Scope
Decide which teams, tools, repositories, questions, success criteria, and reporting boundaries to include.
Start with a Baseline Sprint.
Most organizations begin with a focused sprint to establish a measured view of current AI engineering practice before expanding rollout. The sprint turns AI adoption questions into evidence about workflows, tools and models, code impact, team practice, and governance boundaries.
Start with a focused view of current AI engineering practice
Most organizations begin with a focused Baseline Sprint. The result is a shared evidence base and a prioritized path for improving engineering impact.
Output A practical readout for AI investment, team practice, and responsible governance decisions.
Onboard
Guide selected teams through IDE extension setup, workspace access, and expectations for responsible measurement.
Measure
Capture AI usage, workflow patterns, code impact, and review signals.
Analyze
Review where AI creates value, rework, or risk.
Recommend
Prioritize what to coach, standardize, govern, or roll out next.
Bring the rollout questions that need evidence
You can begin before every answer is settled. These inputs help map DiffDive to the engineering impact and governance decisions your organization needs to make.
A useful first sprint usually includes enough team and repository context to see real development patterns, while keeping the scope narrow enough for a clear readout and an actionable next step.
- Which teams, tools, and repositories should be in the first Baseline Sprint.
- Which workflows matter most: coding, tests, review, refactoring, or learning.
- Which code impact questions matter: persistence, rework, review burden, or risk.
- Which privacy, security, identity, retention, or reporting boundaries need review.
- Who should join the readout from engineering, DevEx, privacy, security, or leadership.
Common questions before a Baseline Sprint
The first conversation usually covers measurement scope, engineering impact questions, privacy boundaries, and enterprise review.
What does DiffDive measure?
AI usage, workflow patterns, code impact, review signals, and team practice across the scope selected during onboarding.
How does the Baseline Sprint work?
The sprint covers scope, onboarding, measurement, analysis, and recommendations for what to coach, standardize, govern, or roll out next.
How does DiffDive protect developer trust?
DiffDive uses team-level visibility by default, protects developer privacy throughout, and focuses insight on improving engineering practice.