We built DocuTect AI after repeatedly seeing the same issue in real AI-driven systems: models often sound correct, but fail silently when it comes to understanding APIs, documentation, or multi-step workflows. In real deployments, this creates a gap between “demo performance” and production reliability — especially in systems that depend on LLM outputs for decisions, automation, or data extraction. DocuTect AI started as a simple idea: validate whether AI outputs are actually grounded in the context they claim to understand. Over time, it evolved into a broader reliability layer that evaluates hallucinations, checks consistency, grades outputs through independent verification, and assigns trust scores before deployment. Our approach also changed during development. Instead of focusing only on detection, we shifted toward evaluation pipelines — treating AI outputs like testable artifacts rather than assumptions. This led us to build modular checks for prompts, documents, APIs, and workflows. The goal is simple: help teams trust AI systems before they go live, not after failures appear in production.

Docutect AI

Audit every LLM hallucination in your APIs & docs

Audit every LLM hallucination in your APIs & docs