Building domain expertise into AI
cairnic encodes expert judgment into AI products for high-trust decisions. HallucinationFinder for legal analysis. Spec Studio for product engineering.
AI document generation is scaling with compute. Human expert review scales with hiring. These curves diverge permanently. The gap between generation and validation is widening — and the consequences are growing with it.
Hallucinated legal citations. Misclassified regulatory submissions. Treatment plans without expert validation. Specs with disconnected requirements. The generation tools are shipping. The validation infrastructure is not.
The most valuable AI analysis comes not from better models but from better instructions — instructions authored by domain experts who know what to look for, how to evaluate it, and what quality looks like. Cairnic encodes that expertise into AI-powered analytical tools. The architecture is identical across industries. The domain expertise is what changes.
Domain experts define what quality looks like — what to analyze, why it matters, and what good work requires. AI alone cannot make these judgments.
Those expert standards are applied through AI to every document, consistently and at speed. One expert's knowledge reaches thousands of documents.
Findings are specific, grounded in evidence, and organized for action. Every observation references the document. No vague generalities.
Cairnic remembers across drafts. Each analysis pass builds on the last — tracking what's been addressed, what persists, and what the author intended.
AI can apply rules at scale. It cannot establish which rules matter, why they matter, or what "good" looks like. Those are expert judgments.
Rapid pre-filing analysis for legal briefs. Citation validity, holding alignment, sanctions risk, and a sequenced revision path.
AI-powered editorial analysis for short fiction. Expert-calibrated craft analysis, iterative revision tracking, and an integrated workspace. The first product built on the cairnic framework — proof the architecture generalizes beyond legal.
Try Story Studio →Specification validation for product development. Ensures engineering specs stay aligned with design intent, compliance requirements, and manufacturing targets across the full development lifecycle — from product charter through production validation.
90% small firms. 56% plaintiff's counsel. Growing weekly.
9 of 27 citations incorrect. 2 entirely fabricated.
Lexis+ AI: 17%+. Westlaw AI: 34%+. Both marketed as "hallucination-free."
"Explainability turns a GenAI output into a defensible, auditable insight. LLM observability ensures the model behaves as expected over time."— Gartner, March 2026
Gartner predicts 50% of GenAI deployments will require LLM observability by 2028, up from 15% today. The cairnic framework provides both layers: explainable analysis with evidence source transparency, and continuous evaluation infrastructure for output quality.