03 Service

AI & automation
that earns its keep.

Practical AI and automation grounded in your own data and workflows — built to remove real hours, with a human in the loop where it counts. No hype, no science projects.

Typical timeline6–12 weeks
EngagementFixed scope or retainer
Best starting pointA two-week discovery
You getWorking software + docs

What we mean by AI & automation.

Most “AI” pitches are a demo that never ships. We start from the boring, valuable end: the repetitive work your team does every week, and the questions they keep asking the same documents.

Then we build the smallest thing that removes that work reliably — grounded in your data, measurable, and safe to hand to real users. If a plain script or a better process beats a model, we’ll tell you.

A Where we help

Four things we do well.

01

LLM features & agents

Assistants, copilots, and agents wired into your product and tools — scoped so they’re useful, not just impressive.

02

Workflow automation

Connect the systems you already use and automate the hand-offs between them — reliably, and visible when something breaks.

03

Knowledge & search (RAG)

Answers drawn from your own docs, tickets, and code, with citations — so people trust the reply and can check it.

04

ML & data pipelines

The plumbing underneath: clean data, evaluation, monitoring, and a way to improve models once they’re live.

B What’s included

Everything you need to run it after we’re gone.

A working system

Deployed in your environment, not a slide deck or a notebook.

Evaluation & guardrails

Tests for accuracy and safety, so you know when it’s good enough — and when it isn’t.

Plain documentation

How it works, how to change it, and what to watch. Written for humans.

Monitoring & hand-over

Dashboards and a clean hand-over so your team can own it. No lock-in.

C How a project runs

Prove it small, then scale it.

01

Discover

Two weeks to map the workflow, the data, and whether AI is even the right tool.

02

Prototype

A working slice against real data, measured — so we’re deciding on evidence, not vibes.

03

Build

Harden it: evaluation, guardrails, and the integrations to put it in front of users.

04

Operate

Monitor, improve, and hand over cleanly whenever you’re ready to take the wheel.

Proof

An AI routing engine that cut manual dispatch by 70%.

Read the Aurora Freight story
−70%
Manual work
3.2×
Throughput
9 wk
To production
D How we build

Tools we reach for.

We’re not religious about the stack — we pick what fits the problem and what your team can maintain.

PythonTypeScriptClaude / OpenAILangGraphPostgres + pgvectorTemporalFastAPIAWS / GCPKubernetesdbt
E Questions

Things clients ask us.

Will our data be used to train someone else’s model?+
No. We build so your data stays in your environment, and we use providers and settings that don’t train on your inputs. Privacy is a day-one decision, not a patch.
Do we need a huge dataset to start?+
Usually not. Many high-value cases — search, drafting, routing — work with the documents and history you already have. Discovery tells us honestly whether you have enough.
What if AI turns out to be the wrong tool?+
Then we’ll say so, ideally in the first two weeks. Sometimes a better process or a plain automation beats a model — and we’d rather tell you that than sell you a project.
How do you keep it from making things up?+
Grounding answers in your data with citations, evaluation against real examples, guardrails, and a human in the loop for anything consequential. We measure it, not just hope.
Let’s build

Have something worth automating?

Book a 30-minute conversation with the people who’d actually build it. We’ll tell you honestly whether it’s worth doing — and how we’d start.