01 / Delivery
Is work moving more effectively?
Look at review turnaround and follow-up work alongside the context behind each change. More activity alone does not prove better engineering.
The Binomial platform
Engineering evidence, connected to your next decision.
A GitHub-connected workspace for understanding delivery patterns, investigating repository friction, and improving how your team works with AI.
The portfolio view
Start wide.
Then follow the evidence.
Review the engineering portfolio and investigate the source context behind a finding. Keep scope, confidence, and freshness in view as you interpret the results.
AI usage and attributed impact remain modeled or unavailable when authoritative provider evidence is absent.
Portfolio
Engineering overview
Example workspace · 3 repositories
Review turnaround12.4h
Selected reporting window
Reviewed changes91
Selected GitHub evidence
Follow-up fixes8%
Investigate with work context
Review turnaround
8-week trend · hoursWeek 1 · 18hWeek 8 · 12.4h
Observed engineering signal in this example
Pattern to investigate
Smaller changes are moving through review faster.
Explore the source context before deciding what to repeat.
Direct AI-tool usage is not included in this example.
ObservedPull requests and review events
ModeledAI attribution remains a hypothesis
- 01
Portfolio context
Start with the selected repository and reporting window.
- 02
Evidence state
Inspect available source evidence and confidence.
- 03
Interpretation
Review modeled conclusions with their limitations.
Patterns worth investigating
Go beyond the
headline metric.
Compare review flow, follow-up work, repository conditions, and source coverage to choose what to investigate next.
Workspace signals3 selected repositories · Last 8 weeks
Example dataReview time by change size
Median hours · 91 pull requests
Compare similar work before changing review practices. Size alone does not explain the difference.
Follow-up work over time
Share of changes with a follow-up fix
12% to 8%Across eight example weeks
View weekly values
| Week | Follow-up fixes |
|---|---|
| Week 1 | 12% |
| Week 2 | 11% |
| Week 3 | 13% |
| Week 4 | 10% |
| Week 5 | 11% |
| Week 6 | 9% |
| Week 7 | 9% |
| Week 8 | 8% |
A follow-up fix can reflect planned iteration or new requirements. This trend does not establish AI impact.
Repository validation conditions
Where to focus a deeper review
platform-core
- Tests
- Mixed
- Docs
- Needs review
- Ownership
- Strong
api-service
- Tests
- Strong
- Docs
- Mixed
- Ownership
- Mixed
data-pipeline
- Tests
- Needs review
- Docs
- Mixed
- Ownership
- Needs review
Example assessment. Confirm tests, documentation, and ownership with the team.
See what the evidence covers
Availability across three selected repositories
GitHub activity does not reveal direct AI usage. Keep missing source coverage visible beside any modeled interpretation.
From activity to understanding
Faster code is only the beginning.
AI changes how work gets done. Binomial helps you examine what happens next: review, rework, codebase readiness, and the practices worth repeating.
02 / Codebases
Where does the friction live?
Investigate repository patterns, ownership, and codebase conditions that make AI-assisted work harder to validate and maintain.
03 / Practices
What should your team repeat?
Turn evidence into better coaching conversations. Try a focused change, then review the next window with your team.
One finding. The full picture.
See the reasoning.
Choose the next move.
Explore three example findings. Each keeps the observations, interpretation, confidence, and limitations beside the recommendation.
Example data
Smaller changes are moving through review faster.
- Observed evidence
- In this example window, smaller pull requests required fewer review cycles than larger changes in the same selected repositories.
- Modeled interpretation
- Focused changes and earlier validation may be helping reviewers. This is a hypothesis to examine with the team, not proof that AI caused the difference.
- Confidence
- Moderate. The pattern is consistent within the selected window.
- Limitations
- Task complexity, work outside GitHub, and direct AI-tool usage are not included.
- Next action
- Try smaller change sets for AI-assisted work and compare the next review window.
Follow-up fixes cluster around large changes.
- Observed evidence
- In this example, several follow-up fixes are linked to larger changes with limited validation evidence.
- Modeled interpretation
- Breaking work into testable increments may reduce avoidable rework.
- Confidence
- Moderate. Source links support the pattern, but work context is incomplete.
- Limitations
- A follow-up fix may reflect changing requirements or planned iteration, rather than a defect.
- Next action
- Agree on acceptance checks before generating code, and make validation visible in review.
Uneven test coverage makes validation harder.
- Observed evidence
- The example repository has inconsistent test signals and sparse documentation around a frequently changed module.
- Modeled interpretation
- Codebase preparation may make AI-assisted changes easier to evaluate and maintain.
- Confidence
- Directional. Additional codebase and team context is needed.
- Limitations
- Workflow signals do not capture all local tests or review practices.
- Next action
- Prioritize focused tests and module documentation, then reassess the affected work.
What stays attached
Enough context to question a finding.
A useful interpretation should be inspectable. Source evidence, selected scope, freshness, confidence, and limitations help you decide what to investigate next.
Portfolio and repository views
Find concentrations of review friction, ownership exposure, and codebase investment needs.
Evidence-linked findings
Understand how the selected engineering signals support an interpretation, and what they leave out.
Coaching conversations
Bring evidence into team conversations and agree on practical experiments. Individual report access remains controlled.
Where to apply the evidence
Start with a useful engineering question.
Choose a review area and start with available GitHub evidence. Agree separately on any additional customer context.
01 / Delivery
Review load and rework
Examine focused changes, review cycles, and follow-up fixes in selected repositories. Ask where faster generation may be creating more validation work.
Inspect an example02 / Resilience
Codebase and ownership constraints
Investigate tests, documentation, ownership, and change patterns around code that is difficult to validate or maintain.
Explore repository readiness03 / Economics
Engineering investment decisions
Compare scenario costs and capacity with explicit assumptions. A modeled capacity gain becomes cash savings only through a separate realization plan.
Model a scenario04 / Governance
Review and validation practices
Use scoped findings to discuss missing checks, unclear responsibilities, and exceptions. Bring in customer policy context through an agreed review.
Review the boundariesStart with your engineering evidence
A clearer picture.
A better next move.
Connect your GitHub repositories and start understanding the patterns behind your engineering outcomes.