Missing verified business outcomes — AI marketplace for verified outcome data

Executive Summary

Many AI teams and product managers at an estimated 5 million commercial businesses lack reliable, auditable ground-truth for model training and evaluation, which directly increases development costs and model failure rates such as hallucinations and regressions. This problem is acute for any task where downstream outcomes must be verifiable—search relevance, fraud detection, medical labeling—because raw label dumps are untrusted and internal labeling is slow and expensive. The solution is a curated marketplace that sells packaged, API-delivered verified outcome datasets and signals with SLAs, signed provenance, and auditable lineage, priced to support a typical $2,000 ACV buyer profile while offering pay-for-performance options and enterprise contracts. Core features would include standardized outcome schemas, cryptographic or third-party attestation of labels, privacy-preserving collection workflows, on-demand evaluation hosts, and a reputation system so buyers can discover high-quality suppliers. This is an attractive moment: the market can be sized at roughly $10.0B (5M businesses × $2K ACV), analysts rate this opportunity 95/100 with a 94/100 revenue potential, and macro trends—rising AI evaluation spend, the shift to productized data, and increasing regulatory demand for auditable datasets—all accelerate buyer willingness to pay. Differentiation will be earned by rigorous verification processes, vertical-first go-to-market focus, and defensible audit tooling, but realistic challenges include the high cost of sourcing and verifying outcomes, complex privacy/compliance work, and the need to build two-sided liquidity against medium competition. Pursue this if you can establish early partnerships in high-value verticals, engineer low-cost verification and provenance, and accept a multi-year effort to scale trust and network effects.