Non-experts can’t trust LLM outputs — expert-in-loop verification + upskilling

Non-experts who rely on large language models—product managers, sales teams, legal ops, clinicians and frontline analysts—regularly encounter hallucinations, incorrect reasoning, and opaque provenance, and they lack practical ways to verify outputs before actioning them. This is an enterprise problem: if 300,000 mid-to-large companies each could spend roughly $100,000 a year to get trustworthy LLM outputs, the implied addressable market is about $30.0B, concentrated where errors carry regulatory, financial, or reputational risk. A practical product would be an integration-first platform that inserts a verified expert in the loop for high‑risk outputs, combines automated provenance and confidence scoring, and includes in-app upskilling micro-courses and assessment workflows so organizations can grow internal verifiers. Key components are a vetted expert marketplace and workforce management to assign tasks, granular audit trails and exportable compliance artifacts, and connectors to enterprise LLMs, data stores, and ticketing systems. We would pursue outcome- and seat-based pricing (target ACV $50k–$200k) and measure success by reducing unverified high-severity errors and downstream remediation costs (pilot targets of 40–60% reduction). Market timing is favorable: rapid LLM rollout across functions, increasing regulatory scrutiny on model provenance and human oversight, and a corporate shift toward human+AI workflows all increase demand for verification plus verifier training. Differentiation is achievable but not trivial — combining a high-quality expert network, embedded upskilling to reduce long-term dependency on external SMEs, enterprise-grade security and compliance, and clear ROI will matter; the primary challenges are scaling and quality-managing the expert pool, long enterprise procurement cycles, and proving financial returns to offset ongoing human verification costs.