Executive Summary

Engineering teams adopting LLM code assistants currently face uncontrolled, non-auditable changes and increased security/compliance risk, and platform/security owners struggle to set and enforce consistent policies across IDEs, PRs, and CI. As LLM usage scales across roughly 3.0M engineering teams, these gaps create frequent policy violations, data leakage exposures, and audit gaps that are expensive to remediate. Build a developer-facing enforcement layer that attaches runtime (IDE/pre-commit) and CI hooks to intercept LLM suggestions, apply context-aware policies via retrieval-augmented policy stores, block or rewrite unsafe outputs, and emit immutable audit logs. The product would include SDKs for common toolchains, a policy authoring UI, vector-backed policy retrieval for up-to-date constraints, and metrics/alerting for compliance teams. The market is attractive now — estimated TAM of $6.0B (3.0M teams × $2K ACV) with accelerating LLM adoption and increasing security/compliance budgets creating near-term buying interest. You can differentiate by enforcing policies at both query-time (via RAG/vector stores) and in CI/runtime hooks for end-to-end prevention rather than post-hoc detection, while optimizing for low-latency developer UX and turnkey integrations. Major challenges are instrumenting diverse toolchains and proving measurable ROI to conservative security teams, but if you can demonstrate clear reductions in policy incidents and minimal developer friction this idea has strong commercial potential.

Market Opportunity

Enforceable AI code assistance via runtime and CI hooks targets a $6.0B = 3.0M engineering teams × $2K ACV total addressable market with medium saturation and a year-over-year growth rate of 35% YoY — developer tooling and AI-assisted dev market expansion (sources: GitHub State of the Octoverse, IDC trends on dev tools and AI).

Key trends driving demand: LLM adoption in dev teams is accelerating — this creates demand for controls and governance to scale usage safely.; Retrieval-augmented generation and vector stores enable persistent, relevant policy injection at query time.; Security/compliance scrutiny of AI outputs is increasing — companies are investing in auditable pipelines.; Shift to cloud-native CI/CD and API-first tooling makes inserting enforcement hooks into workflows easier and more standardized..

Key competitors include GitHub Copilot / Copilot for Business, Sourcegraph Cody, Snyk (Snyk Code / Snyk IaC).

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Enforceable AI code assistance via runtime and CI hooks

Executive Summary

Market Validation

Market Opportunity

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