Platform
Nine views of one delivery system, sharing one vocabulary.
Most engineering analytics is a collection of dashboards that each define "done" slightly differently. Pacia’s sprint, epic and QA views run off the same readers and the same words, so a piece of work reads identically wherever you meet it. That is what makes cross-team and cross-function reporting possible without a translation layer.
What the platform covers
DORA metrics
The four keys, banded elite through low, trended, and split by cycle-time stage.
Pull request insights
Review wait, pickup, size, rework, reviewer concentration and the stuck-work queues.
Delivery forecasting Jira
Sprint and epic forecasts from observed throughput, with dormancy and scope growth handled properly.
QA & test insights Jira
Test pipeline stages, regression rate and escaped defects, on your site’s own vocabulary.
AI adoption & impact
Copilot, Cursor and Claude Code usage and cost against delivery outcomes.
Investment balance
Effort across new value, maintenance, defects and unplanned work.
Working agreements
Per-team targets and a compliance view with every miss linked.
Performance boards
Ranked contributor boards, per-person insights, team comparison and benchmarks.
Developer surveys
Recurring pulse surveys read alongside the system metrics.
Editors, MCP & CLI
Delivery context in VS Code, JetBrains, Claude Code and Cursor, plus a CI coverage CLI.
One model underneath all nine
The reason a figure on the QA page and a figure on the sprint page agree is that they are computed by the same readers over the same normalised data, rather than by nine features that each learned your systems separately.
How Pacia is wired to what you already run
Read, never written to
- GitHub, GitLab, Azure DevOps, BitbucketPull requests, reviews, merges, checks
- JiraSprints, epics, changelogs, QA stages
- Copilot, Cursor, Claude CodeSeats, usage, acceptance, cost
- Your teamPulse survey responses
What you get
- DORA & cycle timeBanded, trended, split by stage
- Delivery forecastsSprint and epic, with confidence
- QA & qualityPipeline, regressions, escaped defects
- Performance boardsContributor and team, drill-down on every rank
- Investment & AI impactWhere effort and spend actually went
Principles that hold across every view
These are enforced in the codebase, not aspirations in a pitch deck.
Team and contributor level
Delivery flow is reported per team; contribution is reported per person on the performance boards. They are the same underlying numbers viewed at two altitudes, so the two never disagree.
Unconfigured means unconfigured
An unmapped rule stays inert and the interface says so. Pacia never treats an empty configuration as "match everything", because a confident wrong number in a DORA metric is worse than a visible gap.
Every figure drills down
Headline numbers link to the pull requests and issues they were computed from. A metric you cannot audit is a metric that gets argued about instead of used.
Discovered, not assumed
Defect types, link types, statuses, resolutions and the story-points field are read from your own site and confirmed by a person. Epics are found by Jira’s hierarchy level, not by an issue type named "Epic".
Correlation stays correlation
AI impact analysis is presented as correlation and labelled as such, however compelling the chart looks.
Freshness is stated, not implied
Pages say when data was last read rather than presenting cached figures as live. Repositories on the GitHub App update in real time; everything else reconciles on a 20-minute cycle.