"Looks right" and "is right for your codebase" are two different things
The AI has never seen your codebase the way you have. It writes the most average version of any piece of code, the one that would look at home in a million other repos. But your codebase is not a million other repos. It has a grain: a way you handle errors, a way you save then publish, a wrapper you always go through, a shape your data always moves in.
None of that is written down. It lives only in the code itself, the way you do things here. The AI does not know it, so it quietly stops doing it, and each change sands the grain off until your codebase reads like everyone else's. We call this de-cohesion: technical debt you pay as interest on every future change.
Nothing you already have catches it
De-cohesion is not bad code. It is valid code that just is not your code, so your tests, your linter, and your type checker wave it straight through.
Why does everything else miss it?
Linters and scanners
enforce universal rules. There is no universal rule that says "in this repo, save is always followed by publish." It is your convention, so they cannot know it.
AI code reviewers
give you a different answer every run, cost a model call per check, and ship your code off your machine to do it.
The AI itself
never saw your codebase, and it navigates by searching small slices of context, so it walks right past the off-pattern spot.
You
can catch it, by reading every diff, carefully, forever.
The real cost is not a bug. It is the stall.
AI handed you 100x the output, and then quietly took away your confidence to ship it. Every change now needs a human to check whether it fits, and that check is the slow part. So you merge fast and blind and the debt piles up, or slow to a crawl reviewing by hand and lose the speed.
That is the stall. The speed is fake if you cannot trust the merge.
What doloop does: it gives you merge confidence
doloop reads your codebase and learns the way you do things, not from a rulebook, from your own code. It folds the things that decide whether a merge is safe, consistency, architecture, flow, security, and performance, into one verdict, and does the boring codebase-review for you. Your attention goes to the handful of changes that actually need a human.
Merge confidence: step forward, do not stall.
It helps you understand the code read
Open a file or a whole repo you have never seen and read it like pseudocode in a minute: where the doors are, where the real logic lives, what leans on what. The unfamiliar becomes familiar fast.
It tells you what to trust gate
For any change, doloop scores it across five lenses and returns one verdict: green to merge, yellow to glance at, red to read. Security is red anywhere; blast radius escalates the rest.
How it is built, and why that matters to you
It runs on your machine
Your code never leaves. No upload, no third-party model reading your source. For the teams who need this most and cannot send code to a SaaS reviewer, it is the difference between no and yes.
Same answer every time
Run it twice, run it in CI, run it in front of an auditor: byte for byte identical. A verdict you can trust and replay, not a mood.
Bring your own model
doloop's core checks are deterministic counting, not AI guesswork. When a model helps, it is your model, your choice.
The smarts stay yours
doloop does not think for you. You stay the senior engineer in command of the 100x the AI is shipping, not a rubber stamp.
One job, done all the way
doloop has one job: it holds your AI's code to your codebase's own law. It is a consistency check, not a quality grader and not a debt scorer, so the judgment of what is good stays yours. It reads structure, the skeleton you navigate by, not a function's intent. And it owns the coherence slice of technical debt, the part nothing else can see, all the way down.
Deterministic, on your machine, the same verdict every time. One real thing, done completely, so the verdict is something you can stake a merge on.
We get you. We got you. We keep you.
AI made writing code cheap. doloop makes trusting it cheap, so you can actually move at the speed the AI promised, without waking up in a codebase that no longer feels like yours.
Read we get you
understands your codebase and makes it understandable in a minute.
Gate we got you
protects it, holding every AI change to your codebase's own law.
Memory we keep you
the conventions you have earned are remembered, so the next change, and the next AI, builds on them instead of eroding them.
Try it on your own repo, and find out what it already knows about you.