Most tools tell you what happened last sprint.
Pacia tells you what's about to.
DORA and PR insights are the floor, not the ceiling. Pacia layers a deterministic forecasting engine on top — surfacing delivery risk, review bottlenecks and quality drift before they show up in next month's retro, with the evidence to back every prediction.
Backward-looking dashboards are table stakes now.
Every engineering analytics tool can show you last quarter's DORA metrics. That's a rear-view mirror. Pacia is built around one different question: given what's changing right now — review load, PR size, AI-assisted output, contributor concentration — what is likely to break next, and what should you actually do about it?
Typical engineering analytics
- Reports what already happened
- One-size-fits-all dashboards
- Individual leaderboards and rankings
- AI usage as a vanity metric, if tracked at all
- Recommendations with no way to check they worked
Pacia
- Forecasts what's likely to happen next, with confidence & evidence
- Every number traceable to source data, sample size and calculation version
- Team-level only — never an individual productivity ranking
- AI adoption, engagement and delivery impact, per provider
- Recommendations tracked as experiments with measured outcomes
One platform, from raw signal to recommended action
Pacia is built in layers — each one deterministic and inspectable, none of it a black box.
DORA & PR Insights
Lead time, deployment frequency, change failure rate and MTTR — plus the PR-level detail behind them: review wait, pickup time, size, rework. Every metric labeled as measured, derived, or estimated. Never a fabricated number.
AI Adoption & Impact
Real usage from GitHub Copilot, Cursor and Claude Code — adoption, acceptance rate, cost, and how AI-exposed work actually compares on cycle time, size and rework. Correlation, clearly labeled as correlation, not causation.
Delivery Risk Forecasting
A deterministic trend and anomaly engine flags review bottlenecks, quality drift and deployment slowdowns while they're still forming — with the contributing signals and confidence shown alongside every forecast.
Evidence-Backed Recommendations
Every recommendation cites the metric and evidence behind it. Accept one, and Pacia tracks it as an experiment — baseline, target, and a measured outcome, not just a suggestion box.
AI coding tools are already part of your delivery pipeline.
Copilot, Cursor and Claude Code are shaping PR size, review load and cycle time whether or not anyone's measuring it. Pacia connects to all three — real usage APIs, not guesswork — and shows adoption, engagement and cost alongside the delivery metrics they actually move.
Built on a few rules we don't bend on
No individual rankings
Pacia reports at the team level by default. It was never designed to rank people.
Nothing fabricated
A number is either measured, derived, or an explicit estimate — and it says which, every time.
Forecasts, not fortune-telling
Every prediction ships with its confidence, its contributing evidence, and a date range — never a bare number with no way to check it.
Correlation stays correlation
AI impact analysis is never presented as proof of causation, even when it's compelling.
Want to see what's coming next in your delivery pipeline?
We're onboarding early access teams now. Tell us about your engineering org and we'll set you up.
Email hello@pacia.io