We built baselane because good AI code depends on your repo and your team sharing a standard, no matter which model you use. Almost none do.
Every team now ships AI-written code. Very few have made their team write it well, or consistently. That gap is the whole reason baselane exists.
An agent fails in a repo that never tells it how to build, test, and conform, no matter how good the model is. Give it that context and the same model writes code that fits. The standard matters more than the model.
Today every developer reinvents their own prompts, rules and agent configs. Five hundred developers, five hundred setups. The org ends up with uneven, hard-to-review AI code and no way to roll anything out. Meanwhile the tooling landscape shifts every month, and no leader can track it.
Generating config is already an open, commoditizing problem. The hard parts are deciding what fits this codebase, distributing and governing it across every repo and developer, and continuously curating a fast-moving field so a bad practice never lands everywhere at once. We build on the open ecosystem and compete on those three. We hold ourselves to the same openness: the CLI, pack engine, and renderers are Apache-2.0 on GitHub — the org control plane is what we sell.
The industry's first instinct was to measure AI by tokens spent. That's the wrong number. baselane tracks what actually happened: what shipped, what got reviewed, what got caught before production. That's what your team should be judged on.
Standards arrive as pull requests your team reviews. Never silent, never mandatory at the machine level.
Everything we produce is plain files in your repos, rendered by an open-source engine, across every tool — not locked into a walled garden.
Curation is editorial. We'd rather ship one practice that fits than ten that look impressive.
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