One view every team can trust
Bring planning, delivery, and quality data together so every team works from the same definitions instead of a dozen conflicting dashboards.
For Platform Teams
Standardize developer velocity without standardizing every workflow
Give teams transparent definitions for flow, quality, rework, and AI-assisted delivery while preserving the tools and practices that already work.
Start with one repository. Keep your tools, workflow, and code ownership.
What you will see
A low-friction platform pilot
Use one representative repository to validate metric definitions, historical accuracy, and team usefulness before expanding across the engineering portfolio.
One view every team can trust
Bring planning, delivery, and quality data together so every team works from the same definitions instead of a dozen conflicting dashboards.
A shared quality baseline
Publish a transparent, org-wide quality baseline for cycle time, review depth and change failure that any team can measure themselves against.
Connect without changing how teams work
Connect repositories and CI with read-only access. Teams keep their existing tools, code, and workflows.
Read-only GitHub access
Choose exactly which repo
No credit card required
Built for your operating questions
Turn delivery data into a decision you can make this week
A shared quality baseline
Publish a transparent, org-wide quality baseline for cycle time, review depth and change failure that any team can measure themselves against.
Connect without changing how teams work
Connect repositories and CI with read-only access. Teams keep their existing tools, code, and workflows.
Guardrails, not gatekeeping
Set baseline thresholds that surface drift and outliers to teams early, without turning platform into the bottleneck on every merge.
Portfolio rollups
Aggregate from PR to team to portfolio, then drill straight back down to the exact change that moved a metric.
AI code, measured honestly
Track how much shipped code is AI-generated and whether it holds the same quality bar as human-authored changes.
From repo to baseline
Useful signal without a rollout project
- 01
Pilot with one repository
Connect GitHub read-only and choose a representative service without requiring developers to change their local workflow.
- 02
Validate shared definitions
Inspect transparent cycle time, review, rework, quality, and AI attribution signals against delivery history your teams already know.
- 03
Expand from a trusted baseline
Roll the same definitions across teams only after the pilot proves useful, then compare trends without forcing every team into one process.
Developer velocity
Create one trustworthy velocity language across teams
Platform teams need consistent measurement without a central process bottleneck. FeatureFactory gives every team the same durable-throughput model while preserving repository-level context.
Explore developer velocity- Standardize the definition of durable delivery across repositories and teams.
- Let teams drill from portfolio trends back to the pull requests behind them.
- Measure AI-assisted throughput against the same quality and rework baseline.
See the full delivery picture, not another isolated dashboard
Most platform teams inherit a patchwork of dashboards - one for CI, one for incidents, a spreadsheet for cycle time - and none of them agree on what "good" looks like. FeatureFactory brings planning, building, and shipping data into one consistent view, so teams can compare results without debating definitions.
Connect repositories and CI with read-only access to cover every repository without asking teams to install software or rewrite their workflow. Platform teams can roll out a shared view in days, then let each team explore the engineering metrics that matter to them.
The result is one trusted view of cycle time, review depth, and delivery flow - measured consistently, defined transparently, and easy to trace back to the work.
Give every team a transparent quality baseline
A baseline only works if teams trust it. FeatureFactory publishes an org-wide quality baseline with metric definitions in the open, so a squad can see exactly why their DORA metrics or change-failure rate sit where they do - and what moving them would take.
Guardrails surface drift and outliers early instead of gatekeeping every merge, which keeps platform out of the critical path. Teams get PR quality analytics and benchmarks to compare against peers and against their own past, and leaders get honest portfolio rollups that drill straight down to the change that moved the number.
As AI writes more of the codebase, that baseline becomes the fairest way to answer the hard question: is the code we ship - human or machine - actually getting better? See how we measure AI-generated code.
Questions before you connect
A homegrown dashboard usually stops at four charts. FeatureFactory connects planning, building, review, and release data, then gives every team a shared, transparent baseline to compare against. See our approach to DORA metrics tracking and engineering metrics.
Keep exploring
Validate the platform on one real repository
Start with a representative repo, inspect the baseline with the team, and expand only when the signal earns their trust.
Analyze one repo freeOne repository. No credit card. Read-only access.