about the security of what OAuth token is used and what user-scoped mechanism of retrieving documents, which is actually pretty scary.

So let's talk about how things go wrong in these systems that people are trying to build and how you can fix it. First and foremost, you forget to filter the tenant. All of a sudden Citadel can search through all the Millennium's documents, you will be in a lot of trouble. Second, a caller claims to be another firm. Meaning I can claim that I'm impersonating this other entity and querying documents that way. Third, search returns information that's out of date. We've had this problem kind of globally and even Google search results. Fourth, direct read skips the search rules. Meaning you're not actually querying the full corpus of information or like there's not an allow list, you only get back the single document that you did the search. Five, you hit an index that isn't populated. The way to actually solve all these problems, and there's individual things listed here, is actually scoping searches with security built in. That's kind of another critical piece that I think a lot of people miss when they're trying to build these systems themselves. Inevitably, people will start caring about the security associated with querying this information. Alpha is the most elusive thing out there, especially for folks on Wall Street. So, making sure you have a good mechanism of actually constraining the set of documents that come back with each query is really important.

Okay. Now, let's talk about verification, which is the last piece here. This is probably what we're known most for, but we do the other stuff I mentioned as well. Um how we do this is by pre-processing information and storing it deterministically. We have systems like code that actually write individual pointers. The model can only then read those pointers and grab information back if they exist in the database. If I grab something out of a hash map that doesn't exist, I don't get anything back. And so, this combination is what allows us to actually make sure the information that we retrieved actually came from the source that it came from. Now, this is a kind of hidden version of what our source IDs look like, but these are hashes, and these are very simple things that you can do in your own system and your own setup to ensure that you're pulling back facts that in fact do exist. Cool. So, that's a little bit about how we do verification. Now, I want to chat a little bit about what this enables kind of globally.

This enables us to verify facts that come out of our grounded corpus of data. Now, you as a builder, or you as a startup creator, or you as someone who's trying to figure out how do I trust my outputs, really just need you to do a couple of things. You need to show people that as you're creating these agents that are theoretically looking for alpha, or whatever they'd be looking for, you're using information that is actually relevant and concrete, and most importantly accurate, to the set of people that you're trying to service. So, we end up with a contract where citations are either verified, corrected at search time, or flat out refused. This is crazy if you think about it. If a model cannot deresolve that pointer, it cannot print that number out. So, that's kind of an insane thing. It means that the whole and not just a number, let's talk about facts. Like we see these court cases all over of precedents being hallucinated. Well, why can't we create a system that just has the precedent stored deterministically and allows us to search through that list and not have a random court case that never existed all of a sudden start existing?

So, this tech and this this mechanism and this technology actually becomes fairly globally applicable. And the reason I'm on stage telling you how we do what we do is because I'm terrified that Wall Street never trusts this technology because of the fear that so many other companies or so many build-it-at-home folks have been uh instituting in these organizations. It's It's gotten to the point where the vast majority of finance AI tools right now are being used to format PowerPoint presentations. We still haven't actually built a DCF cradle to grave despite what most people are saying. And if we had, every single company on Wall Street would have adopted that overnight. So, this is my fear. My fear comes from a very simple fact. For us to actually get value out of this technology, we need to be able to build technology that people actually can trust and can verify. That isn't just true in finance, it's true in a bunch of different industries. So, please take what you need to from this presentation and get people using this technology, otherwise we're all going to be out of a job.

Now, I want to try through what happened when we saw this. So, when we went through this whole process, um this is actually some of our our real production data here. Um refusals are when the model does not is not able to produce a number. Meaning, the model will say, "I couldn't dereference this pointer, therefore I could not produce this number." We saw that drop super drastically. Now, the reason that it didn't drop to zero is because models are still probabilistic. Meaning, it can still make up numbers after the fact during inference. So, there was another pre- a post-processing step after this that we we have as well. But my point with this slide is just by creating a system where the models have to dereference their own pointers, you can actually create a very powerful, very verifiable AI platform. And that to me is my ask to all of you. As you go through the building process, as you go through the scoping process, any tool that you use, ask them if you can actually verify the numbers. Ask them if you can see where the sources are, not in terms of some after-the-fact annotation, but click on the number, whether it's a calculation, whether it's an extraction, or some combination of the two, to make sure you trust the insight.

And all of this ultimately is going to be the unlock. So, you as a vendor or you as a builder, ask these questions. Does the model actually write pointers? Does it actually create a mechanism for verifying the output? Is every citation source checked? Is arithmetic source checked? Do you have a mechanism for stopping files that reach clients or customers that it shouldn't reach? And most importantly, what happens to a number that came from nowhere? What happens when the when a fact comes from nowhere? And how do I actually handle these kinds of of hallucinations? So, this whole platform is actually to make a very simple statement very obvious, which is we can have the smartest models in the world. We can hit some crazy cool piece of technology, but we still have the same database problems that we've had from the beginning, which are I cannot if I search through a a row and that column does not value doesn't exist, I won't get a retrieval back.

We're now out of the search phase of AI. We need to be building infrastructure. We need to be building scaffolding and harnesses and substrates or whatever the cool VC term is these days that actually enable us to make use of this technology. So, this is my challenge and my ask to all of you. Um I hope you go out there and build something compelling, but I hope you go build something compelling that you can trust. Thank you. >> [applause] >> Hey.