Platform Overview
What Fact AI Lab does
Fact AI Lab is eval-first AI automation infrastructure for regulated industries. The platform sits as a thin verification layer between your application and your LLM provider. You do not change your prompts or your model - you pipe outputs through the platform before they reach users.
The core product is automation. Compliance, safety, and data privacy are built-in features of how the platform works, not the entire pitch. The goal is to let your team ship AI-powered workflows with confidence, knowing that every output has been verified before a user sees it.
Who it is built for
Fact AI Lab serves teams at 50-500 person firms deploying AI in high-stakes workflows across financial services, real estate, legal, insurance, healthcare, and regulated SaaS. The primary buyers are CROs, CCOs, CFOs, and VPs of Engineering who need to demonstrate that their AI outputs are accurate, auditable, and defensible.
These teams typically face the same set of problems: they are running LLMs over sensitive documents, they need those outputs to be correct, and they need a paper trail for their compliance and legal teams. Fact AI Lab addresses all three without requiring a change to the underlying AI stack.
What the platform does not do
Fact AI Lab is not a chatbot, an AI wrapper, or a generic model provider. The platform does not train on your data. It does not store your prompts beyond the processing window. It does not make claims your LLM cannot back up with retrieved source material.
Your data is never used to improve Fact AI Lab's models or shared with any other customer.
How it connects to your stack
The platform offers a Python SDK, a TypeScript SDK, and a REST API that works with any OpenAI-compatible endpoint. Average integration time for a single workflow is under 2 hours.
Once integrated, every LLM output goes through the verification pipeline before reaching users. The platform returns a verification score, a list of flagged claims, and an immutable audit log entry - all in the same response cycle.
For a deeper look at how verification works, see Core Concepts. For data handling and encryption details, see the Security Model.