Hallucination Reduction as a Service
An infrastructure layer that plugs into any AI agent and enables the synthesis of verifiable mathematical algorithms from formal specifications, with guaranteed correctness.
The Logos layer intercepts each reasoning step, verifies it against Lean 4, and guardrails the agent's chain of thought.
User provides a mathematical spec. The result is a formally correct algorithm with a machine-checkable proof. No hallucinations, guaranteed.
Works with any frontier LLM — Claude, GPT, Gemini. Bring your own API key.
A proprietary database of applied mathematics, and search engines fine-tuned and optimised for it.
Each repo is a graphical blueprint of mathematical knowledge.
Full history of every formalized theorem and definition. Diff, roll back, branch — just like code.
Enterprises keep proprietary formalized strategies siloed.
Generate blueprint from formal spec
Absorb into LogosLib via post-processing
Record failures as reusable tactics
Fine-tune search engines on new content
↻ Each problem improves search for the next.
Given a formal spec for an IO-optimal GPU attention algorithm — without mentioning FlashAttention — the agent independently identified, synthesised, and proved the algorithm correct in Lean 4.
Formalisation of an entire chapter of a graduate stochastic analysis textbook — Baudoin, Diffusion Processes and Stochastic Calculus.