The library for teams putting AI into production.
Frameworks, essays, and playbooks on AI reliability, knowledge governance, and the new discipline of treating data as a first-class input to every AI decision.
Start here
Three pieces that explain the problem, the math, and the architecture behind reliable enterprise AI.
Hallucination detector: how detection works
Grounding checks catch invented claims. They don't catch an answer grounded in a document that contradicts another. Where detection stops working.
RAG evaluation metrics that matter
Faithfulness, context precision, context recall — plus the corpus-level metrics that explain why your scores plateau.
Retrieval drift: why RAG accuracy decays
Your pipeline didn't change — your knowledge did. How to monitor embedding staleness, index freshness, and rising conflict rate.
How to reduce hallucinations in RAG systems
Retrieval lowers hallucinations but never eliminates them. Learn how to detect knowledge conflicts and keep RAG aligned with documented truth.
RAG evaluation with LLM-as-a-judge
LLM-as-a-judge scores answer quality — but point it at your source documents and it becomes a conflict detector. How to evaluate the knowledge, not just the answer.
The CFO's guide to AI ROI
How to quantify the cost of conflicting documentation, outdated policies, and AI-generated misinformation — with formulas you can paste into a board deck.
Govern your knowledge before it governs you
The thesis behind Alignode: why every serious AI deployment needs a reliability layer, and what that layer looks like in practice.
By topic
Pick the lens that matches what you're building today.
Guides
Practical, step-by-step playbooks for shipping reliable AI.
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Engineering
Deep dives on conflict detection, alignment graphs, and retrieval architectures.
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