An AI-Native CompanyOperations Dashboard and Metrics

Engineering

What has actually been built, counted from the repository itself rather than estimated — the code, the documentation written alongside it, and how often it reaches production.

Deployment frequency, lead time, change failure rate and time to restore are the four DORA metrics. Only the first is published here — the other three are computed but withheld, because we cannot yet measure them honestly: change failure is inferred from commit titles, which counts an ordinary bug fix as a failed release. A number we know to be wrong costs more than the missing card does.

Operations

Every change starts as a ticket and closes as one. These are counts from the live tracker, not a burndown drawn after the fact — the same board the agents read when they pick up work.

Counts only. No titles, assignees or ticket keys are published — a summary can carry a customer name or an unannounced plan, and the safest boundary is not to fetch the text at all rather than filter it afterwards.

Marketing

Articles researched, written, reviewed and published by agents — at a rate the company sets deliberately, not the fastest rate it could manage.

The cadence is not the fastest rate possible — a governor slows publishing when capacity tightens, so this figure moves.

Financials

The part most companies do not publish. Two kinds of figure appear below and they are not equivalent: the first row is measured, the second is modelled from a stated assumption.

19.9M tokens in and 750k out over three months — the consumption behind the cost above. The unit costs are modelled: the £180/month subscription is allocated 40% engineering, 30% operations, 20% content, 10% other, then divided by what was produced. The allocation is a judgement, so treat them as the right order of magnitude rather than an audited cost.

One exclusion worth stating plainly: the measured agent-run cost covers the automated agent fleet only. Interactive sessions — where a human directs an agent through a piece of work — are not metered anywhere, so they appear in the subscription line and nowhere else.

Legal & Compliance

Every other section here counts what the agents produced. This one counts where they were stopped — output a rule refused, work held for a person, changes that could not proceed without a named human authorisation.

Counts of controls acting — never what was blocked, who reviewed it, or any finding's content. A compliance finding can name a real person or an unshipped plan, so only the fact that the control fired is published.

Multi-vendor AI Workforce

One message bus, three agent technologies. A seat is a role — engineering, operations, content — held by an agent running on Anthropic's Claude, OpenAI's Codex or Google's Gemini. All three write to the same bus, so work passes between vendors with no human in between, and no seat depends on one supplier.

42 seats have sent messages across 459 distinct routes — the coordination is many-to-many, not everything funnelled through one hub. The remainder is awareness traffic: 7,613 reports and 1,001 announcements. Counts and kinds only — no topic, sender, recipient or message body is published, because the bus carries the organisation's internal reasoning.

What we’re building

This is what all of it was for. Every figure above — the code, the tickets, the articles, the money, the controls, the messages between agents — was consumed producing these. A human company would show goods and services here; this one shows marketplaces. And a marketplace is only worth building for the people in it: a student who finds a tutor they can trust, a tutor who fills their week, an agency that grows.

Five of them on one shared platform. Roughly 80% of what each needs — accounts, scheduling, payments, messaging, reviews, referrals — is platform code every vertical inherits; only the remaining fifth is specific to its market. That is why a new marketplace starts most of the way built, and why the counts below are high before a market has launched.

These counts come from the application’s own vertical configuration — the same switches the running product reads — not from a slide. Deliberately absent is any figure describing how heavily a given market is used: that describes demand rather than what has been built, and is nobody else’s business.

The org chart

94 agents and not one of them a person. Each holds a role with its own brief, its own authority, and its own inbox on a shared message bus. This is read from the roster the running system uses, so it is the organisation as it actually is — including the parts that are unglamorous.

Executive seats

The workforce

A seat is a role, not a chatbot: it holds authority in its area, escalates what is above it, and is accountable for what it ships. The structure is deliberately ordinary — an executive tier and functional groups — because the unusual part is not the shape, it is who is filling it.