Independent Analysis: AI, SaaS, and Revenue Growth Strategy

Taisa Noetzold: The question I get on almost every call is some version of: What LLM is your product built on?  People have been burned by tools that look great in a demo and then produce a wrong number in production with no explanation. So they come in skeptical, which is fair. The pitch that AI can answer data questions has been made a lot of times by a lot of companies, and most of them are using probabilistic models that cannot guarantee a consistent output. We have to establish pretty quickly that the architecture is different before any other conversation can happen..

TN: The underlying technique is compositional learning framework, which has roots in computer vision. We train a custom language model based on customer’s database structure, business logic and relationships in it. When someone asks a question in plain language, the system translates it into an exact database language – it could be SQL, MongoDB and others. The answer comes directly from your database. It is not prompting a general-purpose model and hoping the output is correct.

The underlying technique is compositional learning, which has roots in computer vision research. The result is a custom language model built specifically for your data. The model does not predict an answer. It constructs the query and executes it. That distinction matters for everything downstream, from accuracy to cost to compliance.

TN: It means the same input, same output. Ask the same question, and you get the same result every time. There is no variability based on wording, timing, or randomness. Each output can be traced back to explicit logic.

At Chata.ai, the core architectural principle is straightforward: we do not generate the answer itself. We generate the database query. The database produces the answer, and databases do not hallucinate.

When someone asks a question in natural language, our proprietary models are not trying to invent or estimate the response. Their job is to translate that question into the precise database query language required, whether that is SQL, MongoDB, or the native language of a specific data warehouse.

That query is then run directly against the database, which returns the exact mathematically correct result. The AI’s role after that is simply to present the result in a user-friendly format.

Compare that to generative AI, which is probabilistic. It predicts the most likely answer given the input. That is fine for drafting a summary or generating an image. It is a real problem when a CFO is looking at a liquidity number, or when a compliance officer needs to reproduce exactly what the system returned six weeks ago.

The compounding math matters. A 5% hallucination rate sounds manageable in isolation. Run a three-step analytics workflow and that error compounds to an 85.7% accuracy rate across the chain. Chata.ai's system has no hallucination rate because it executes defined logic against actual data.

TN: The proof is in deployment. Every query is logged. Every result traces back to the exact query logic that produced it. There is no black box to explain after the fact. And practically: we run on standard CPUs, not the GPU infrastructure generative models depend on. If we were running a probabilistic model under the hood, that would not be architecturally possible.

TN: At the product level, Chata.ai is built to enforce the governance model an organization already trusts rather than asking customers to replace it. We connect to the native controls already in place across the customer environment, including role-based access controls and row-level security where applicable, so permissions are enforced at query time. In practical terms, if a user does not have permission to access payroll, customer-sensitive, or other restricted data in the source system, Chata.ai does not create a back door to that information. The system respects those underlying permissions and only allows queries within the boundaries the organization has already defined.

From an architecture standpoint, the platform is designed so data stays where it already lives. Chata.ai connects directly to structured sources such as databases, warehouses, APIs, or governed exports and queries them in place. It does not require the customer to move, merge, or centralize all data into a new environment just to use the product. That is especially important in regulated settings, because reducing unnecessary data movement reduces risk and helps organizations maintain existing control boundaries.

On deployment and compliance, Chata.ai supports single-tenant and multi-tenant models, and it can be deployed on-prem, cloud, edge, and air-gapped setups, depending on requirements. Even considering that data will never leave the customer’s environment, we are SOC 2 and ISO 27001 compliant.

TN: We do not publish a one-size-fits-all setup price because it depends on scope, deployment, and support requirements. Today, we charge USD $0.05 per outcome. The bigger point, though, is the production cost curve. Chata.ai runs on CPUs, not GPUs, so costs stay far more predictable as usage scales. Internally, we position that as roughly 500x lower CPU cost than GPU-heavy approaches, which makes production deployment much more practical at enterprise volume.

TN: A strong example is Sync Insights on the Canton Network. That matters because Canton is not a small or experimental environment. It is an institutional-grade blockchain for highly regulated financial assets, used by major firms including Goldman Sachs and BNP Paribas. Sync Insights is the analytics layer for that network, powered entirely by Chata.ai.

Since June 2025, Sync Insights has delivered more than 450,000 financial insights, with 24/7 real-time analytics coverage and anomaly detection and 100% consistent, repeatable outputs. In practice, that means users on Canton can monitor network activity continuously, spot issues early, and make decisions faster without needing deep technical expertise or waiting on specialists to manually pull data together. 

That is the real outcome: Chata.ai turns a complex, multi-source, highly regulated data environment into something business users can actually operate against in real time. Instead of routing every question through a technical team, they get governed answers and alerts directly, while the system correlates signals across sources through deterministic monitoring and conversational analytics. 

This is not about one or two companies using a dashboard. It is about supporting an institutional financial network where scale, auditability, and uptime matter every day.

SB: You closed a $10 million Series A in January. Where does the investment take the product?

TN: On the market side, it is accelerating growth in traditional finance and decentralized finance. And from there, the roadmap expands into other high-stakes markets, including gaming and other operationally intensive sectors like transportation, where the need is the same: real-time monitoring, governed analytics, and human-in-the-loop actionability inside existing environments.

On the product side, we have DIY model training, so teams can train their own models. 

We are also working on new integrations including making easy to integrate our technology to agentic AI workflows, while keeping Chata’s core intelligence deterministic and governed. 

TN: The focus coming out of that round is traditional finance, decentralized finance, and wealthtech. These are sectors where a wrong answer has regulatory consequences, not just operational ones. The investors understood that. The thesis from 7RIDGE and Izou Partners was specifically about the compliance requirement and the audit trail — not just the analytics capability.

On the product side, we are expanding what we call AI workers. Right now, a user asks a question and gets an answer. AI workers transform a query into a continuous monitor. You configure it once and it runs automatically, alerting the right person when a threshold is crossed or a metric moves. The direction is from self-service analytics on demand to analytics that surface insight without anyone having to ask.

TN: The question they should be asking is: what happens when this system is wrong, and can I prove it? With probabilistic tools, the answer to that question is uncomfortable. You get an output that looked reasonable and you cannot reconstruct how it got there. With a deterministic system, you can always trace a result back to the query logic that produced it. That is what compliance teams need. That is what auditors need. And frankly, it is what any executive should need before they sign off on a decision that came from an AI system.