Engineering intelligence
Your engineering org, fully mapped before your next stand-up.
Connect a repository and Pacia backfills 180 days of history straight away. Within about half an hour you have DORA metrics, review bottlenecks, sprint and epic forecasts, QA flow and AI tool adoption — for every team, in one place, with the evidence behind every number.
Read-only access. Team and contributor level, every figure traceable to the work.
- Review wait, 14-day trend
- +34%
- Reviewer concentration
- 2 people, 71% of reviews
- Lead time for changes
- 18.2h → 26.4h
- Sprint 42 forecast
- 3 of 18 issues at risk
- Deployment frequency
- Elite band
Every figure links to the pull requests and issues it was computed from.
See it in action
Walk through the product yourself
A guided, click-through tour of the real dashboards — DORA metrics, sprint forecasts, QA pipeline and AI adoption — no account needed.
What you get on day one
- 180 days
- of pull request history backfilledAutomatically, the moment a repo connects
- 5
- code and issue providersGitHub, GitLab, Azure DevOps, Bitbucket, Jira
- 24 months
- of retained historyEnough for real year-on-year comparison
- £0
- for non-developer viewersQA, product, delivery and leadership are free
The dashboard is not the problem. The gap between the dashboard and the decision is.
Every engineering analytics tool on the market will show you last quarter’s DORA metrics. That part is solved. What none of them reliably answer is the question an engineering leader is actually asked on a Monday morning: is this landing, what is holding it up, and what should we do about it this week?
Pacia is built around that gap. It reads the same systems you already use — your repositories, your Jira board, your CI checks, your AI coding tools — and turns them into one shared picture of how work moves, where it stalls, and what is about to slip. Not a leaderboard. A working view of your delivery.
Thirty minutes, start to finish
This is the real sequence, not a marketing abstraction. The longest step is waiting for the backfill to page through your history.
Sign in and connect a repository ~5 minutes
Sign in with GitHub, GitLab or Azure DevOps — the first sign-in creates your organisation for you, there is no separate account-creation step. Then either install the Pacia GitHub App on the repos you choose, or paste a read-only access token for GitLab, Azure DevOps or Bitbucket. No app registration needed for the token route.
History backfills itself ~15 minutes
A background job pages through up to 180 days of pull requests, reviews and merges per repository and normalises every provider into one shape. You can watch it fill in. GitHub App repos also start receiving live webhooks immediately; everything else reconciles every 20 minutes or on demand.
Group repos into teams ~5 minutes
Teams are how everything is scoped — DORA bands, review load, working agreements, forecasts. Point each team at its repositories and, if you use Jira, its projects and boards. Members only ever see their own teams’ data; owners and admins see everything.
Read the first honest picture of your delivery ~5 minutes
DORA metrics with elite/high/medium/low banding, a cycle-time breakdown that splits lead time into coding, pickup, review and merge-wait, the pull requests currently stuck, and — if Jira is connected — sprint and epic forecasts. Every number traces back to the rows it came from.
Read-only in, decisions out
Every provider is normalised into one model before anything is stored, which is what lets an organisation running three different code hosts get one comparable set of numbers.
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
One platform, not nine dashboards
Sprints, epics and QA deliberately share one vocabulary — planned, unplanned, delivered, carried out, hit rate — so a piece of work reads identically wherever you look at it.
DORA metrics
Lead time for changes, deployment frequency, change failure rate and time to restore, each banded elite through low, trended over time, and broken down by stage so you can see where the time actually goes.
Pull request insights
Review wait, pickup time, PR size, rework and reviewer concentration. Find the two people every review is queued behind before they burn out, and the PRs that have been open for a month.
Sprint & epic forecasting Jira
Will this sprint land? Will this epic finish this quarter? Forecasts built from the team’s own history, with dormant epics excluded rather than projected into 2033.
QA & test insights Jira
Test stages, regression counts, escaped defects and the QA pipeline, using your Jira site’s own vocabulary — discovered and confirmed, never guessed from issue-type names.
AI adoption & impact
Real usage from GitHub Copilot, Cursor and Claude Code — seats, active users, acceptance, cost — set against the delivery metrics that work actually moved. Correlation, labelled as correlation.
Investment balance
How engineering effort splits across new value, maintenance, defects and unplanned work, with a drill-down into any category. The answer to "what did we spend the quarter on".
What we do differently
Most of this market grew out of contributor scoring. Pacia did not, and several of these are hard rules in the codebase rather than preferences.
Common in this category
- Contributor scores with no way to see the work behind them
- A confident zero rendered wherever configuration is missing
- Per-seat pricing for everyone who needs to look at it
- Jira treated as planning data, with QA left out entirely
- Forecasts presented as a bare date with no way to check them
- AI usage reported as a vanity adoption percentage
Pacia
- Team and contributor boards where every rank opens to the pull requests behind it
- Unconfigured shows as unconfigured; silence beats a wrong number in a DORA metric
- Unlimited free viewers — you pay for active developers, nobody else
- QA, sprints and epics share one model and one vocabulary
- Every forecast ships with its inputs, its sample size and its confidence
- AI adoption set against the delivery metrics the work actually moved
Connects to what you already run
Read-only access throughout. Every access token is AES-256-GCM encrypted at rest and never returned by any API response.
GitHub
One-click GitHub App with live webhooks, or a read-only token.
GitLab
Read-only personal access token. No app registration needed.
Azure DevOps
Code (Read) personal access token. No app registration needed.
Jira
Sprints, epics, QA stages and defect flow, mapped to your site’s own vocabulary.
GitHub Copilot
Seats, active users and acceptance from the real usage API.
Claude Code
Console or Enterprise analytics keys — usage, sessions and cost.
Questions engineering leaders ask first
How long does setup actually take?
About thirty minutes of your attention. Signing in and connecting a repository takes around five minutes; the 180-day history backfill then runs on its own and takes roughly fifteen minutes per repository depending on volume. Grouping repos into teams is another few minutes. You do not need to finish configuring everything before the first metrics appear.
Does Pacia rank individual developers?
Yes. The Leaderboard is a ranked contributor board with seven selectable metrics, share-of-team percentages and trend sparklines over any date range, and Contributor Insights presents the same data as a single-person lookup. Every figure opens to the pull requests behind it, which is what makes it usable in a review rather than merely arguable.
What access does Pacia need to our code?
Read-only, and it never reads file contents for analysis. For GitHub the app asks for Pull requests (read), Metadata (read) and Members (read). For GitLab a token with read_api; for Azure DevOps, Code (Read). Every token is AES-256-GCM encrypted at rest in a dedicated table and is never returned in any API response.
Do we need Jira?
No. DORA metrics, pull request insights, AI adoption, investment balance, working agreements and developer surveys all work from your repositories alone. Jira unlocks the sprint and epic forecasting and the QA pipeline views, because those need issue-level data.
How fresh is the data?
Repositories connected through the GitHub App update in real time via webhooks. Everything else — GitLab, Azure DevOps, Bitbucket and GitHub connected by token — reconciles on a 20-minute cycle, or immediately when you press refresh. Jira delivery data syncs hourly, and every page states when it was last read rather than implying it is live.
What happens if we have not configured something?
The page tells you it is unconfigured. Pacia will not treat an empty defect-type mapping as "match everything" and render a confident zero — an unmapped rule stays inert and says so. A wrong number in a DORA metric is worse than a missing one.
How much does it cost?
£20 per active developer per month, with a 10-developer minimum. Contracts are annual and paid annually. QA, product, delivery, finance and leadership viewers are unlimited and free. Repositories are unlimited. History is retained for 24 months.
Who can see which numbers?
Access follows the owner, admin and member roles, read live from the database on every request. Members are restricted to their own teams’ data — enforced in the query services rather than hidden in the interface — while owners and admins see the whole organisation.
See your own delivery, not a demo dataset
Connect one repository and judge Pacia on your own history. If it does not tell you something you did not already know within the first half hour, it has not earned the seat.
Read-only access. Guided onboarding included on every paid plan.