The Expert Lens: encoding expert knowledge as toggleable rules.
A scientist will never trust a black-box relevance score. Here's how we built a 9-rule expert lens that scientists could reason about — and turn off — one rule at a time.
The AI Market Intelligence Platform had one non-negotiable acceptance criterion: the lead scientist had to trust the relevance ranking. That meant no opaque embeddings-as-relevance, no LLM-as-judge with hidden criteria, no statistical magic.
What worked: encoding the scientist's actual decision criteria as nine explicit, MCP-compatible rules. Temperature floor. Material class. Chemistry scope. PFAS sensitivity. Market size. Liability exposure. Recency. Novelty. The 200°C gap.
Each rule is independently toggleable. The dashboard shows the score with the rule on and off. The scientist can A/B their own expertise against the system. That's where trust comes from — not from the score, but from the ability to interrogate it.
The pattern generalizes: wherever you need expert trust, encode the expert's heuristics explicitly, and make every one of them inspectable.
Frequently Asked Questions
What is the Expert Lens in AI market intelligence?
The Expert Lens is Enso Labs' pattern for encoding a domain expert's decision criteria as explicit, independently toggleable rules instead of an opaque relevance score. On an AI market-intelligence platform for a Fortune 500 manufacturer, it took the form of nine MCP-compatible rules — temperature floor, material class, chemistry scope, PFAS sensitivity, market size, liability exposure, recency, novelty, and a 200°C gap — that the lead scientist could reason about and switch on or off one at a time.
Why encode expert knowledge as toggleable rules?
Because trust comes from the ability to interrogate a score, not from the score itself. Making every heuristic explicit and inspectable lets an expert A/B their own judgment against the system with each rule on and off, which is what earns adoption in high-stakes domains. Enso Labs applies this wherever expert trust is the acceptance criterion; get in touch at https://ensolabs.ai/contact.
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