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← Business Ideas · Project: Ontology-Grounded Agent Compliance Checker

Ontology-grounded compliance layer that blocks invalid agent tool calls before they hit regulated systems

Customer: ML engineer at 20-200 person fintech or digital health startup who owns their LLM agent stack, is getting pressure from compliance/legal to audit agent behavior, and has no budget for enterprise AI governance vendors

Problem: Agent tool calls and outputs are opaque — no runtime enforcement that outputs conform to domain rules (e.g., FHIR constraints, FINRA suitability rules). Post-hoc logging is not enough for auditors. Pre-deployment red-teaming misses edge cases at runtime.

Pricing: saas-mrr — $1,200 MRR in 4 months (6 customers at $200/mo)

Why now

Multi-agent architectures hitting regulated industries right now; compliance teams waking up to audit requirements before engineering teams have solutions; ontology-based constraint enforcement is proven in academia but has zero accessible tooling for practitioners

Go-to-market

  1. Post a deep technical writeup on the problem (with a real finance or healthcare ontology example) on Hacker News / LessWrong / Substack — target ML engineers, not executives
  2. Build a free open-source CLI that wraps any agent’s tool call/output through OWL ontology validation — use it as the lead magnet, hosted SaaS is the paid tier with audit logs + dashboard
  3. Cold DM 30 ML engineers on LinkedIn who have posted about deploying LLM agents in fintech or health — ask for 20-min call, not a sale; offer to audit one of their agent schemas free
  4. File under ‘compliance-as-code’ in relevant Slack communities (Latent Space, MLOps Community, AI Engineer) — the framing matters more than the tech at this stage

Moat (or lack thereof)

No real moat. Ontology tooling (owlready2, RDFLib) is open source; any engineer can build this. Defensibility comes from: (1) accumulating domain-specific ontology packs (FHIR, FINRA, HIPAA-mapped) that take time to curate, and (2) becoming the reference integration in a popular agent framework like LangChain or LlamaIndex. Neither is guaranteed. This is a first-mover-in-a-niche play, not a patent moat.