How Unyform thinks about alignment

When people say "AI alignment" they usually mean something cosmic — keeping a superintelligence from going rogue. Unyform means something narrower and far more immediate: does the code an AI just generated match how your team actually builds?

That is the alignment problem every engineering org already has. A model writes plausible code that uses the wrong HTTP client, invents an error pattern you abandoned two years ago, or reaches across a boundary your architecture forbids. It compiles. It passes review on a tired afternoon. It drifts your codebase a little further from itself. Multiply that by every developer and every agent, running in milliseconds, and you get entropy at machine speed.

Here is how we think about closing that gap.

The codebase is the spec

Most "alignment" tooling points the model at documents — a style guide, a wiki page, an ADR someone wrote in 2023. Those describe an intent that may no longer exist. The code is what the team really does. So Unyform treats the repository itself as the source of truth: it mines the patterns you actually follow, the libraries you actually reach for, the architecture you actually ship.

We call those mined patterns idioms, and the analyzed profile of a repo a blueprint. Neither is hand-authored. Both are extracted from reality and re-derived as the codebase moves. Alignment to documents rots the moment the documents go stale. Alignment to the codebase can't, because the codebase is the measurement.

Enforcement has to live in the request path

Advice that arrives after the fact is just review with extra steps. If alignment shows up in a linter run, a CI comment, or a nightly report, the misaligned code is already written, already merged, already part of the problem you're now paying to undo.

So enforcement belongs where generation happens: in the request path, before the developer ever sees the output. Unyform's gateway sits between your AI tools and the model provider. On every request it injects the relevant blueprint and the most relevant existing code, runs policies, and forwards upstream with your own key. The model is steered toward your reality at the one moment that matters — and the moment it produces something out of bounds, that gets caught inline, not in a postmortem.

Runtime, not advice. That distinction is the whole product.

Evidence over vibes

"Our AI follows our standards" is a claim. Unyform's job is to make it a measurement. We don't ask you to trust that alignment is happening; we show it.

• Adoption — what percentage of your code actually follows each idiom, so you know which conventions are real and which are wishful.
• Drift — the specific edges where new code diverges from the established pattern, surfaced as drift rather than buried in a diff.
• Audit — every governed request logged with what context was injected, which policies ran, and what happened, so alignment is reconstructable after the fact, not asserted.

Vibes don't survive an enterprise review. Numbers and an audit trail do.

The principles, and what embodies them

Each idea above maps to a concrete mechanism. None of this is philosophy for its own sake — it's the reason each piece exists.

Alignment, to Unyform, is not a value you write down. It's a property you can measure, enforce in the moment, and prove on demand — grounded in the only artifact that never lies about how your team builds: the code itself.