Pacia Intelligence

For CTOs & VPs of Engineering

Answer the board with evidence, not with a rebuilt spreadsheet.

You are asked where the money went, why the roadmap slipped and whether AI is paying for itself — usually with two days’ notice. Pacia keeps those answers current from the systems your teams already use, with every figure traceable to the pull requests and issues behind it.

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Quarter to date — all teams
New product value
48% of effort
Maintenance & defects
31% of effort
Unplanned work
21% of effort
Epics forecast to slip
4 of 26
AI seats active
38 of 44 assigned

Drill into any figure to the exact work it was computed from.

The questions land faster than the answers can be assembled

Most engineering leaders can answer any one of these given a week: what did we spend the quarter building, why did that epic slip, is the Copilot renewal justified, are we getting faster or slower. The problem is that they arrive together, usually before a board meeting, and the answer has to survive being questioned.

Pacia keeps the underlying picture continuously current instead of reconstructing it each time. Investment balance shows where effort went across new value, maintenance, defects and unplanned work. Epic forecasting shows what is likely to land. AI adoption sits next to the delivery metrics the work actually moved. None of it is a static export — you can drill from the headline number into the individual issues.

It reports at both altitudes, too. The performance boards rank contribution per person across seven metrics, and Contributor Insights gives the same data as a single-person lookup — so the organisation-level answer and the review-cycle answer come from one dataset rather than from two tools that disagree. Every rank and every total opens to the pull requests behind it, which is what stops a contributor number becoming something people contest rather than use.

The board answer and the delivery answer share a source

The useful executive view is not a separate reporting layer. It is the same organisation-scoped delivery model, framed for the decision and still open to inspection.

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
Pacia Every provider normalised into one model, organisation-scoped on every query, with up to 180 days backfilled the moment a repository connects

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

What you can defend in the room

Each of these is a page in the product, current as of the last sync, with a drill-down behind it.

  • Where engineering capacity actually went

    Investment balance splits effort across new value, maintenance, defect work and unplanned interruptions, per team and per quarter, with a drill-down into any category. This is the R&D question finance keeps asking, answered from delivery data rather than from timesheets.

  • Whether delivery is improving or degrading

    All four DORA metrics banded elite through low, trended over your chosen window, plus a cycle-time breakdown that says which stage — coding, pickup, review or merge-wait — is absorbing the time. A trend beats a snapshot every time you are asked "is this better than last quarter".

  • What is at risk before it slips

    Sprint and epic forecasts built from each team’s own history, with confidence and inputs shown. Epics untouched for 90 days are marked dormant and excluded rather than projected — because nobody closes a finished epic in Jira, and projecting one produces finish dates in 2033.

  • Whether the AI spend is returning anything

    Seats, active users, acceptance and cost from GitHub Copilot, Cursor and Claude Code, set against cycle time, PR size and rework on the work those tools touched. Presented as correlation and labelled as correlation — you can make the investment argument honestly.

  • How the organisation feels, next to how it performs

    Recurring developer pulse surveys whose results render on the same pages as the system metrics. A throughput dip and a morale dip in the same fortnight is a very different conversation from either one alone.

Where this lives in Pacia

Common questions

Can I get a board-ready view without asking my teams to do anything?

Largely, yes. Connecting repositories is a one-time task for someone with admin rights on those repos, and everything derived from pull requests — DORA, cycle time, PR flow, investment balance — needs nothing from individual engineers. Jira-backed views need a one-time mapping of your site’s issue types and statuses, which takes a conversation with whoever owns the Jira configuration.

Will my engineers object to this?

The objection is almost always about being measured by a number nobody can interrogate, and it is a reasonable one. Pacia does report per person — and every figure opens to the pull requests behind it, so an engineer can check, question or correct it, and can see their own view before any review. Working agreements are targets a team sets for itself rather than targets imposed on it. Showing engineers the product beats announcing it.

What does it cost for a leadership team to have access?

Nothing. Pacia charges £20 per active developer per month on an annual contract, with a ten-developer minimum. Everyone who is not authoring or reviewing code — executives, finance, product, delivery, QA — reads the platform free and without a seat limit.

How far back does the data go?

Pacia backfills up to 180 days of pull request history per repository the moment it is connected, so you have trends on day one rather than in three months. History is retained for 24 months, which is enough for genuine year-on-year comparison.

See it against your own delivery data

Connect one repository and judge it on your own history rather than a demo dataset. Guided onboarding is included on every paid plan.

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