A large claim, so here is the arithmetic. One senior engineer embedded in your operation, shipping working AI systems against your real data. No project team, no discovery phase, first system live within weeks.
Weeks to value
Not two quarters of discovery
One engineer
No agency margin or bench
Yours outright
Documented, no lock-in
Time to first working system
2 to 4 weeks
In production, not a demo
One engineer
Inside your business
25+
Years delivering operational software
1000s
Engineers and operatives serviced
6+
Industries from freight to HPC
2-4 wks
To the first system running in production
The model
The role started at Palantir and is now how the AI companies themselves work. Instead of selling software or advice from a distance, you put a senior engineer inside the business. They sit with the people doing the work, learn the operation, and build against real data and real workflows.
This is how Provolve has proudly worked for decades. It has only recently been given a name.
On site and in your operation, alongside the people who use the systems. Not delivering against a scope document agreed six months ago and never revisited.
The deliverable is working software your team uses on Monday morning. Not a roadmap, not a maturity assessment, not a set of recommendations for someone else to implement.
Your data, your exceptions, your compliance rules, your people's trust in the output. That is the part no product can ship for you, and it is where the value actually sits.
Watch a workflow on Monday, put a working tool in someone's hands by Friday, then fix what breaks in contact with reality. Weeks, not quarters.
Why the role exists now
Anyone can call an LLM. The hard part is knowing which workflow is worth automating, wiring it into the ERP or the WMS or the document pipeline, building the evaluation and guardrails so it does not quietly go wrong at 3am, and getting operational staff to trust it enough to stop doing the job by hand.
None of that can be done from outside the business. It needs someone technical, senior, and in the room.
| Consultancy | Dev agency | Hiring in | Forward deployed | |
|---|---|---|---|---|
| You get | A report and a plan | A scoped build | A permanent salary | Working systems in production |
| Time to value | Weeks to a document | Months to a release | Months to hire and onboard | First system live in 2 to 4 weeks |
| Learns your operation | Through interviews | Through your spec | Over the first year | By standing in it, in week one |
| When it goes wrong | Out of scope | Change request | Depends on the hire | Fixed the same week |
| AI capability | Slideware and strategy | Varies, often outsourced | Scarce and expensive | Daily production practice |
When to deploy someone in
It is that nobody senior enough is close enough to the work to say what should be built. Advice from outside produces plans. Being inside produces systems.
If several of these sound familiar, an embedded engineer will get you further than another round of discovery.
How a deployment works
Every deployment follows the same shape. The point is to get something real in front of the people doing the work before anyone has had time to write a specification for it.
On site with the operation. Watching the actual work, not reading a process document that describes how it was meant to happen. By the end of the week there is a map of where time and money are being lost.
One workflow, taken all the way into production and put in front of real users. Not a demo, not a proof of concept that lives on a laptop. Something your team can use to do their job this week.
Contact with reality always exposes things no design session would have found. That feedback goes straight back into the system, and the next workflow starts while the first one is bedding in.
Deployments should end well. Either your team picks the systems up with documentation and enough understanding to change them safely, or the deployment continues onto the next part of the operation.
Scout
One week
Embedded diagnosis. You get the workflow map, the shortlist, and an honest view of what is worth building.
Deployment
6 to 12 weeks
The full model. First system live inside a month, then extend into the workflows around it.
Standing
Days per week
Ongoing embedded capacity, including CTO-level oversight where the business needs it. Fractional CTO
What gets built
Most AI projects fail on the unglamorous parts: the data is a mess, the integration does not exist, nobody trusts the output. A deployment covers all of it, because the AI is worthless without the rest.
LLM workflows that sit inside a real process and are measured on whether the process got better, with human review wherever being wrong is expensive.
Twenty-five years of ERP, WMS, MRP, and field service means the systems your AI has to talk to are familiar territory, not a discovery exercise billed to you.
The reason the last AI attempt stalled is usually here. It gets fixed as part of the deployment rather than raised as a blocker and handed back to you.
Being inside the business means the strategic questions get answered with evidence from the work itself, rather than from a workshop and a set of assumptions.
Why one person can do this
The forward deployed model only works if the engineer can build as fast as they can learn. AI-assisted development is what makes that possible, and it is how every hour of every engagement is executed: architecture, implementation, review, and testing.
A workflow discussed on the warehouse floor in the morning can exist as working software by the end of the week. That loop is short enough to keep the people who do the job involved in shaping it.
AI-assisted review surfaces edge cases, security gaps, and design problems before they reach production. Fast delivery only counts if the thing still works in month six, when the deployment is over.
Guidance on where AI fits in your business comes from someone shipping production systems with these tools every day, not from a certification, a vendor deck, or a workshop.
This is why one person embedded can outrun a project team.
No handoffs, no requirements written for someone else to misread, no waiting for the next sprint.
Track record
Being embedded is worth nothing without the depth to recognise what you are looking at. Twenty-five years of designing, building, and rescuing operational systems is what makes week one useful instead of an education paid for by you.
View Case Studies25+
Years
6+
Industries
Systems in production
Real-time visibility of labour, compliance, and site activity for specialist contractors - replacing paperwork with a live digital system.
Low-code LLM workflow platform built with React, Rust, and Python for business users and developers.
Full ERP, WMS, MRP, ordering, accounting, and product configurator across multiple facilities.
Legacy replacement with Android app, real-time sync, and contract management across 13 branches.
Multithreaded, low-latency Rust software for data-centre power management with secure gRPC.
Booking, real-time pricing, warehouse tracking, and compliance tooling for US airport freight operations.
WMS, call centre, payment gateways, and fulfilment systems for a high-volume gifting retailer.
Few possess his level of technical expertise while also excelling in communication across all business sectors; this distinction truly sets him apart. It's this combination of skill, commitment, and communication that keeps us continually reaching out for his services.
Marc Mudie
Head of Enterprise Applications · Stannah
Book a short call to talk through where the work is getting stuck, what your systems are costing you, and whether a deployment is the right answer. If it is not, you will be told that.