Questions about AI impact measurement, rollout, and privacy.

DiffDive measures AI usage, workflow patterns, code impact, review signals, and team practice across the teams, tools, repositories, and questions selected during onboarding.

The most useful readout connects adoption to engineering reality: where AI is used, which tools and models appear in the work, which AI-assisted changes persist, where rework or review burden rises, and which practices should become standard across teams.

DiffDive combines an IDE extension with an analysis workspace. The extension captures AI usage across tools, agents, and models. The workspace connects that activity to workflows, code changes, review signals, team-level patterns, and sensitive areas of the codebase.

This lets leaders interpret AI assistance in the context of real engineering work, including coding, review, testing, refactoring, and learning workflows.

DiffDive currently combines a managed VS Code extension with a web analytics workspace. The demo maps your current assistants, agents, models, and approved development environments so the first Baseline Sprint matches your rollout state.

DiffDive is introduced through guided onboarding so scope, reporting boundaries, privacy expectations, and review topics are clear before teams participate.

That onboarding is part of the product experience because the measurement is meant to support team practice improvement and responsible governance.

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

Reporting expectations, admin boundaries, capture, storage, retention, deletion, and stakeholder access are scoped before teams participate in a Baseline Sprint.

The exact data flow depends on the deployment and Baseline Sprint scope. We cover extension capture, workspace storage, retention, deletion, admin access, and reporting boundaries during the demo and privacy review process.

Yes. Privacy review is available as part of enterprise evaluation and onboarding, including security, deployment, identity, retention, admin access, deletion, reporting boundaries, and model-training questions.

Those topics can start in the first conversation and become more concrete during deployment or engagement planning.

A Baseline Sprint is a focused starting point for measuring current AI engineering practice. It covers scope, onboarding, measurement, analysis, and recommendations for what to coach, standardize, govern, or roll out next.

The result should be a practical evidence base: enough context to inform leadership decisions, team coaching, review habits, privacy boundaries, and the next rollout step.

The maturity model gives leadership a shared language for moving from spend visibility to usage visibility, code impact understanding, standardized team practice, and governed AI-assisted development.

It helps teams decide what to measure first, where practice needs improvement, when playbooks should become repeatable, and where stronger governance is needed for sensitive code paths.

Pricing will be scoped during our initial conversations based on team count, rollout model, deployment needs, and support requirements.

We care about making impact and enabling development teams to improve their work with AI. The right package should combine access to the DiffDive tool and maturity model with the methodology, onboarding, analysis, and engagement services that fit your needs.

Yes. DiffDive packages can include appropriate engagement services such as Baseline Sprint interpretation, workflow assessment, maturity-model facilitation, playbooks, and rollout planning. The goal is to help teams turn measurement into better AI-assisted development practice.