In the AI era, some teams just grow their engineering budgets. Others manage them — on OpMetrics.
Without a layer of transparency — when people’s allocation and activity aren’t visible — burnout, drops in productivity and missed deliveries surface too late.
Cost is assembled by hand, after the fact. Without live delivery economics, overruns and expensive tracks stay invisible until the budget is spent.
Spend on AI tools grows with no read on real usage or effect on speed — payback stays an assumption, not a measured quantity.
OpMetrics resolves data from the systems your team already uses to people, teams and projects — automatically, every day.
A map of allocation and load across people and projects: overload and under-load are recognized as they arise, not after the fact.
The cost of every project and team — compensation, AI and tooling in one figure, tied to output. Delivery economics becomes measurable: where a unit of work is expensive, and where it’s efficient.
Process bottlenecks — slow review, QA returns, incidents — surface before they break a release.
Real spend and real effect of AI: who uses the tools, how delivery speed changes. Subscription decisions on usage data, not guesses.
OpMetrics is an MCP server — the open protocol of AI agents. Connect it to Claude, ChatGPT or any compatible client and ask about your engineering where you already work with AI. Roles, access boundaries and precision labels — the same as on the screens.
How much did we spend on Aurora in July?
Aurora's full spend for July is $12,480. By person:
| Person | salary (alloc.) | AI |
|---|---|---|
| James Miller | $3 480 | $86 |
| Priya Sharma | $2 940 | $54 |
| Diego Ramos | $2 610 | $37 |
And by team?
Core — $7,120, Platform — $5,360. Core runs at 112% of capacity — overload.
Everyone asks within the boundaries of their role: a lead sees their team, a PM their projects, compensation opens only to holders of the corresponding grant. The agent is issued to the whole team on the same terms as product access — no inner circle of the trusted few.
The agent reads the same verifiable data as the screens and names itself where a figure is measured and where it is estimated — there are no invented numbers here. An answer in the chat deserves the same trust as the dashboard: the decision is made in the conversation itself, without re-checking.
Claude, ChatGPT, any MCP client — an open protocol, no vendor lock-in. The team does not learn yet another tool: the analytics answers in a familiar window. For work without external clients there is a built-in chat inside the product.
Trackers, repositories, monitoring, HR — including self-hosted. Several instances of one system resolve into a single picture out of the box; access to sources is read-only.
Data is attributed to people, teams and projects automatically. Every metric keeps its link to the primary record — a task, a commit, an incident — so any number is verifiable.
Every manager sees their own slice — from company economics down to a specific code change. Sensitive data is separated by role.
“Where are resources leaking — and where should they go?”
“What state is the project in, and what does it cost?”
“How healthy are my team’s processes?”
Ordinary analytics asks you to trust the final figure. Here every metric traces down to the primary records and carries a precision tag — measured, estimated or missing.
No composite “scores” or verdicts. Every number carries a precision tag — whether it’s measured, estimated or absent.
Direct measurement, no assumptions: incident resolution time runs from first appearance to close; AI spend comes from the vendor’s own bill.
An honest stand-in where direct measurement isn’t available: delivery time is counted from opening a change to merging it, not to reaching production — and it’s flagged.
A calculation with assumptions, flagged so it isn’t over-trusted: splitting salaries across projects is a computation, not a fact.
No source, no number. Instead of a made-up zero — a dash with the reason: “monitoring not connected”, “person on leave”.
AI spend is only a small part of the picture. Management decisions need the full cost: compensation, AI and tooling — by project, team and specialist. And the payback of AI, not just its price.
A tool trusted by both leadership and the team lasts longer and delivers more. Here the boundaries aren’t a declaration — they’re part of the product’s architecture.
A person is never reduced to a score, and no verdicts are passed. A signal is a reason for a one-on-one; the decision always belongs to the manager.
Message content is never collected. Sensitive data is visible only by role. Profile views and admin actions are recorded in an immutable audit log.
Cloud with a per-client isolated perimeter; data is never shared with third parties. Enterprise is self-hosted or a dedicated perimeter, where data never leaves yours. Read-only access to sources.
Same product on every plan. Scale and support differ.
The whole product on your data.
For a small team.
Pay only for people beyond the first five.
Your perimeter and support.
A contributor is one analysed employee profile.
A demo on your data: what now takes weeks to assemble — in one conversation.
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