Forward deployed engineering · Flagship
Forward deployed engineers, inside your business.
A small senior team of forward deployed engineers joins yours: Rakesh as engagement lead, AI engineers, a data scientist, and security when the work needs it. We map one workflow, set the number it has to move, then build, evaluate and ship. Every week you see what changed and what it is worth.
What you are left with
- One workflow in production with an owner and a measured result
- AI agents with human sign off where it matters
- Evaluation evidence your risk team can read
- A handover your own people can run
Most AI stalls between the demo and a working Tuesday.
Most stalls trace back to one gap. Nobody wrote down how the work really runs: the supplier who always sends the wrong form, the approval that happens on WhatsApp, the spreadsheet only one person understands.
Forward deployed engineers learn those exceptions by sitting with the people who handle them, then build around them. The result gets used because the people who use it helped shape it.
What the engineers ship at each gate.
The same six stages as every engagement, seen from the engineering side. Each gate leaves something your team can inspect.
1
The line
A short brief from the sponsor and a list of the people and systems involved.
FromGroup COOToRakeshSubject(no subject)The board wants an AI plan. Can you help?
2
The business case
A baseline measured from your data, the target number, and the data access the build will need.
Business case, one page
- Problem
- Order to certificate takes too long and nobody owns it
- Number to move
- Days per order, from 14 to 3
- Value at stake
- €380,000 a year, agreed with finance
- Owner
- Head of Quality
- Top risks
- Data access and supplier formats
- Gate
- Fund proof of concept if baseline holds
3
Scoped proof of concept
A working extraction or agent on your data, with a test suite and a pass threshold agreed up front.
Proof of concept scope
- One supplier, one product line, last 90 days of email
- Success if extraction is right 19 times in 20
- Run on your data, inside your tenancy
- Out of scope: other sites, ERP write back
4
Controlled pilot
A human approval queue, an audit log of every suggestion and decision, and weekly evaluation results.
Controlled pilot, today
Certificate matched to order lineApprovedHeat number differs from mill recordHeld for reviewMissing inspection report flaggedApproved5
Pilot at scale
Deployment to more sites and teams, monitoring of quality and cost, and a security review signed off.
Pilot at scale
SitesTeamsVendors6
Deployment
A runbook, drift alerts, the golden set and the code in your repository, handed to the team that runs it.
Live- Owner
- Your operations team
- Runbook
- Handed over and tested
- Value realised
- €210,000 to date, signed off by finance
Illustrative example with invented figures. Real figures stay with the client.
Workflows we take apart and rebuild
Inbox to record
Orders, certificates and supplier emails read by AI agents and turned into structured records, with a person confirming what matters.
Document heavy reviews
Contracts, claims and compliance files triaged and summarised, every answer traced back to its source.
Operational exceptions
Agents that watch a queue, flag what is late or wrong, and draft the fix for someone to approve.
Reporting packs
Weekly reports assembled from the systems you already have, so people spend their time on the decisions.
Team knowledge
Answers drawn from your own documents and history for teams that keep asking the same people the same questions.
A senior bench, assembled for the work.
Rakesh leads every engagement. The rest of the team comes from a vetted network of specialists he has delivered with, chosen for the problem in front of you.
Engagement lead
Rakesh, on every engagement. Owns the outcome, the stakeholders and the plan.
AI engineers
Build the agents, the integrations and the tests that prove they work.
Data scientists
Find the signal in your data and set the number the work has to move.
Full stack engineers
Put the AI inside the tools your people already open every morning.
Security specialists
Threat model, access design and hardening before anything faces production.
Run like a programme, with every release tested before anyone relies on it.
A plan, a risk log with named owners, and a weekly read out against the target, run to PRINCE2, MSP and SAFe standards with a light hand. EU AI Act classification is a design input from week one, so the final review is a formality.
Nothing reaches production without passing the Production Gate: evaluation against a golden set, a security review, and a person who signs off.
It is the same way Rakesh took GenAI to production across 2,500 lawyers at Freshfields, put a digital twin live at Shell in eight weeks, and is building Helios for the oil and gas supply chain.
Questions
What is forward deployed engineering?
Engineers who work inside your business instead of from a distance. They sit with the people doing the work, learn how it really runs, including the exceptions nobody wrote down, and build the software and AI around that reality. We apply it to one workflow at a time, with a measured target and a handover your team can run.
How is this different from a consultancy or staff augmentation?
A consultancy usually hands you a recommendation. Staff augmentation hands you people and leaves the outcome with you. A forward deployed team owns a result: one workflow, one number it has to move, and evidence that it moved. The plan, the risks and the quality are managed by us.
Who is on the team?
Rakesh leads every engagement. The engineers, data scientists and security specialists come from a vetted network he has delivered with, assembled for the problem in front of you.
Where does our data go?
Nowhere it should not. We work in your environment and with your access controls wherever possible, under NDA, and the security review is part of the method rather than an afterthought.
How are engagements priced?
A fixed fee for each stage, agreed after a scoping call, so you see the number before anything starts.
Bring the workflow that costs you most.
Thirty minutes, confidential. You leave knowing whether AI can move it, what it would take and what it is worth. If we are not the right team, we will say so.