Executive Summary

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.

Market Opportunity

Keep AI copilots local by registering local/company resources for private tasking targets a $9.0B = 3M businesses × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% YoY (market for enterprise AI tooling and hybrid AI deployments — Gartner, 2024).

Key trends driving demand: Hybrid AI adoption — enterprises are adopting hybrid cloud patterns that mix cloud and on-prem inference, creating demand for orchestration tools that route workloads appropriately.; Privacy and compliance pressure — regulatory scrutiny and internal privacy programs push companies to keep certain data and inference local, increasing demand for private copilots.; Edge and efficient models — model optimizations and smaller LLMs make on-prem inference feasible for many tasks that previously required large cloud models.; Rising cloud inference costs — as usage grows, companies are motivated to offload some inference to cheaper local resources to control costs..

Key competitors include Microsoft Copilot (cloud-first), OpenAI / ChatGPT Enterprise, Hugging Face (private model hosting and inference).

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

Executive Summary

Market Validation

Market Opportunity

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