Show your clients what AI-assisted delivery actually delivers.
Clients ask the same question your engineers do
Every client now asks whether AI makes your team faster and cheaper. "Yes" without numbers is a discount waiting to happen. The research says output rises and review queues, pull-request size and incidents rise with it — Faros AI measured 98% more pull requests and 91% longer review time in high-adoption teams, and DORA 2025 ties AI adoption to higher delivery instability.
What wins the conversation is your own data: lead time per project, change failure rate per service, and how much of each sprint was feature work rather than rework.
Flow: per project, per team
Lead time for changes and deployment frequency per repository and per team — which projects flow and which wait, and where: review, CI, handovers to the client's side. The sprint report arrives every sprint without anyone compiling it.
Quality gates: the numbers an SLA is written in
Change failure rate, MTTR and Error Budgets per service — the vocabulary of a client SLA, measured from your deploys and monitoring rather than asserted. Leaks Finder catches the secrets a generated snippet pastes into a client repository.
Features versus maintenance, sprint by sprint
Sprint Insights classifies every ticket as feature or non-feature work, so the hours a client pays for are visible as features shipped rather than tickets closed. Kudos keeps recognition visible across projects and teams.
How it works across your projects
No new process for your teams, nothing for the client to fill in.
Connect
Access tokens you create and can revoke, for GitHub, GitLab or Bitbucket, Jira and your CI — per project, so a client's repositories stay their own.
Baseline
The first sprint gives each project its starting point: lead time, deployment frequency, change failure rate, the feature-to-maintenance ratio.
Report
A sprint report per team at the end of every sprint. What you share with a client, and how, is your call.
Scrum Alliance
We thought our bottleneck was deployment speed. It turned out to be handovers.
Questions software companies ask us
Can we run it per client or per project?
Yes. Repositories, Jira projects and services are grouped into teams, and every metric is measured and reported per team. A client project can be its own team.
Does it rank our developers?
No. Everything is measured on the system — a team, a service, a repository — never on an individual. That is what keeps engineers on board and what makes the numbers defensible in front of a client.
Does it measure what the AI tools cost?
Not yet. Today it measures what AI-assisted development does to delivery: lead time, change failure rate, Error Budgets, the feature-to-maintenance ratio. Cost per team next to those numbers is on the roadmap, and it will not appear on a page before the product measures it.
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