Engineering intelligence

Uncover AI's impact on velocity & code quality.

Scryable measures the real impact of your AI tools — on velocity, code quality, and the engineers using them.

Get started free See how it works

One repo free forever · No credit card required

The scrying glass — 30-day view

91%

of engineering teams are investing in AI tools right now

22%

have a formal, documented AI strategy for engineering

1.3%

of velocity variance is explained by AI adoption rates alone

43%

of engineering leaders disappointed by AI output quality and code bloat

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.

For the first time, you'll have the data to turn AI adoption into a deliberate strategy. Not gut feel. Not licence counts. The actual picture, from your actual codebase.

scryable · api-service · last 90 days

live

The adoption gap

Having the tools is almost entirely uncorrelated with getting results.

91% of engineering teams have AI tools. Only 22% have a formal strategy. The gap between those two numbers is where the value disappears.

The teams winning with AI are the ones measuring it — tracking velocity before and after, watching for churn, and adjusting based on what they find. Scryable gives you the baseline to do that.

40–50%

higher velocity in teams that treat AI as a strategic priority vs. an operational add-on

+18%

typical increase in commit volume in the first 60 days after AI tool adoption

1.38×

average churn ratio increase when AI adoption goes unmeasured and unchecked

How it works

Three steps. No integrations required.

STEP 01

Point it at your repos.

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

github.com gitlab.com

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.

~minutes for 12 months

STEP 03

Get a clear picture.

The dashboard shows velocity, quality signals, churn, per-author breakdowns, and the before/after comparison against your pre-AI baseline.

no jargon no verdicts just data

What you get

Everything you need to see what's actually happening.

Built for engineering leaders who want real data, not dashboards that flatter.

Core · Velocity

Before and after your AI baseline.

The single most important number in AI ROI: what did velocity look like before your tools, and what does it look like now? Scryable establishes the baseline automatically.

Learn about velocity metrics

Signal · Quality

Code churn, rework, and duplicate detection.

Speed without quality is just faster debt accumulation. Track churn ratios, rework rates, and the duplicate code signals that tell you whether AI output is landing cleanly.

Read about code quality signals

Signal · Adoption

Who's using AI, and how.

Per-author breakdowns show you where AI-assisted output is concentrated, who's adapting well, and where coaching or tooling changes might help.

Explore adoption patterns

Core · Reporting

Built for engineering leaders and their stakeholders.

Clear visualisations, shareable views, and plain-language summaries you can put in front of a CTO, a board, or a sceptical VP of Engineering without translation.

See the docs

Get started

The ROI on AI coding tools

has always been unknowable.

Until now. Connect your repos and see what your AI tools are actually doing to your code. Free to start, no credit card required.

Get started free

One user free forever·No contracts·No integrations required