Taisa Noetzold: The question I get on almost every call is some version of: is this just ChatGPT pointed at my database? 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: We built what we call a corpus generation engine. The way I describe it to non-technical buyers is that it works like a teacher model. It maps your specific database structure, learns the objects and relationships in it, and builds a knowledge base from that. When someone asks a question in plain English, the system translates it into an exact database query using that knowledge base. It is not prompting a general-purpose model and hoping the output is close to 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 question produces the same answer, every time. No temperature settings. No variation based on how the question was phrased or what time of day it ran. Every output traces back to defined logic.

Compare that to generative AI, which is probabilistic. It predicts the most likely answer given the input. That is fine for drafting a summary. 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.

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: We are ISO 27001 certified and hold SOC 2 Type II. Both required independent audits of our security controls and operational practices. They are not self-reported.

At the product level, role-based access controls determine who can query what. A business analyst cannot pull payroll data if they do not have access to payroll data. Every query is logged. Your data never moves or merges — we connect to your existing database and query it where it lives. Nothing leaves your environment.

The deterministic architecture is itself a governance feature, and I think that part gets underappreciated. Because every output traces to defined logic, there is no mechanism for the system to fabricate a plausible-sounding answer. When something is wrong, you can find exactly where it went wrong. That traceability is what regulated industries need and what black-box AI genuinely cannot provide.

TN: We do not publish a standard price list because the right setup depends on user count, the data environment, and how the organization wants to deploy. What I can say is that because we run on CPUs rather than GPUs, the cost curve does not spike as you scale. Generative AI tools get expensive fast at volume — the infrastructure costs are real. Our CPU-based inference cuts production costs to roughly 0.2% of what a comparable generative AI deployment runs. At scale, across hundreds or thousands of users, that gap is not a footnote.

TN: A railway operations team used Chata.ai to monitor maintenance data continuously. The proactive alerting flagged issues before they escalated into service disruptions. Downtime dropped 40%. Maintenance costs dropped 15%. They stopped running reports after the fact and started getting ahead of problems.

In financial services, the recurring constraint is that business users cannot reach their own data without routing through a technical team. With Chata.ai, they ask the question in plain language and get an exact, auditable answer. The analytics team stops handling routine requests and can focus on work that actually needs their expertise.

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: They ask about features and integrations. 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.