01
Connect
Choose the systems that hold delivery work and customer context.
- Input
- GitHub plus Jira, Linear, Slack, support, CRM, or calls as needed.
- Output
- A connected source layer with ownership and provenance intact.
How it works · system mechanics
From connected systems to a measured outcome-in six controlled steps.
Connect the work you already do, establish a baseline, turn evidence into a plan, execute with agents under review, and measure what happens after merge.
Connect
Control
Measure
The operating sequence
Each step produces the input for the next.
Context should accumulate through the workflow. It should not be rewritten from memory every time work crosses a tool or team boundary.
02
Normalize
Map records from different tools into a consistent product and delivery model.
- Input
- Pull requests, work items, conversations, accounts, releases, and identities.
- Output
- One traceable record without forcing teams into one replacement tool.
03
Baseline
Measure the current quality, flow, rework, and authorship picture.
- Input
- Repository history, reviews, releases, and explicit AI-attribution signals.
- Output
- A starting point for deciding what “better” means on your own work.
04
Plan
Turn recurring customer and delivery evidence into an approved specification.
- Input
- Cited insights, themes, related work, constraints, and success measures.
- Output
- A build-ready product plan with testable acceptance criteria.
05
Build
Convert the plan into scoped work and route it through controlled execution.
- Input
- Approved product plan, repository context, guardrails, agent, tests, and review gates.
- Output
- Customer-owned code with a visible trail from intent to pull request.
06
Close the loop
Measure the shipped change against the baseline and intended outcome.
- Input
- Merged change, release, rework, quality rationale, and outcome trend.
- Output
- Evidence for whether to expand, revise, revert, or learn.
Clear responsibility
The layer connects your stack; it does not swallow it.
Your existing tools
Remain the systems where teams talk to customers, plan work, write code, review, and ship.
Original records and workflows stay in place.
FeatureFactory
Connects context across those systems and keeps the intent-to-outcome trail coherent.
A normalized, auditable outcome loop.
Your team
Owns prioritization, architecture, review, sensitive decisions, and the definition of success.
Human judgment remains explicit at every gate.
Control points
Automation does the repetition. People keep the judgment.
The workflow makes ownership explicit instead of hiding a human decision inside an autonomous-looking run.
Read-only measurement
GitHub measurement starts with read-only repository, pull request, commit, review, and contributor data.
Approval before execution
A generated plan or work order is a draft until an accountable owner approves the scope and criteria.
Repository controls remain
Tests, branch protection, human review, and merge authority continue to govern what ships.
Every score is inspectable
Quality and attribution views retain a rationale, underlying signals, and uncertainty instead of presenting a black-box verdict.
Inspect the mechanics
The proof stays in the workflow.
Scoring methodology
Inspect the rubric and rationale behind PR-quality measurement.
Read the methodology →AI attribution
See how known, estimated, and unknown authorship signals are handled.
Read the attribution approach →Local execution
Understand how work orders can run inside a developer-controlled environment.
Read Local Runner docs →Before you connect
Questions about the operating model.
Most teams start with GitHub to establish a delivery baseline, then add the customer or planning source that holds the strongest context for their next decision.
See it on your own work
Start with the part of the loop you can measure today.
Join early access to connect one repository, establish a baseline, and map the next source or workflow around evidence from your own team.
Read-only to startNo replacement rolloutYour IP stays yours