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.