237K+ AI events recorded on our own fleet · last 30 days AI events on our own fleet · 30d
Security & governancePut your AI policies into practice.EngineeringGive developers AI tools with clear rules.LeadershipAdoption, ROI and risk in one view.
Works with Cursor · Claude Code · ChatGPT · GitHub CopilotExplore the platform →
Our approachObserve first, then set rules.Why we built itA note from our founder.InsightureThe team behind Tracelet.
DESIGN PARTNERSShape Tracelet with us.Three months, hands-on, in your cloud. →
SecurityHow Tracelet keeps data in your cloud.Shadow AIHow exposed is your organisation?Real-world incidentsWhen AI actions go wrong.BlogPerspectives on governing AI.FAQAnswers to common questions.

Engineering

Give developers AI tools with clear rules.

One shared policy model for your coding tools, so developers know what is allowed and platform teams see what needs attention.

Become a design partner See the rollout steps

From the first machine to every team.

Enrol, agree the rules, then expand in stages. Check tool coverage before you plan the first one.

  1. Enrol machines, see coverage.

    Find out which tools, MCP servers and actions are covered for each team.

  2. Agree the rules, then publish them.

    Start with file, shell and MCP policies. Publish the ones that work to every repository they apply to.

  3. Roll out one team at a time.

    Decision records, fleet health and delivery metrics show where to expand next.

Delivery metrics

Delivery and AI use, on one timeline.

Velocity, pull requests, CI and release health from your GitHub history, with AI activity on the same axis. See whether AI use tracks with delivery.

  • Prove AI's impactTie AI adoption to real delivery outcomes.
  • No extra setupWorks from the GitHub history you already have.
TraceletEngineering / Insights

Engineering Insights

Delivery health from ingested GitHub history, with AI coding activity on the same time axis.

Deployment frequency

deploys / week
14/week+3 per week vs prior 12 weeks

Merges to the default branch counted as deploys, 12-week average.

01020W2W4W6W8W10W12

Lead time for changes

hours, opened to merged
31h−6 h vs prior 12 weeks

Average hours from pull request opened to merged.

0h20h40hW2W4W6W8W10W12
AI Coding Activity — the “AI based” overlay
AI coding hours Deployments per week
0 h090 h5180 h10270 h15360 h20W1W2W3W4W5W6W7W8W9W10W11W1200905180102701536020W2W4W6W8W10W12
Does AI activity track delivery?Read both series on one time axis before deciding.
180 → 348 hWeekly AI coding hours, week 1 to week 12
10 → 19Deployments per week over the same period
168 deploys412 pull requests6 repositories12 weeksGitHub history + AI activity

Developer metrics

Team health, not individual rankings.

Usage is grouped into bands and reported only as counts per team.

  • Coach teams, not peopleSpot where a team needs support.
  • Privacy built inSmall teams are hidden so no one is singled out.
  • Transparent by designDevelopers know exactly what is measured.
TraceletEngineering / Developers

Developer bands

57 active developers · bands recomputed nightly from the last 30 days.

High18Top third by score
Mid31Middle third by score
Low8Bottom third by score
Median keep rate71%Fleet median
Sessions9/ wkMedian per developer
Fleet distributionAggregate only
High1832%

Keep rate above 75%, eight or more sessions a week, deep feature use.

Mid3154%

Steady sessions with keep rate around the fleet median.

Low814%

Few sessions or a low keep rate in this period.

Movement vs prior 30 days: +4 into High · −2 out of Low

By team
TeamDevsDistributionHighMidLow
Platform engineering248133
Backend services186102
Developer experience9351
Data platform6132
What feeds the banding
Keep rate

Share of AI-suggested code still present at merge.

71%Fleet median
Session frequency

Active AI coding sessions per developer, per week.

9 / wkFleet median
Feature depth

Distinct capabilities used in a session: edits, tests, MCP, review.

4 of 7Fleet median

Aggregate by design. Bands are computed per developer but never listed by name in reports, and any group smaller than five developers is suppressed.

57 developers4 teamsMinimum group size 5No individual rankings

Standards rollouts

Publish once. Roll out everywhere.

Keep your recommended skills, rules and MCP servers in one catalog. Roll them out to every repository as tracked pull requests.

  • Consistent setupsEvery repo gets the same approved tools.
  • Nothing slips throughKnow which repos are still behind.
  • Faster new projectsNew repos start with your standards on day one.
TraceletEngineering / Standards

Standards

Org-recommended skills, rules, tools and MCP servers, and where each one has been pushed.

NameKindDistributionScopeRequiredCreated
Repository context before editInstructionPushEveryone RequiredPlatform engineering · 3 wk ago
Structured commit messagesInstructionPullBackend servicesOptionalBackend services · 2 wk ago
Test-first refactorSkillPullBackend servicesOptionalBackend services · 5 d ago
Rollout · batch #14Repository context before editStandardize from acme/payments-api to 5 repositories
Tracking

4 of 5 pull requests opened · 1 merged · 1 pending

RepositoryState
acme/payments-apiSource
acme/checkout-webMerged
acme/ledger-servicePR #88 opened
acme/notificationsPR #231 opened
acme/billing-workerPR #57 opened
acme/identity-gatewayPending
Clone Standards Pack
Platform baseline

Clone the whole bundle onto a new repository as one tracked batch.

6 standardsacme/new-service
23 standards4 packsBatch #14 · 4 of 5 PRs openedTracked per repository

Give AI room to work. Keep your business in control.

Tell us about your team, the tools you use, and the work you want to protect.

Let’s talk