AI does the work and a person signs off.

AI works on the delivery too

Most studios sell AI as something they build for you. Polargate does that too, and it also uses AI to run the studio. This page describes the machinery, with the numbers we have, so you can judge it instead of taking a claim on trust.

A knowledge base per client

Everything starts with context. Polargate keeps a knowledge base per entity in Postgres: text chunks with embeddings and an HNSW index, scoped by client and project, with a wall between client, internal and personal knowledge. Our own tools reach it through an MCP server that exposes more than 100 internal tools: knowledge, tickets, hour balances, invoices, contacts, releases, the fix queue.

The practical effect is that when your ticket arrives, the agent reading it already knows your stack, the decisions taken last quarter and what broke last time. The usual failure is a missing fact, not a weak model. Your data stays in your project: you never see our internal knowledge base, and no other client sees yours. That rule lives in the database, not in a policy document.

What happens when a ticket arrives

Care runs on a multi-tenant ServiceDesk, in production since 17 July 2026.

• Your email lands on our support domain, with DKIM and DMARC checks and signed reply tokens, so a reply attaches to the right ticket and to nothing else.
• A triage agent classifies it: type of change, repository, risk, hours it should consume. That step costs about 0.05 USD per ticket.
• If it is a candidate for automation, the fix runs as a GitHub Actions job inside your repository, through the Claude Code CLI with a hard cap of 80 turns.
• The job posts a diff back and stops. It waits.
• A senior engineer reviews that diff: approve, ask for a replan, adjust the estimate, draft your reply, or cancel it.
• A person merges and deploys, behind typecheck, tests, build and a look at the real page on desktop and on a 375 pixel phone.

The first fix that shipped end to end through this pipeline, in July 2026, cost 0.31 USD in model spend. Model spend per fix is cents. The cost that matters is senior review time, and it does not go to zero.

Parallel audits and a sceptic for every finding

On build work we fan out audit agents by dimension: correctness, architecture and reuse, interface, accessibility, responsive behaviour, performance, and security including row-level policies. Every raw finding then goes to a separate agent whose only job is to refute it. That second pass is what makes the output usable.

• Golf club site, two rounds before merge: 37 raw findings and 4 confirmed, then 26 and 3.
• Hotel site, August 2026: 55 agents across 6 areas, 48 findings, 48 confirmed, 0 refuted, all fixed.
• Operations platform for a hotel refurbishment contractor, before the first user logged in: 59 confirmed findings, all fixed before launch.

We stop after two consecutive clean passes with zero blockers. Verified means build, typecheck and tests green plus the interface opened in a browser, because green gates lie: one project passed its typecheck for months on a tsconfig whose root matched no files.

Releases

Store builds go out from tag-triggered GitHub Actions runs, and over-the-air updates for mobile apps are published from our own release hub with channels, a rollout percentage and a kill switch. Automation prepares the release. A person decides it goes.

The guardrails

These limits are database constraints, not instructions in a prompt.

• No agent replies to a client. Every message that leaves is reviewed and sent by a person.
• No agent deploys. None holds production credentials.
• No agent runs destructive migrations or touches authentication, roles or secrets.
• A daily spend cap stops the automated lane when it is reached.

Client data is not used to train models. Secrets live in an encrypted, audited vault, never in a repository or a chat window. And we tell you in writing where AI sits in your project, including the parts where it is not allowed to act.

How it is priced

Engagements start with a fixed-price Discovery Sprint, and maintenance runs on a Care retainer. What this machinery buys you is senior review on a contained budget, not a junior team with a chatbot.