Notes from a domain software studio

We’ve been running this studio with our heads down for a while — shipping MeterSnap into UAE district-cooling operations, building Seatroom into a real multi-tenant facility platform, and watching what AI-assisted engineering actually looks like inside a small team that has to make money this quarter.

This is where we’ll write about that.

What you’ll find here

Three kinds of posts, mostly:

1. Domain notes — what we’ve learned about a specific industry vertical that you’d otherwise have to spend years inside to discover. District-cooling tariffs. SCADA quirks. Why facility operators run on WhatsApp and Excel and what that means for software design.
2. Build notes — how we ship. Stack decisions, the bits of infrastructure we lean on, AI-leverage patterns that worked for us and ones that didn’t.
3. Field notes — short observations from inside customer deployments. The thing the spec missed. The reason a feature gets built or pulled.

We’ll keep them short. The bar is: it teaches the reader something they couldn’t easily Google.

A small example

Here’s a snippet from the bill-intelligence pipeline that ground-truths every district-cooling bill against a meter reading:

type BillExtraction = {
  customerId: string;
  meterId: string;
  periodStart: string;     // ISO date
  periodEnd: string;       // ISO date
  consumptionRTH: number;  // refrigeration ton hours
  baseTariff: number;      // AED / RTH
  totalAED: number;
  auditFlags: AuditFlag[];
};
 
type AuditFlag =
  | { kind: "tariff-mismatch"; expected: number; actual: number }
  | { kind: "consumption-spike"; sigma: number }
  | { kind: "missing-meter-read"; daysSince: number };

Twenty audit checks run on top of this, and the audit flags drive the entire reviewer UI. The interesting part isn’t the schema — it’s that the schema took eleven iterations with real bills before it stopped surprising us.

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