Services
Forward-deployed AI engineering
Engineers in your team, shipping every week. We embed forward-deployed engineers alongside your people and put AI into your live workflows increment by increment, working against your real data and constraints rather than a sanitised sandbox. Model-neutral by default, and built to be handed over: your team owns what we ship, with no dependency on us to keep it running.
The process we optimise
The process we optimise: whichever live workflow you point us at
The distinctive thing here is the operating model, not a fixed process. We embed engineers in your team and point them at a live workflow you choose, then ship AI into it week by week, working against your real data and constraints rather than a sandbox. The objective is not a demo or a report: it is a working improvement to the process that stays working after we leave, owned by your people with no dependency on us to keep it running. Because the workflow varies, the discipline is constant. Each increment starts from what the process exists to achieve, maps how it runs today, builds around that workflow, and is judged against the original objective. Capability transfer is a deliverable, not a courtesy, which is why this reads as a rolling engagement rather than a project with an end date bolted on.
Before and after
What changes when the system is rebuilt
Before: AI at arm's length
- AI ambitions that stall because no engineer is close enough to your real workflow to ship into it.
- Vendors who build in a sandbox, hand over a prototype and leave the integration to you.
- Long delivery cycles where months pass before anything reaches the people doing the work.
- A dependency on outside help that never ends, because the knowledge left with the supplier.
After: shipping into the real workflow, owned by you
- Engineers embedded alongside your people, working against your live data and constraints.
- A working increment shipped into the real workflow most weeks, not a prototype at the end.
- Capability transferred deliberately, so your team can extend and run what we build.
- A production handover you own outright, with no standing dependency on us.
How we think about it
We start from the objective, then work outwards
The same discipline runs through every engagement: understand what the process is for, then design the system to serve it and measure against it.
Start from what the chosen workflow exists to achieve
Whatever workflow you point us at, we start from its objective: what it is meant to deliver for the business, and how you would know it was working better. We agree that measure before writing code, because the operating model only earns its place if the process improves against its own purpose, not against how busy it looks.
Map how the workflow actually runs, from inside the team
Being embedded is what makes this honest. We sit with the people doing the work and map the real process, including the workarounds, the manual handoffs and the edge cases that never appear in a briefing. Working against live data rather than a sanitised copy is the point: it is where the difficulty, and the value, actually is.
Build around the workflow and ship weekly, transferring capability as we go
We design increments around your workflow rather than forcing the workflow around a tool or a model, and we are model-neutral by default. Shipping most weeks keeps the work honest and lets your team steer. Capability transfer runs through every increment, so ownership moves to your people as we go rather than in a handover at the end.
Measure each increment against the original objective, then leave it owned
Every increment is judged against the objective we agreed at the start: is the process measurably better, and can your team run and extend it without us. When the answer is yes, that is the exit for that piece of work. The test of the model is that what we ship keeps working after we step back.
An engagement, step by step
A representative engagement, drawn from how we structure this work. This is the first eight weeks of a rolling engagement:
- Weeks 1 to 2
Embed and agree the first target
Our engineers join your team, get access to the real environment, and sit with the people who run the chosen workflow. Together we pick the first increment and agree the objective it has to move. Being inside the team from day one is what lets us map the process as it actually runs.
- Weeks 2 to 4
First ship into the live workflow
We put a first working increment into the real workflow, against real data, rather than demonstrating something in a sandbox. It is small on purpose. Getting something live early exposes the true constraints and gives your team something concrete to steer with.
- Weeks 4 to 6
Delivery cadence
We settle into a weekly rhythm: each week ships a further increment or hardens the last one, prioritised with your team against the objectives we agreed. Your people are working alongside us throughout, which is where capability transfer starts rather than being saved for the end.
- Weeks 6 to 8
Capability transfer and review
We deliberately move ownership towards your engineers, documenting what we build and handing over the parts they will run. At the end of the window we review against the original objectives and set the next quarter's targets, because the engagement rolls rather than stops.
By the end of the first eight weeks the process is measurably better and your team can already run and extend part of what we shipped. The engagement is reviewed quarterly, and the aim throughout is a clean handover with no standing dependency on us.
What you get
- Embedded engineers inside your team
- Weekly shipped increments into live workflows
- Capability transfer to your people
- Production handover you own outright
How it typically runs
Typical engagement: rolling monthly, typically 2 engineers, reviewed quarterly.
Embed
Weeks 1 to 2
First ship
Weeks 2 to 4
Delivery cadence
Weeks 4 to 8
Capability transfer
Ongoing
Indicative timeline. Every project is scoped individually: book a discovery call and we will provide a detailed proposal within 48 hours.
Where it fits
This practice is delivered through one of the two QuantSpark engines.
Model-neutral by default
We evaluate Claude, GPT and open-weights models head to head for every problem, and recommend whichever wins on cost, latency and accuracy.
Proof
Forward-deployed AI engineering in the field
We are still tagging the library by practice. In the meantime, the full body of documented work is one click away.
Explore all our work