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Forward-deployed AI engineering

We build it, run it at our own expense, then hand you the keys.

Bring us the workflow that is slow, expensive, or done by hand. We design the system, build it inside your cloud with your engineers beside us, and hand it over documented — or we tell you in the briefing that it isn’t worth building.

$ swarm run "teardown of 3 competitor pricing pages"

  plan      7 tasks · 4 agents · est. $0.84
  gate      awaiting human approval … approved
  tokens    0 spent before the gate opened
  verify    12/12 sources re-fetched, 0 fabrications
  done      report + reasoning graph + raw JSON

# a real run from /deployments/swarm/
7
systems in production, each with a full case study
11
more on the bench, statuses published
Wk 1
working software, from the first week of the build
$0
spent on discovery decks before you see running code
Deployments

Systems we ran ourselves before deploying them beside you.

All seven deployments → Live telemetry → Eleven in the lab →
Deployment 01 · swarm

Multi-agent research with a live audit trail

Agents you can watch work. 0 tokens spent before a human approves the plan; 100% of sources re-fetched before a claim ships.

Read the case study →
Deployment 02 · AutoTest

Self-healing QA from a one-line prompt

Plain English becomes a browser test suite that repairs itself. 2 models must agree before anything passes; a failed assertion is never healed away.

Read the case study →
Deployment 03 · sainma

Ask a video library a question, get the clip

3 fused signals, indexed at scene level, searched by 5 specialist agents. The answer is a watchable clip, not a timestamp.

Read the case study →
Deployment 04 · nearby

Territory to booked meeting, autonomously

Every business diagnosed for about 2¢. Models in 5 judgment roles, code everywhere else — 75% less model spend from one split.

Read the case study →
Deployment 05 · frontline

Customer-facing agents, fully auditable

A support agent and a marketing rep, answering 24/7. Your team reads 100% of what was said in your name.

Read the case study →
Deployment 06 · corpus

One search over five knowledge stores

Chat, memory, notes, bookmarks, and documents in one workspace. “What did we decide about X?” gets an answer with its source.

Read the case study →
Deployment 07 · Meai

AI with its guardrails written in code

Safety-critical AI: 0 code paths can call food “safe,” and 100% of inventory changes are confirmed by a person.

Read the case study →
Core capabilities

Three practices, one architecture standard.

See all three practices →
01
Retrieval you can check

An answer layer over the documents your team already has — every answer arrives with its source, and access follows the permissions you already enforce.

Proven in production: corpus · sainma
02
Agents your governance team will approve

AI does the hours; people keep the judgment: a human gate before money moves, a complete audit trail, and model calls only where judgment lives.

Proven in production: swarm · nearby · frontline
03
Safety invariants in code

When a system must never do something, that rule lives in code and schema, not in a prompt and a hope. A prompt is a request. A filter is a guarantee.

Proven in production: Meai · AutoTest
How we work

Bring us the problem everyone else called too hard.

We take the workflow others walked away from and put it in production: shipped, documented, owned by you. The seven systems above are that appetite spent on ourselves — built, run and debugged at our own expense, so the first thing you pay for is the part that works. Eleven more projects are on the bench behind them.

Delivery has one shape: a permanent core, a build pod chartered per project, and agent crews carrying the volume while senior engineers make the calls. It is the same structure we hire into.

Read the method →

45 minutes, no deck, and a verdict at the end.

The briefing runs 45 minutes with your engineers in the room. If we are not the right firm — or there is no system worth building — we say so and leave you the notes.

Book the briefing