Your team ships PRs faster than anyone can review them.
AI made writing code fast. It did nothing for reviewing it. Teams get speed in week one — and lose control silently in the weeks after. The reviewer’s real question, “what else does this change break?”, is answered by nobody: not the AI that wrote it, and often not the reviewer either.
Your old quality system was never a process. It was people.
Every handoff between two humans was a hidden checkpoint. Agents absorbed the work, the roles collapsed into one person directing agents — and the checkpoints disappeared with them.
Forced a spec conversation
Handing work to a developer meant explaining what you actually wanted. Requirements got written down because a second person needed them.
Forced a second pair of eyes
Handing code to a reviewer meant someone else read it before it shipped. Not perfect — but a real check, on every change.
Both checkpoints gone
Now one person directs agents through the whole loop. No spec conversation, no second pair of eyes — and code volume went up, not down.
And it gets worse from here: the supply of people who can do old-style deep review is shrinking. The next generation of builders enters without ever learning to read code deeply.
Review broke first. It’s measurable.
Not our numbers — the industry’s. Sources on every card.
Median review duration
Where review capacity collapses under the volume, per-developer defect rates jump from 9% to 54% — and zero-review merges are up 31.3%: code ships with no one looking at all.
missed agent-inserted sabotage
Of developers reviewing code with deliberately inserted sabotage, 94% missed it — and 56% accepted it even after a monitor flagged it.
“almost right, but not quite”
Two thirds of developers name AI solutions that are almost right, but not quite, as their top frustration. That’s exactly the code that slips through a tired review.
say debugging AI code takes longer
The time saved writing comes back later — as debugging code nobody on the team wrote or fully read.
“I can't possibly review this volume of code.”
The reviewer asks “what else does this break?” Timo answers it.
Today nobody answers that question — it lives in the heads of your two most senior engineers, if it lives anywhere. Timo computes the answer, deterministically, on every PR. The machine checks first. A human judges where it matters.
Intent summary
What the change claims to do, in plain words, next to what was asked for. The reviewer starts from the question, not from a cold 500-line diff.
Computed blast radius
Every file, endpoint, and job the change can reach — computed from a verified map of your codebase, not guessed by another model.
Evidence table
The tests that ran, what they prove, and what they don't. Gaps are shown, not hidden.
Change Passport
Every merge leaves a record: why the change exists, who approved it, what it touches, and how to roll it back.
The gate gives reviewers time back, not paperwork. Pre-verified PRs merge faster— the reviewer’s clock goes down.
The blast radius is only as good as the map behind it — that map is Timo Memory, committed to your repos with every claim cited to file:line or flagged.
One repo. No commitment. Nothing leaves your network.
1 · Scan a repo
Run the Agent-Readiness Scan on one repo. Free and read-only — it tells you where review is weakest and what agents will get wrong there. See the Timo CLI →
2 · Install as a PR
Timo installs memory, docs, and the gate configuration as a pull request your team reviews— the same discipline we’re selling, applied to ourselves.
3 · The gate goes into CI
Every PR after that arrives with an intent summary, a blast radius, and an evidence table. Reviewers judge; the machine does the reading.
Start with the repo your reviewers dread.
The scan is free and read-only. You'll know in five minutes where review is thinnest.