Stop AI hallucinations derailing research — Controlled RAG with auditable provenance

Executive Summary

Enterprise teams deploying LLM assistants for knowledge work—legal, life sciences, finance, research and product teams—are routinely disrupted by model hallucinations that derail investigations, waste expert time, and create regulatory and reputational risk. The problem is large and addressable: roughly 20 million enterprise knowledge workers and a $30.0B global AI-assistant market ($1.5K ACV) mean the economic incentive to eliminate trust gaps is substantial. You could build a Controlled RAG platform that enforces retrieval policies, produces deterministic retrieval decisions, and emits auditable provenance for every model response—cryptographic checksums for source passages, immutable lineage and versioning, human-review workflows, and a policies console for data owners. The product would ship with connectors to common vector stores and enterprise systems, a developer SDK for reproducible queries, model sandboxing to limit extrapolation, and an immutable audit trail mapping claims back to source passages and timestamps. Implementation risks include integration complexity across heterogeneous data sources, latency trade-offs from stronger determinism, and the engineering cost of building robust, tamper-evident provenance. Timing favors entry: enterprise AI adoption and emerging regulation (e.g., the EU AI Act and industry compliance regimes) increase willingness to pay for provable provenance, while open models and hosted vector DBs lower infra cost and speed go-to-market. To stand out you’ll need to prioritize developer ergonomics and measurable outcomes—turnkey connectors for the top 10 enterprise data systems, pilot metrics that demonstrate reduced hallucination incidence, and third-party attestation for provenance—while acknowledging the real challenges of convincing security teams, maintaining low latency, and sustaining provenance integrity as models and corpora evolve.