Fragmented AI workflows — add a unified control layer for governance & cost

Executive Summary

Many engineering and platform teams at mid-to-large enterprises are now running fragmented AI workloads across multiple LLMs, specialty models, and cloud providers, creating gaps in governance, provenance, cost control, and latency-sensitive routing. These groups—typically platform, compliance, and finance stakeholders at organizations spending tens of thousands to millions annually on inference—struggle to enforce policies, audit outputs, and stop surprise bills. You could build a unified control layer that sits between applications and model endpoints to provide centralized policy, cost-aware routing and caching, standardized audit trails with provenance, human-in-the-loop workflows, and turnkey integrations (SSO, SIEM, major LLM vendors). The product would include an SDK and control plane console for policy authors, a runtime that routes per-request based on cost/quality/SLA, and immutable logs to satisfy regulatory audits. Customers could materially reduce inference spend and compress audit lead times, though the magnitude of savings will vary by workload and adoption level. The timing is favorable—multi-model adoption, rising inference cost awareness, and regulations like the EU AI Act make the $30B TAM (1,000,000 organizations × $30K ACV) and high market/revenue scores credible drivers of demand. This can stand out through deep, low-latency cross-vendor integrations, a policy-first architecture built for auditors and finance, and a focused enterprise go-to-market, but expect tough engineering work to integrate vendors, the need to prove ROI via pilots, and long sales cycles with security and procurement teams.