The methodology behind two solo-built AI products — and what every Role Install at your company is a fraction of.
Production AI without an engineering team. The operating system, codified. Published. The credential, not the product.
In-Seat AI isn't zero people. It's zero new hires. The methodology doesn't replace your team — it makes each person on it capable of two or three more, by giving them agents they direct themselves. The name describes what stops growing (the org chart), not what disappears (the humans).
Software businesses are built around the assumption that engineers are the labour. Headcount is the lever. Hiring is the bottleneck. Scaling output means scaling team size, which means scaling capital, which means scaling fundraising or bootstrapping pain.
That assumption broke in 2024–2025.
AI agents can now do most of what an engineering team does — write, review, test, deploy, monitor, iterate — when they are orchestrated by one technically literate operator who understands the system. The bottleneck is no longer hiring. It is direction.
In-Seat AI is the methodology that operationalises that shift. It is how one person runs the work of a team without the team. It is also — at a smaller scale, role by role — how one department head runs the work of two or three juniors without the juniors.
The methodology is open. Read it, build on it, take it with you. The methodology is the credential. The product Domanski.AI sells is the install — me sitting next to your operator, building the agents that fit their specific workflow, training them to own it after.
It is not a tool. It is not a stack. It is the operating system.
The problem of "I don't know how to set this up." Every agent gets a brief — not a prompt. A one-page contract that names the job, the inputs, the outputs, the receiver. The operator (in a Role Install) or the technical director (in a product like MzansiEdge) writes the briefs. We build the first one together. From then on the operator owns it. The director is the only human in the loop.
The problem of "I lose the thread between sessions." Your agent remembers — your voice, your project, your last decision, the feedback you gave it on Tuesday. Context lives in files the operator can read and edit, not buried in a chat thread that resets when you close the tab. Open the laptop tomorrow and the agent picks up exactly where you stopped. This is also where the methodology turns "a clever conversation" into a system that compounds.
The problem of "I'm using 15% of what AI can do." Multi-agent stacks — currently Claude Code — actually do the repetitive 60% of the role: outreach lists, first drafts, research compilations, formatting passes, QA, evidence-gathering. Parallel where independent. Sequential where dependent. Your judgment stays on the 25% that compounds — the creative call, the client conversation, the strategic choice. The production layer is fungible: agents improve every six months; the methodology must not depend on which model is current.
The problem of "how do I know this is actually working?" Every run produces a receipt — what the agent did, what it used, what it skipped, what it flagged for review. Proof is not "the agent says it's done." Proof is: the test passed, the deploy succeeded, the metric moved, the artefact passed validation, the second model (Codex) signed off. Gates are mechanical, not aspirational. You audit your own AI labour the same way you'd audit a junior. No black box. This layer is what separates production In-Seat AI from "I let Claude write some code."
Every agent installed under this methodology runs in one of three modes — set at install time, changed only via the memory-update protocol.
inputs/, applies a frozen brief, produces output, notifies the named operator. Stable contracts; per-run variability lives in the inputs.All three preserve the methodology's discipline — same gates, same evidence checks, same audit log. Mode just collapses the live-director steps into a notification when no director is in chat at run time.
A product team of ten ships maybe one significant feature per fortnight. A In-Seat AI operation ships one to three meaningful changes per day.
Output is not 10× lower because there are no engineers. It is comparable or higher on the surfaces where the methodology is applied.
This is not a marginal efficiency claim. It is structural.
And the team stays perpetually current. New models, frameworks, and APIs ship weekly. Most engineering orgs are 6–12 months behind on integrations because eng cycles are slow. A In-Seat AI operation integrates new capabilities at the speed they ship — investigation brief on Tuesday, integration shipped by Friday. The methodology absorbs the model churn instead of being slowed by it.
The receipt.
Product. Sports-betting intelligence platform. Generates evidence-backed recommendations, narratives, image cards, and publishes to a Telegram bot plus a fanout publisher across 23 social channels.
Headcount. 1 (Paul).
~5–6 FTEs replaced. Total cost: token spend + one director. This is the proof, not the claim.
The first Role Install.
Workflow. Distributor pitch decks for a 7-person premium spirits brand house operating across multiple markets and four product lines.
Headcount before. The brand team assembled each distributor deck manually — 4–6 hours per deck, repeated 8–10 times a quarter as new distributor conversations opened.
The install. Built live in the 13 May 2026 Founding Workshop.
First Role-Install-flavoured build at a client. The methodology, role by role. Read the case study →
Bespoke AI systems for creative production. Installed into your employees. Not in place of them.
Three engagement shapes — Founding Workshop (half-day), Role Install (per-operator), Continuous Operator (monthly). Entry pricing on the pricing page. Full conversation on the discovery call.