DomeWorks · Work

What shipped, and what it changed.

DomeWorks publishes work only when there is a working system behind the claim. The examples here show AI infrastructure in operating context: the workflow it changed, the constraints around it, and what had to be true for the system to keep running after launch.

Some client systems are still held back by permission and confidentiality. They should not appear as public proof until the release gate clears.

01

Selected systems

These are the systems that are cleared for public reference. The list is intentionally small. A proof page is only useful if every item on it can survive diligence.

DomeWorks · our own operation · 2026

We run our own business on the systems we build

A working AI operating system — request triage, a review gate, and a daily briefing — running the practice end to end.

an afternoon

for a knowledge-base migration that used to take a weekend

Read the case →

02

How to read these examples

A DomeWorks case is not a logo slide. Read each example for four things:

  • The workflow: where work was getting stuck before the system existed.
  • The operating context: who needed to use it, review it, or maintain it.
  • The build: what changed in the actual flow of work.
  • The proof: what can be said publicly without rounding up the result.

For confidential client work, the same standard applies internally. If permission is missing, the case stays out of the public collection and out of the schema.

03

What makes a system stick

AI systems fail when they stay in the demo layer. They stick when the operating layer is built around them.

  • A named owner for the workflow.
  • Clear rules for what the system can decide and what it must escalate.
  • Context that lives somewhere durable, not in one person's memory.
  • Review gates where judgment still belongs to a human.
  • Measurement tied to the workflow, not to model novelty.
  • A maintenance handoff so the client can keep the system current after DomeWorks leaves.

This is why the proof standard is conservative. A prototype can look impressive in a meeting and still fail in production. A useful system changes how work moves on Tuesday morning.

04

Proof standards

Working system, not deck

There has to be a system people can use. Strategy, recommendations, vendor comparisons, and demos do not become case studies by themselves.

Named constraints where permission allows

The best proof names the real workflow, real constraints, and real operating environment. If a client cannot be named, the anonymized version still has to be specific enough to be useful. If it cannot be specific without exposing the client, it stays unpublished.

Measurement before claims

Metrics need to trace to the work. If a number is not cleared, sourced, and connected to the workflow, it should not appear in the page copy or JSON-LD.

Release gate before schema

Unreleased cases are not just hidden visually. They are excluded from the work item list, prerendered routes, sitemap, and JSON-LD, so search engines see the same proof set a human sees.

05

Related engagement path

Most public proof starts with the AI Operating Leverage Audit. The audit maps where coordination drag is costing the company time, then scopes the AI Systems Sprint that can turn one or two workflows into working systems.

If you are deciding what is real enough to build, start with the audit-to-sprint path. For the underlying standard, read the framework behind working AI systems.