Keep AI copilots local by registering local/company resources for private tasking

Security, compliance, and AI product teams at enterprises and mid‑market companies confront a growing dilemma: cloud-based copilots accelerate workflows but can expose sensitive documents, internal APIs, and PII to external inference services. There is no standardized way to register and orchestrate local or company-owned resources for private tasking, so organizations resort to manual workarounds, shadow IT, or wholesale blocking of copilots—creating productivity loss, audit gaps, and compliance risk. You could build a lightweight orchestration and registry platform that discovers and registers on‑prem and cloud resources, enforces policy-driven routing (local inference when required), provides immutable audit trails, and ships connectors/SDKs for common enterprise systems (Active Directory, SharePoint, Confluence, private APIs) along with a zero‑trust inference gateway. Timing is favorable: a roughly $9.0B SAM (3M businesses × $3K ACV), accelerating hybrid AI adoption, tightening privacy regulations, and practical advances in efficient models make on‑prem inference both cost‑effective and operationally feasible. This space has medium competition, so to stand out prioritize provable compliance (SOC 2/ISO, exportable audit logs), policy-first routing, low‑latency connectors, and an open integration model that reduces lock‑in while enabling a partner ecosystem. Strengths include clear market demand and attractive $3K ACV unit economics, while realistic challenges are heterogeneous integrations, multi‑quarter sales cycles, and the engineering effort to maintain secure, low‑latency local inference; start by targeting regulated verticals and modular connectors to de‑risk execution and validate ROI.