Engineering intelligence

Your team is using AI. Is it actually helping?

Most engineering teams adopted Copilot or Cursor months ago. Few have data to show whether it changed anything. Scryable reads your git history and tells you exactly what happened.

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What's actually happening

The truth is in your git history.

Every commit tells a story. Scryable reads them all, surfacing velocity trends, quality signals, and whether your AI tools are producing the outcomes you're paying for.

Most decisions about AI tooling are made on instinct. There is rarely a before-and-after baseline, rarely a way to separate real velocity gains from noise, and rarely a clear picture of what has actually changed in the code. Scryable builds that baseline from your git history and shows you what is there.

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The adoption gap

Most teams adopted AI before they had a way to evaluate it.

Research shows that teams spreading AI across every part of the SDLC tend to have lower velocity than those who focus it in just a couple of areas. More tools, used everywhere, does not mean better outcomes.

The teams winning with AI are the ones who can actually answer the question. They track velocity before and after adoption, watch for churn, and adjust based on what they find. They have data they can bring to a 1:1 without relying on gut feel. Scryable gives you that.

Teams that treat AI as a strategic priority see 40-50% higher velocity than those that treat it as an operational add-on.

Commit volume typically rises +18% in the first 60 days after AI adoption, but average churn ratio rises by 1.38x when that adoption goes unmeasured.

How it works

Three steps. No integrations required.

STEP 01

Point it at your repos.

Connect your GitHub, GitLab or Bitbucket organisation. Scryable reads your commit history — no code execution, no agent, no access to your production systems.

STEP 02

Scryable reads the history.

We analyse every commit: who wrote it, what changed, how it relates to what came before and after. AI-assisted output is identified without requiring any commit metadata.

STEP 03

Know whether AI is helping - and show your work.

The dashboard shows velocity, quality signals, churn, and per-author AI adoption - all compared against your pre-AI baseline. Evidence you can use in a 1:1, a team review, or a budget conversation.

Features