Product documentation

Four connected levels of AI engineering performance.

Binomial evaluates individual effectiveness, team capability, codebase readiness, and organizational leverage from selected repository and delivery evidence.

01

Inputs

Assessments are GitHub-first and may include customer-selected repositories, pull requests, reviews, tests, ownership, tickets, delivery workflows, AI usage signals, and relevant cost context. The source scope, time period, and exclusions are recorded before analysis.

02

Observed evidence and modeled conclusions

Observed evidence describes selected events and artifacts in the approved sources. Modeled conclusions interpret patterns across that evidence. Conclusions are not presented as direct facts; their assumptions, confidence, limitations, and relevant context stay visible.

03

Individual profiles

Named profiles are available only to authorized customer reviewers and only within the customer-selected scope. Each insight includes work context, supporting evidence, confidence, and limitations. Binomial does not reduce an engineer to a single productivity score or make automated personnel decisions.

04

Team and repository views

Aggregated views connect team practices with repository conditions instead of assigning every difference to an individual. Work type, codebase quality, ownership, review load, architecture, and operating context remain part of the explanation.

05

Assessment outputs

The assessment produces individual profiles, a team capability map, a repository AI-readiness assessment, an organizational leverage view, a technical-debt roadmap, and a measurement baseline. Every output identifies supporting sources, known exclusions, and the decisions it can reasonably support.

06

Ongoing measurement

The baseline makes later coaching, team standards, codebase improvements, and organizational changes measurable. Follow-up compares like-for-like evidence where practical, updates confidence as the evidence changes, and preserves limitations rather than turning directional findings into unsupported certainty.

Next step

Start with a bounded readiness assessment.

Define the questions, sources, reviewers, and decision boundaries before connecting sensitive systems. The assessment establishes a reviewable baseline for improvement.