01
Measure
Set the baseline before choosing the work.
Connect delivery history and establish the quality, flow, and outcome measures the feature is meant to change.
- Signal
- Checkout completion is falling
- Baseline
- 61% completion
- Target
- At least 70%
Product tour · 4-minute walkthrough
Turn customer signal into shipped outcomes- and prove what changed.
FeatureFactory connects feedback, planning, agent execution, and engineering measurement in one auditable loop. Your team keeps its tools, its review process, and its IP.
Read-only measurement Source-cited plans Customer-owned output
One connected record
Signal → theme → product plan → work order → scored PR → outcome
The product loop
Follow the evidence through the actual product.
Representative product data shows how the command center, planning evidence, and controlled execution remain connected. Open any view at full size to inspect it.
The missing context
Shipping is only half the job.
Coding agents make output easier. They do not tell you what deserves to be built, whether the generated change is durable, or if the customer problem improved. FeatureFactory keeps those questions attached to the work from the first signal through the post-release result.
One problem, end to end
Follow a checkout problem through the full loop.
Every stage below uses the same example, so you can see how context compounds instead of disappearing at each handoff.
Illustrative workflow · all quantities are sample data, not customer results
02
Plan
Turn scattered evidence into build-ready intent.
Related support, product, and delivery signals become a cited theme and a product plan with testable acceptance criteria.
- Theme
- Onboarding checkout friction
- Evidence
- 28 linked signals
- Artifact
- Product plan with 4 acceptance criteria
03
Build
Give agents a work order, not a vague prompt.
The product plan becomes scoped work with repo context, explicit checks, and a reviewable trail from intent to pull request.
- Work order
- Checkout recovery flow
- Execution
- Agent + engineer review
- Artifact
- Scored pull request
04
Measure again
Close the loop on the result.
Compare the shipped change with the baseline: did quality hold, did delivery improve, and did the intended outcome move?
- Quality
- PR score + rationale
- Durability
- Rework after merge
- Outcome
- Checkout completion trend
What each stage delivers
Every handoff carries the evidence forward.
The value is not three more dashboards. It is one traceable record of why the work exists, what the agent changed, and whether it held up.
- Inputs
- Pull requests, commits, reviews, releases, and authorship signals.
- FeatureFactory does
- Score quality and effort, compare AI-assisted and human work, and track flow over time.
- You get
- An auditable baseline for quality, velocity, cycle time, and rework.
- Inputs
- Jira, Linear, Slack, support conversations, CRM activity, and calls.
- FeatureFactory does
- Extract insights, group recurring themes, preserve sources, and draft requirements.
- You get
- A source-cited product plan with acceptance criteria an engineer or agent can execute.
- Inputs
- Approved product plans, repo context, guardrails, and your existing coding agents.
- FeatureFactory does
- Create scoped work orders, execute, review the diff, and retain the full decision trail.
- You get
- Customer-owned code that flows directly back into measurement.
The proof layer
Answers for the decisions you actually make.
A useful metric changes what you do next. FeatureFactory puts quality, speed, authorship, and product outcomes in the same decision frame.
Where should we use agents?
Compare quality, review effort, cycle time, and rework by authorship instead of treating adoption as the result.
Did speed cost us quality?
Read delivery flow and durability together, so a faster merge that creates rework does not look like a win.
Did the feature move the outcome?
Keep the original problem and success measure attached to the shipped work, then compare it with the baseline.
Checkout recovery · post-merge
Decision view
- PR quality
- 84/100
- Cycle time
- 1d 8h
- 30-day rework
- 7%
- Checkout completion
- 61% → 72%
Rationale available
Intent to merged PR
Durability check
Target reached
Proof you can inspect
Trust the trail, not a black box.
The strongest proof is the evidence your own team can reproduce. Methodology, attribution confidence, and ownership rules are visible before you buy.
Transparent scoring
See the rubric behind PR quality, effort, and written rationale. Scores are evidence you can inspect, not a mystery grade.
Read the scoring methodologyHonest AI attribution
Authorship is labeled from explicit signals and shown with confidence, so estimates are never presented as ground truth.
Read the attribution approachCustomer-owned execution
Run work from your own machine, keep code in your repositories, and retain the artifacts if you ever leave.
Explore the Local RunnerWorks with your stack
Connect the work before replacing it.
FeatureFactory sits across the systems where customer context, delivery decisions, and code already live. Start with GitHub measurement, then connect more of the loop as your team is ready.
Browse all integrationsPlan from signal
JiraLinearSlackIntercomZendeskHubSpotFireflies
Build and measure
GitHubClaude CodeCursorYour own agents
Before you connect
Questions teams ask on the tour.
FeatureFactory is an outcome loop for software teams. It connects customer and delivery signal, turns that evidence into build-ready plans, orchestrates agent work, and measures what ships.
See it on your own work
Bring one repo. Leave with a baseline.
Join early access to connect your delivery history, see how measurement works, and map the rest of the loop around your existing stack.
Read-only to startNo replacement rolloutYour IP stays yours