ShipSystems
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We get ship done

We find where AI pays off in your business. Then we build it.

Engineers audit your workflow, pick the steps worth automating, and ship them to production in your repo and your cloud.

Book an Intro Call

30 minutes with the engineers who would do the build. Bring one workflow and leave with a written read on it.

01 FIND02 BUILDSS GET SHIP DONE
Yes, the name was always a pun. We're committed now.

Trusted by teams shipping real work

Dreamit Ventures logoEarly-Stage Venture FirmGlass Entertainment Group logoTV, Film & Podcast ProductionMOD logoCreative & Branding AgencyScribble logoScribbleHome Health Documentation SoftwarePerry logoPerryAI Pre-Bill Compliance PlatformEFS Networks logoEnterprise Technology ConsultingPanga AI logoVenture Studio & Growth PartnerSyrch logoSyrchAI Investigative Search PlatformOrchid Studio logoOrchid StudioAI Production Assistant for Creators

What we ship

Two steps. Each ends in something you can use.

Step 1 · Find

We find where AI fits

We audit your operations, data, and team, and rank the workflows by value and feasibility. Where the honest answer is don't build it, we say so.

Duration
2–3 weeks, fixed scope
You get
A prioritized roadmap with architecture and cost estimates
Then
Credited toward your first build

Step 2 · Build

We build it and ship it

One scoped agent, end to end, measured on your real cases before it goes live. Your team owns it at handoff.

  1. PrototypeWorking agent on your data, 2–3 wks
  2. EvaluateAccuracy, cost, go/no-go, 1 wk
  3. ProductionShadow → supervised → autonomous, 2–4 wks
  4. OperateDrift checks, tuning, runbook

Start with step 1, or skip to step 2 if you already know the workflow.

Book the intro call →

Before we pick a vessel

Do you need an LLM?

Most decisions inside a workflow don't need a model that writes. Routing a ticket, tagging a document, checking a policy, choosing an agent's next step: each has a fixed set of answers. For those we use system one models, which return one of your options with a probability attached. Chat models are system two, for the parts that need writing and reasoning.

CompareSystem oneSystem two · LLM
Good forRouting, classification, moderation, extraction checks, next-step choiceDrafting, summarizing, multi-step reasoning, explanation
OutputOne of your options, with a probabilityFree text to parse
SpeedTens of millisecondsSeconds
CostInput tokens onlyInput and output tokens
When unsureSays so, so the case goes to a personCan return a confident wrong answer

In step 1 we mark each decision in your workflow as system one or system two, so you only pay for an LLM where it earns its place.

We won't bottleneck

A working agent in weeks, not quarters.

Most AI projects stall waiting on vendor reviews, committees, and a model nobody needed. We scope one workflow, build on your data, and put a working agent in front of your team in 2–3 weeks.

*bonk*
Fig. 3 · An AI initiative meets a six-month procurement cycle. Dramatization.
First working agent
2–3 weeks, on your data
Who builds it
Senior onshore engineers. The people on the call do the build.
Where it runs
Your repo, your cloud, your accounts, from the first commit.

Pick a route

Ways to Work Together

  • AI Roadmap Sprint

    Step 1 · 2–3 weeks

    We find where AI fits and leave a prioritized roadmap.

  • Agent Build

    Step 2 · 6–10 weeks

    One scoped agent from empty repo to a live system your team runs.

  • Embedded AI Team

    Monthly · ongoing

    We keep finding and building: AI delivery, architecture calls, and leveling up your engineers.

  • Founding Engineering Team

    Ongoing · scoped to your stage

    For startups with an empty repo: we build the whole product, agents at the core.

No published rate card. Scope and price are set against your workflow on the intro call.

Anything to declare?

Questions we get before the call

What if we don't know where AI fits yet?

That's what step 1 is for. You bring the operations; we find the workflows worth automating and rank them.

Does every workflow need an LLM?

No. Decisions with a fixed set of answers, like routing, tagging, and policy checks, run faster and cheaper on system one models. We use an LLM where the work needs writing or reasoning.

Who actually does the work?

Senior onshore engineers, with no junior bench behind us. The people on the intro call are the people who do the build.

Can this run without sending data to a model provider?

Yes. Open-weight models on your servers or in your own cloud account, and inside an air-gapped network where that's required.

What happens at handoff?

Your repo, your cloud, your accounts, from the first commit, plus evals, tracing, docs, and a runbook.