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Snow Digital

Software engineering · Bangkok

Software engineering, with agents doing the repeatable half.

Snow Digital builds software for European and Thai companies — business applications, APIs, mobile — and automates the operations work that sits around them. A fleet of AI agents does the repeatable half of the engineering under human review, which is why a fixed price from us buys more than the number suggests. We run our own company on the same system.

Book a scoping call No pitch deck. One call, a straight answer, a price.
run · sales-agent our own pipeline
  1. outbound:ingest prospects in
  2. trigger check a public signal, or skip
  3. draft first touch + follow-up
  4. Natt reads and approves
  5. send only after approval
  6. outbound:status the result, measured

This is our own sales pipeline, not an illustration. The brass gate is a person: nothing we automate sends, spends or writes to a system of record without someone approving it.

What else we build

And the engineering we have been doing for years.

Most of what this team has shipped is ordinary, difficult software: line-of-business systems, integration layers, apps that work with no signal. That work did not stop being needed. If you arrive with a specification rather than a problem, start here.

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The part that usually fails

Most AI projects die in production, not in the demo. These four things are why ours don't.

Independent research puts enterprise AI pilots with no measurable P&L impact at 95%, and pilots that never reach production at 70–85%. The gap is never model access. It is everything around the model.

Deterministic orchestration
State machines and event-driven workflows decide what happens next — not a language model improvising every step. The model does the judgement calls it is good at, inside a path you can draw on a whiteboard.
A real integration layer
One semantic layer over your existing systems, with field-level permissions, instead of a script per tool. Your data stays where it lives.
Evaluation-driven build
Task success measured against a fixed test set before anything ships, traces you can open when a run goes wrong, drift monitoring after launch, and an automated cap on token spend.
Governance you can show a board
Zero-data-retention enterprise APIs, no training on your data, an approval gate on every action that touches money, customers or a system of record.

Sources: MIT NANDA, 2025 · ISG Index, 2026

How we run

We run our company on AI agents. We'll build yours the same way.

Sales, marketing and delivery at Snow Digital run on agents we wrote, dispatched from one repository where every task, decision and human approval is committed as a file. It is not a demo we built for this page — it is why a small team can quote a four-week build and mean it, and it is the thing we will show you on the call.

  • Every agent has a written scope and a list of paths it is allowed to write to.
  • Actions that reach the outside world stop at a human gate. Drafts wait; they do not send.
  • Every run leaves a log and a commit, so a claim can be checked rather than believed.

Ask on the call and we will open the repository, the gates and an evaluation run.

  • laravel dispatcher
  • n8n workflows
  • claude code agents
  • git as the ledger

How the company runs →

Start with the smallest thing that tells you the truth.

A scoping call is 30 minutes and free. You describe the process; we tell you whether it is worth automating, what it would cost, and whether we are the wrong people for it.