Detect underspecified model inputs & orchestrate missing-fact collection (clarify + fetch)

Detect underspecified model inputs & orchestrate missing-fact collection (clarify + fetch) targets a $20.0B = 400,000 enterprises x $50K ACV (enterprise model-governance + prompt-orchestration spend) total addressable market with medium saturation and a year-over-year growth rate of 30% (LLM tooling and MLOps category expansion).

Key trends driving demand: LLM adoption in enterprises -- creates a need for predictable, auditable outputs and pre-invocation checks; MLOps & model governance growth -- drives demand for tools that sit before/around inference to ensure correctness and compliance; Hybrid human + AI workflows -- organizations favor systems that combine automated checks with human verification for high-stakes outputs; Embeddings and retrieval augmentation -- enable automated fact-fetching to resolve underspecification prior to inference.