Event-driven Monte Carlo simulations price six sports for betting platforms.
Forward-deployed AI engineers, inside your team
Forward-deployed engineering means our engineers work inside your team, on your tools, until your team can run what they build.
Talk to a partner- Who it suits
- Teams with work people do by hand, every day: checking, matching, chasing and re-keying.
- Shape
- Discovery, a pilot, then ongoing or handed over.
- Start
- Usually within 2–3 weeks of a signed proposal.
What forward-deployed engineering means
The engineers who build the system work where it will run, learning the job from the people who do it rather than from a specification.
Where the term comes from. Palantir named its engineers who worked at customer sites “forward deployed”. AI companies such as OpenAI and Anthropic now use the term for engineers who build inside a customer’s own systems. We use it in the same sense.
How an engagement runs
How we work- Weeks 1–2
Sit with the team
A partner sets the engagement up with you. Our engineers watch the work, map it and time it, and pick the first workflow with you.
You keep A written map, the numbers to measure against and a ranked shortlist, whether or not you go ahead.
- Weeks 3–6
Build the first agent
On your tools, in your environment, with frequent demos and a progress update every week.
You keep A working agent your team has tried.
- Weeks 7–8
Run it beside your people
The agent does the work with a person checking, measured against the numbers from the first two weeks.
You keep Before-and-after numbers.
- After
Hand it over
Your team runs it, or our engineers move to the next workflow on the list. Either way, we can stay on monthly for fixes and changes.
You keep A system your own team runs, with fixes included for 30 days after handover.
What we need from you
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One person who owns the outcome and can make decisions.
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About 2–3 hours a week with the people who do the work.
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Access to the tools involved, or a sandbox copy of them.
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Real examples of the work, under your data rules.
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Someone from IT or security for access and review.
Your data
Agents work on real examples from your business, so this is settled in writing before anything is connected.
- Where agents run
- Where your setup requires: in your cloud, in ours, or on your own servers.
- Models and training
- Mainly Anthropic’s Claude models, on business terms. We never use your code or data to train AI models.
- Who has access
- Only the engineers working on your project. When someone leaves the project, their access ends, and our employment terms require them to delete your data.
- What you keep
- The code, prompts, tests and examples, in your repository, under your accounts. A runbook your team has used, and the numbers: where you started and where it is now.
- Agreements
- We sign your NDA, or send ours, and a data processing agreement (DPA) if you need one.
AI we have shipped
A selection of our work-
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Computer vision for logistics yards, checked against India's vehicle registry (Vahan) and GST e-way bills.
How is this different from an extended dev team?
An extended dev team adds engineers to your roadmap for as long as you need them. Forward-deployed engineers come to change how one piece of work gets done, then hand it to your team.
Do your engineers work on site?
They work remotely, with shared working hours with your team every day.
Who checks what they build?
A senior engineer reviews everything they ship.
What does it cost?
The pilot has a fixed price, agreed after discovery. If it continues, it becomes a monthly engagement. A partner will come back with a written proposal and the price.
Tell us what you’d put AI to work on first.
A partner onboards every new client and is a call away when you need one.
Carlton D’Souza, Glenn Fernandes, Jude Fernandes, Tanushree Chandavarker