AI engineering, built for production

Make the systems you already bought work harder.

We don’t rip out what you run, and we don’t add another tool. We make it work harder, starting at the last mile, where data finally becomes a decision, and the layer we build is yours to keep.

AI engineering, built for production

Make the systems you already bought work harder.

We don’t rip out what you run, and we don’t add another tool. We make it work harder, starting at the last mile, where data finally becomes a decision, and the layer we build is yours to keep.

AI engineering, built for production

Make the systems you already bought work harder.

We don’t rip out what you run, and we don’t add another tool. We make it work harder, starting at the last mile, where data finally becomes a decision, and the layer we build is yours to keep.

Talk to us

Talk to us

Data

Data

ML

ML

Agents

Agents

Apps

Apps

◆ The Decision

◆ The Decision

What we build

One path, data to decision.

Not a menu of services: one system, built end to end.

Fig. 01 — One pipeline, one decision

Fig. 01 — One pipeline, one decision

Fig. 01 — One pipeline, one decision

01

Data Engineering

Pipelines, warehouses, and real-time streams that make your data usable, current, and trustworthy.

02

AI & ML

Data becomes intelligence. Models tuned to your domain and your data, not off-the-shelf guesses.

03

Agents

Systems that act, running real workflows across your tools, with your people holding the decisions that matter.

04

AI-Native Apps

The interface your team actually works in, decision-first where the stakes are high.

01
02
03
04
◆ The decision

We design it backwards from that decision, so it gets made with the right data, by the right person or agent.

01

Data Engineering

Pipelines, warehouses, and real-time streams that make your data usable, current, and trustworthy.

02

AI & ML

Data becomes intelligence. Models tuned to your domain and your data, not off-the-shelf guesses.

03

Agents

Systems that act, running real workflows across your tools, with your people holding the decisions that matter.

04

AI-Native Apps

The interface your team actually works in, decision-first where the stakes are high.

◆ The decision

We design it backwards from that decision, so it gets made with the right data, by the right person or agent.

See what we build

See what we build

See what we build

The shift

The bottleneck moved. It’s not the model,

it’s the mile to production

Every enterprise can reach a capable model today. Almost none can get it running reliably against real data, real integrations, and real accountability.

That last mile, where AI stops being a demo and starts making decisions, is where projects stall. It's the part we engineer end to end.

0 — The model

The easy part, now.

Production

The hard part, and the part we engineer.

0¼½¾1 mi
The last mile
Real dataIntegrationsAccountability
0 — The model

The easy part, now.

The last mile
Real dataIntegrationsAccountability
Production

The hard part, and the part we engineer.

How we’re different

Product engineers, deployed at your edge.

Not a body shop with a forward-deployed label, product people who make software work for the human at the end of it.

Tools
Data
People
Workflows
Your edge↓ Product engineers, embedded here
01 · Embedded product engineering

For the human at the end of it.

An embedded engineer writes code that works; a product engineer builds something people want to use. Trustable enough to run.

02 · Depth that reaches production

The whole stack, not a slice.

We build data to app, not a slice. That's why our systems ship where others stall at the integration layer, and why you don't need half the stack already in place to start.

03 · Owned, and getting smarter

Yours from day one.

Your system, on your stack, yours from day one. The judgment your experts put in compounds into an asset that stays with you.

01 · Embedded product engineering

For the human at the end of it.

An embedded engineer writes code that works; a product engineer builds something people want to use. Trustable enough to run.

02 · Depth that reaches production

The whole stack, not a slice.

We build data to app, not a slice. That's why our systems ship where others stall at the integration layer, and why you don't need half the stack already in place to start.

03 · Owned, and getting smarter

Yours from day one.

Your system, on your stack, yours from day one. The judgment your experts put in compounds into an asset that stays with you.

Underneath all three

Domain-first, and model-agnostic by design.

The model is a swappable part. The rest stays yours.

The model is a swappable part. The rest stays yours.

We architect so the model is a swappable component, not a lock-in. What we build on top, and the judgment it captures, stays yours.

Our deepest production work is on the Google AI stack today, by choice.

Proof

In production, in hard places.

Different industries, one method, which is the point. Depth isn’t how many industries we list. It’s where we’ve run production systems.

01 / 04 · Mining & Metals

Mining & metals: Plant optimisation, mineral recovery, beneficiation.

Plant-wide ML on a copper concentrator.

A production data and ML system across the full circuit, holding recovery at its ceiling as ore and plant conditions shift, and retraining under human sign-off as the plant drifts.

31ML models in production, feed to final concentrate
≈$20MValue at stake a year for every +0.5% recovery

See more of our work

See more of our work

See more of our work

How we engage

Start with one problem that already has a budget.

We land on a problem you're already trying to solve, a data mess, an ML need, an agent to build, an app to ship, and make it work fast.

AI deployment

Forward-deployed, backed remotely.

Thin and senior team deployed at your edge. A product engineering mindset backing them.

Often a first working system in weeks, not quarters

One quarter
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
Typical AI project: 2–3 quarters →
First working system

Often a first working system in weeks, not quarters

One quarter
W1
W2
W3
W4
W5
W6
Typical AI project: 2–3 quarters →
First working system

Where this leads for you

Today a system. Over time one backbone under all of it.

Start with the one system you most need to work, data to decision, in production. From there, momentum builds in your favour: each system you add goes faster than the last, and your AI decisions stop living in scattered tools and start sharing one consistent, portable backbone you own. Not a platform you adopt, a backbone that accrues from systems that already work.

Bring us your hardest workflow.

Tell us the one system you most need to work. We’ll show you the path from your data to the decision, and what it takes to get there.

Talk to us

Bring us your hardest workflow.

Tell us the one system you most need to work. We’ll show you the path from your data to the decision, and what it takes to get there.

Talk to us

Bring us your hardest workflow.

Tell us the one system you most need to work. We’ll show you the path from your data to the decision, and what it takes to get there.

Talk to us