Responsible measurement with clear privacy boundaries.

Organizations evaluating DiffDive need clear answers on capture, storage, retention, admin access, reporting boundaries, and enterprise review. Those topics are part of the initial conversation and become concrete during deployment or engagement planning.

Team-level visibility by default

DiffDive is designed for responsible measurement: team-level visibility by default, developer privacy protected throughout, and insight focused on improving engineering practice.

The goal is to help engineering leaders understand where AI assistance is improving practice, where it creates review burden or rework, and which usage patterns should be coached, standardized, or governed.

Responsible measurement starts before onboarding

DiffDive is scoped with the customer before teams participate. The first conversation can cover the engineering questions, team scope, reporting boundaries, and privacy review topics that need to be settled for a Baseline Sprint.

Reports support team and system conversations

The readout is designed around workflows, code impact, review signals, team practice, and governance decisions. It should help leaders improve how AI is used in engineering work.

Privacy review becomes part of deployment planning

Data handling, identity, admin access, retention, deletion, deployment needs, and model-training questions are refined during deployment or engagement planning.

Developer privacy and reporting boundaries

The exact reporting model is scoped with each customer. DiffDive focuses the readout on teams, workflows, tools and models, code impact, review signals, and governance decisions.

This keeps the measurement useful for leaders and teams while making the boundaries clear enough for privacy, security, and deployment stakeholders to evaluate.

  • Team-level and workflow-level reporting by default.
  • Capture, storage, retention, and deletion expectations reviewed during onboarding.
  • Admin access and reporting boundaries scoped before teams participate.
  • Insight focused on improving engineering practice.
  • Sensitive code path questions covered with privacy and security stakeholders.

Privacy review checklist

These are the topics we cover during the initial conversation and refine during deployment or engagement planning. The list is meant to make the review practical, so the right people can join early and the Baseline Sprint can be scoped responsibly.

  • What the extension captures
  • What the analytics workspace stores
  • Data retention and deletion expectations
  • Admin access and reporting boundaries
  • Identity, SSO, and deployment options
  • Model-training and third-party processing questions
  • Contact and notification handling

Initial conversation

We identify the teams, repositories, tools, success criteria, and stakeholder questions that should shape the first Baseline Sprint.

Deployment or engagement planning

We review capture, storage, identity, retention, deletion, admin access, and reporting boundaries so the operating model is explicit.

Team onboarding

Participating teams get clear expectations for responsible measurement, workspace access, and how the readout will be interpreted.