Know your codebase is sound, auditable, and governable, whoever wrote it, human or AI.
AI makes delivery fast. It does not tell you the result is safe to own, to audit, or to buy. We give you an independent, evidence-based read of your code, and the guardrails that keep it that way. Assurance, not assertion.
Whether you build the software or you are about to buy it, the question is the same: can you trust what is in the repository.
Start with a free readiness scorecard, then a Readiness Assessment: a scored report, the risks an AI assistant will amplify in your code today, and a prioritized roadmap. Remediation installs the guardrails in place, so your engineers do not have to retrain to keep the standard.
Before you commit capital, an independent, committee-ready read of a target's codebase, legacy or AI-built: an asset, conditional, or liability verdict, ranked findings tied to the code, and the cost to bring it to a safe standard. Under NDA, on a fixed timeline.
"Everyone feels like a systems engineer capable of building things now, but the truth is it gets us into trouble, especially at publish time."
"A non-technical analyst on the team built an audit agent for their CRM from what he learned, documented it, and presented it to leadership. They called it complete."
The method behind the assessments is published, and you can inspect it. Write the specification as a contract, derive the code from it, and keep the guarantee outside the model. A buyer who cannot verify us can verify the method.
The paper tree (a base paper and focused derivatives), the formulas with the status each has earned, the pre-registered experiments and their raw evidence, and the GS Core course. Open access, with a DOI.
Free tools for individual developers. Each one addresses a specific failure mode of AI-assisted development.
Portable quality gates, one per property, wired to standard tools: mutation, real coverage, complexity, duplication, dead code. Hand it to your AI and it cables the gates into your project straight from the spec. The ratchet only goes up.
Quality gates →Graph-powered hybrid search for your codebase. Vector + BM25 + path, fused with RRF. Semantic search, dependency traversal, and contextual file reads — the AI reads your codebase the way you do, not just grep.
GitHubThree-tier persistent memory for AI sessions. Buffer, Working, and Core layers with Ebbinghaus decay and an intelligence distillation layer. Cross-project knowledge that survives context boundaries.
GitHubTwo days. Your team. Your codebase. A different speed.
Point AI at a codebase and you get plausible output fast, then pay for it in review and rework. The Forge teaches the discipline that makes the output correct enough to trust: spec-driven development, clean architecture, and directing AI against a spec instead of prompting into the dark. Two days, on your actual codebase, with your team.
Book a ConversationLong-form writing on specification, AI-assisted development, and what it means to engineer in an age of stateless readers.
ambientengineer.dev The Andon Method →Workshop inquiries, research questions, collaboration.