For engineering leaders rolling out AI

Know if AI is actually working for your engineering team

Correlate AI tool usage with real productivity and code quality metrics. Stop guessing ROI. Measure it.

Early access opening Q1 2026 · Free pilot for first 10 teams

Your team adopted Claude Code, Cursor, Codex... now what?

You are spending 10-20% of your engineering budget on AI tokens and tooling. Your board asks: "Did productivity increase?" You have no answer. Usage dashboards show adoption, not impact. Git stats show velocity, not quality. You need the full picture.

LLM Proxy Layer

Tokens consumed per developer, per model, per day. Cost in dollars per team and per project. Request volume and error rates. Prompt and completion token ratio. Model selection distribution (Claude Sonnet vs Opus, GPT-4o vs o3). All tracked via virtual keys per developer, no code changes needed.

OpenTelemetry Collection

From Claude Code: session duration, tool calls per session, files edited, commands run, accept/reject rates. From Cursor: completions accepted, tab completions vs. chat usage, Composer sessions. From Codex: task completion rate, tool invocations, sandbox runs. All via native OTEL or API integrations.

Git Attribution

Percentage of lines per commit flagged as AI-generated vs. human-written. PR size and merge time. Review comment density. Revert rate per PR. Lines surviving to production after 30 days. Hotfix frequency by code origin. Linked to CI/CD pass rates and deployment incidents.

Token spend vs. cycle time

Does higher AI usage per developer actually reduce time from first commit to merged PR? Broken down by team, project, and language.

AI-generated code vs. bug rate

PRs with >50% AI-generated lines: do they produce more hotfixes, more reverts, more review comments? Compare against the team's human-only baseline.

Tool selection vs. output quality

Which tool drives the best results for your team? Compare Claude Code, Cursor, and Codex on lines surviving to production, review cycles, and defect rates.

Adoption depth vs. velocity

Teams using AI for completions only vs. teams using agentic workflows (Composer, Claude Code sessions, Codex tasks). How does depth of adoption affect sprint throughput?

Cost per shipped feature

Total token cost attributed to each feature branch, from first prompt to production deploy. Know exactly what you are paying for each delivered unit of work.

Code longevity by origin

What percentage of AI-generated lines are still in the codebase after 30, 60, 90 days? Compare churn rates between AI and human code to measure lasting value.

Not just software. We help you roll out AI the right way.

Centaurif is a platform and a team. We set up the infrastructure, run the rollout, and stay with you as your AI adoption scales.

Infrastructure Setup

We configure the proxy layer, telemetry pipelines, and git integrations in your environment. You do not have to figure it out alone.

Rollout Playbook

Phased rollout plans, team onboarding, works council documentation, and change management support for your engineering org.

Ongoing Optimization

Regular reviews of your AI usage data. We identify what is working, what is not, and where to invest next.

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GDPR & EU AI Act Compliant by Design

Privacy-by-default with team-level aggregation, no PII storage, works council co-determination support, and full Data Protection Impact Assessment templates. Your legal team will thank you.