Forward-deployed AI engineering

Most AI stops at the demo. Ours goes to production.

Closing that gap takes experience, not optimism. Senior engineers embed with your team, build into the systems you already run, and prove the agent works before you commit. You keep control, you own the code, and nothing rides on a leap of faith.

Book a diagnosis → See how we work

Embedded, not advisoryWritten in code, not glueEvals from commit oneYou own everything we ship

01 The gap

A demo runs on clean data and a happy path. Your operations don't.

The hard part of enterprise AI was never the model. It's the seam where a general capability meets a specific business — the messy data, the exception nobody documented, the system of record that only one person understands, the governance that has to hold up under audit.

That seam is where pilots stall and budgets quietly disappear. Closing it takes someone who can sit with your team, read your data, write production code, and own the result — not a deck, not a vendor support queue. That's the work we do.

02 How we work · the embed model

Map the real workflow

Before any model, we document how the work actually happens — the handoffs, the exceptions, the system of record. The output is a written diagnosis, not a workshop. Most failures trace back to skipping this.

Week 1–2 · written diagnosis

Build the narrow thing, for real

One workflow, one agent, wired into your actual data and tools — not a sandbox. Specs first, then code, with evals and human-in-the-loop gates designed in from the start. A working system beats a slide.

Weeks 3+ · weekly slices, behind flags

Prove it, then hand it over

We instrument the agent so its value is measured, not asserted, and we leave behind the evals, the runbooks, and a trained team. The engagement ends when your people can run and extend it without us.

Exit criteria, not open-ended retainer

03 Method

The discipline that makes it hold up under load.

An agent that demos well and an agent you can trust in production are different artifacts. The difference is engineering rigor — the unglamorous part most “AI agencies” skip. We don't.

// aridan/method.ts type Engagement = { specs: WrittenFirst; // not vibestests: BeforeCode;evals: Always; // catch drifthumans: InTheLoop; // where it mattersownership: Yours; // the code is yourslockIn: None; };

04 What you walk away with

The code

Your repo, your keys

Everything we build ships into your environment, on your infrastructure. No black box, no per-seat tax, nothing you can't read or run without us.

The evidence

Evals & observability

An eval suite and monitoring that tell you when the agent drifts — so a model upgrade or a prompt change doesn't quietly break what works.

The capability

A team that can extend it

Runbooks, architecture written to be read, and your engineers trained to own it. We design ourselves out of the dependency on purpose.

05 Why trust us with it

We don't sell AI strategy as a slide deck. We ship the agent into your stack, then prove it earned its place.

— Operating principle

Built at scale

Two decades in enterprise data platforms — petabyte-scale OLTP and OLAP systems, lineage, governance, and ML infrastructure in production.

Senior-led, always

The person who scopes your work is the person who writes the code. No account managers, no junior hand-offs.

Boutique by design

Few engagements at a time. Depth over volume — your problem gets a principal's full attention, not a slice of it.

Outcomes ledger — published as live engagements reach production. Specific, attributable, with the client's sign-off. No invented numbers.

06 Where to start

The Diagnosis · fixed scope, fixed fee

Start with a diagnosis, not a pitch.

Two weeks. We map one workflow, score the AI opportunities by return and risk, and hand you a written plan your CTO and CFO can both act on. The fee is credited toward the build if you proceed.

Output A

Workflow map

How the work actually moves today — inputs, exceptions, system of record.

Output B

ROI-scored opportunities

Every candidate ranked by value and feasibility, not hype.

Output C

A phased build plan

What to build first, what it costs, and how we'll know it worked.

Have a workflow that should already be automated?

Tell us the one process that drains the most hours. We'll tell you, honestly, whether it's ready for an agent yet.

Book a diagnosis →