Provolve works inside manufacturing and operational businesses to eliminate manual work, connect disconnected systems, and deliver measurable improvements using AI-powered engineering. Working solutions in weeks. No unnecessary software. No vendor lock-in.
Working in weeks
Not two quarters of discovery
No unnecessary software
Connect what you already own
No vendor lock-in
Documented and yours outright
Time to first working system
2 to 4 weeks
In production, not a demo
A week inside your operation
£5,000 + VAT, fixed
£2,500 credited against a sprint
25+
Years delivering operational software
1000s
Engineers and operatives serviced
£5k
Fixed-price week to find where the money goes
2-4 wks
To the first system running in production
Where the money goes
Most manufacturing, field service, and logistics businesses already own enough software. The cost sits in the gaps between those systems, and in the people who bridge the gaps by hand every day.
Problem one
Sales in Pipedrive. Designs and bills of materials in SolidWorks. Accounts in Xero. Production planning in a spreadsheet only one person understands. Every handover between them is retyped by hand, and every retype is an error waiting to reach a customer.
Problem two
Planners, estimators, engineers, and office staff lose hours every week to purchase orders, timesheets, job sheets, chasing suppliers, and copying figures from one screen to another. None of it is the job they were hired for.
Problem three
Stock, work in progress, delivery dates, and job margins are known in someone's head or last month's spreadsheet. Decisions get made late, on numbers nobody fully trusts, and the cost shows up as expediting, overtime, and lost orders.
The arithmetic
Take one bottleneck: an office that spends 40 hours a week retyping orders and chasing paperwork. At an employment cost of £25 an hour, that is roughly £52,000 of staff capacity every year, spent on work a connected system does for nothing. That capacity only becomes cash when the business redeploys it or stops paying for it, which is why every Provolve engagement is scoped against a measured result rather than a list of features.
Sound familiar?
Three ways to engage
Every engagement starts from a measurable business problem and ends with a working system and a before-and-after figure. Start small, prove the return, then decide whether to go further.
£5,000 + VAT, fixed
Five days inside your operation, with the people who do the work. We find where time is wasted, where data is retyped, where systems fail to connect, and which fixes are realistic.
You get
Fixed price, quoted against the result
One specific bottleneck eliminated. We connect the systems involved, automate the manual steps, deploy to the people doing the work, and measure the result against the audit baseline. The price is fixed against the scope and the result, not the hours.
You get
Fixed monthly fee, defined capacity
Ongoing automation, optimisation, and senior technical oversight for businesses that would rather keep improving than commission another project. A standing relationship, not a ticket queue.
You get
Sprints and partnerships are quoted after the audit or a short scoping call, and every proposal states the result it will be measured against. Smaller, tightly scoped jobs are quoted separately. Proven components from previous engagements are reused where they fit, which is why a sprint takes weeks rather than months.
How each engagement works, who owns what, and what happens nextHow it gets delivered
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. It is the only reliable way we know to get AI and integration work out of the pilot stage and into daily production use.
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. Where advice is needed, it comes from what was built and measured inside your operation, not from a workshop and a set of assumptions.
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 |
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.
Engagement shapes
Audit
5 days, £5k fixed
Embedded diagnosis. You get the workflow map, the costed shortlist, and an honest view of what is worth building.
Sprint
4 to 8 weeks, fixed price
The full model. One bottleneck removed, first system live inside a month, result measured against the baseline.
Partner
Monthly, fixed fee
Ongoing improvement and support, 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 weeks, not quarters
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 an embedded Provolve engineer 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
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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
A five-day Operational Friction Audit finds out, and leaves you with a costed, prioritised plan whether or not you take it further with us. Book a short call to see if it is the right starting point. If it is not, you will be told that.