Build

We design and ship the AI system end-to-end.

You hand us a workflow (or a set of related workflows) that's bottlenecking the business. We figure out what's actually going on, design the way it should run, build the automated tasks and AI assistants that handle the work, set up the four basics (data capture, testing, quality control, adoption), and stand it all up in production.

You operate it from week five. Your experts learn to extend it from the inside. The thing we ship survives us. That's the test.

Section I · What gets installed

The full system. Not a tool. Not a license.

Everything the framework calls for, scoped to the workflow you bring us. We don't ship a generic platform. We ship yours, grounded in your data, your existing review process, and the time your team has to keep it alive after we leave.

Scope is set in week one of discovery and written into the engagement charter. From a single workflow inside one team up through a multi-team system with shared context: the playbook is the same, the depth follows the work.

Section II · The cadence

Four phases. Twelve to sixteen weeks most of the time.

i · 1–2 wk

Discovery Map the workflows that bottleneck the org. Identify the experts who own each. Write the engagement charter. Lock the scope.

ii · 4–6 wk

Foundation Build the first wave of automated tasks with your experts. Wire up the data so you can see what's happening. Stand up testing that catches when the AI starts being wrong. Set up the review process that gates what ships.

iii · 4–8 wk

AI assistants Stitch the automated tasks into AI assistants that handle whole jobs. If multiple teams are involved, set up the way they share what they've built. Train your experts to add new ones and ship them on their own.

iv · 2 wk

Handoff Ownership transitions to your team. Documentation, runbooks, and the on-call playbook for when the testing flags something. First-month retainer for course-correction.

Section III · In practice

A financial-services firm. 14,000 earnings-call transcripts a quarter.

The analyst team's bottleneck wasn't reading the transcripts. It was synthesizing across them. The first wave we built reads each call, pulls out named entities and forward-looking statements, and tags topics. An AI assistant sitting on top of those does three-source corroboration before any output goes to a human. A second assistant routes the brief to the right desk by topic.

12 weeks · 24 experts trained · 9 automations shipped · 2 AI assistants in prod

End state: 200 calls a day are summarized into a desk-ready brief. Analyst revision rate stabilized at 12%. Foundry left. The system kept running.

Section IV · When to choose Build

You want the platform shipped. You don't want to learn to build one yet.

The workflow is on fire. Speed to production matters more than knowledge transfer right now; build first, teach your team later if you want to.
You don't have an internal engineering team that can build automation, or you have one but they're booked through next year.
You want a working example of the way to do this in production, so you can point at it and say "do that."

Some clients add a Teach intensive once the first platform is stable, to graduate the next workflow class without us in the room.

Section V · Talk to us

If your platform isn't shipping, send us the workflow that's stuck. We'll scope it.

foundrysolutionsai@proton.me →