Customer-defined scope
Agree on repositories, workflows, and analysis questions before access is requested.
Private engineering intelligence
Understand your engineering systems with confidence.
Binomial analyzes engineering work, AI usage, delivery risk, and technical drag through a scoped evaluation path designed for sensitive software environments.
Start with a bounded review, agree on the systems in scope, and see a sample analysis before any broader rollout.
For teams that want useful analysis without opening every system on day one.
Scoped integrations
Read-only where supported
Security review before connection
Sample analysis before rollout
Trust model
Built for sensitive engineering environments.
Binomial is intended for organizations evaluating analysis over important source-control, ticketing, delivery, and AI-assisted development signals. The evaluation starts with scope, access, and review decisions before integration.
Least-privilege posture
Use the narrowest practical access path for the evaluation and avoid broad rollout defaults.
Read-only review first
Start with analysis-oriented access where supported by the connected systems.
Security review before connection
Give technical, legal, and procurement reviewers a clear path before sensitive systems are connected.
No visitor data sale
Public-site contact information is used to respond to inquiries and evaluate access requests.
No unsupported claims
Security and compliance posture should be reviewed through written customer terms, not marketing shortcuts.
1
Scoping call
2
Data/access review
3
Limited pilot
4
Sample report
5
Decision
AI Economics
Evaluate AI-assisted development without opening everything at once.
A pilot can compare selected pull requests, AI-assisted changes, review load, ticket movement, and tool spend to show whether AI is reducing work or moving it somewhere else.
The first answer should be narrow: where AI changed the work, where review changed, and where risk moved.
Track AI tooling and inference cost across teams, repos, and workflows.
Measure actual usage patterns, not vanity rollout metrics.
Compare AI-driven engineering activity against throughput, review burden, rework, and delivery outcomes.
Identify where AI-generated code is increasing duplication, hallucinated logic, weak testing, or architectural drift.
Evaluation scope
Start with the systems you already worry about.
The first evaluation can be limited to specific repositories, services, issue queues, release workflows, or AI-assisted change patterns. That keeps the work concrete and makes the access decision easier to review.
Pull request evidence
Review the work where software quality and ownership are visible.
Sample analysis can cover review depth, change size, ownership concentration, test movement, and AI-assisted code patterns.
Delivery evidence
Compare selected tickets, pull requests, and releases to find where work waits, reopens, or changes direction.
Cost evidence
Map tool spend, review time, rework, and remediation effort to the scope of the pilot before estimating broader impact.
Governance evidence
Identify missing reviews, unclear owners, policy exceptions, and release-process gaps inside the agreed evaluation boundary.
Sample questions
Questions the pilot should be able to answer.
Show which scoped repositories or workflows have repeated fragile changes, weak review, or unclear ownership.
Separate normal review load from rework, reopen loops, oversized changes, and unclear acceptance paths.
Trace selected tickets and releases to the handoffs, dependencies, or queues that slow delivery.
Find missing approvals, skipped checks, policy exceptions, and ownership gaps within the pilot scope.
Compare AI-assisted work against review effort, rework, test changes, and delivery movement.
Sample report
What a sample report should include.
Scope and assumptions
Exactly what was reviewed, and what was left out.
The report should name the repositories, workflows, dates, access mode, and exclusions so reviewers can judge the result.
Evidence summary
Findings tied back to concrete engineering signals.
Each finding should connect to the selected pull requests, tickets, workflow events, cost records, or AI-assisted changes behind it.
Reviewer view
Open questions for security, legal, and technical owners.
The pilot should make access, retention, ownership, and next-step questions visible before any wider connection is approved.
Decision path
Approve, narrow, expand, or stop.
The sample report should support a practical decision about whether the evaluation scope is useful enough to continue.
AI-specific appendix
Where AI appears to help, add review load, or increase risk.
AI findings should separate usage from effect: what changed in review, rework, test coverage, delivery movement, and code patterns.
Example packet
A decision packet, not raw charts.
How to read the output
The report should explain what was analyzed, what was excluded, and what changed the decision.
That keeps the result useful even when the pilot remains intentionally narrow.
Private evaluation
Start with a scoped conversation.
Share the engineering questions, AI adoption concerns, or operating risks you need to understand. Binomial will help frame a careful evaluation path before any sensitive system is connected.