IT asset disposal business · Platform architecture, AI & automation
In a pitch, a prospect asked how he’d know a drive had been wiped. I answered it. IT asset disposal touches a warehouse bench, a corporate client’s compliance team and a wholesale buyer. They need different interfaces over the same record.
Live platform counts, August 2026. AI evaluations since June 2024.
The situation
The business processes IT assets at volume: collection, data destruction, testing, grading, pricing and resale. Each of those steps was a judgement made by a person, and when one fell through, nothing said so.
The harder problem was three audiences who share nothing except the asset record: a technician at a bench needs hardware access and a fast keyboard-driven flow, a corporate client needs collections, quotes, documents and an audit trail they can show an auditor, and a wholesale buyer needs stock and prices. Built as three systems they’d disagree within a month.
My role
I shaped the process, built the software, and sat in the pitch.
This engagement started with the workflow and one question: which of the judgements people were making by hand could a system make more consistently?
Working that out and building it were both mine, so I could put the automation where the money was.
I sat in the pitches too, so I knew which questions a client would ask about the audit trail.
The outcome
The business has since won work with some of the largest insolvency practices in the UK, where evidencing what happened to every device is what’s being bought. The sequence is evidenced, and whether the platform is why they won is my reading, not something I can prove.
What changed
Two technicians could look at the same laptop and write down different things, and nothing downstream could tell. Every device now resolves to a defined grade with stated reasons behind it. Over 248,000 tests have gone through it.
In asset disposal the certificate is most of what the client is buying. Theirs once depended on somebody reading a number off a drive and typing it in. The serial’s now read off the hardware and bound to the certificate before anyone touches either.
The client’s view and the warehouse’s view used to be two accounts of the same events, and now they read from one record.
Cosmetic grading feeds pricing, so a model that varies on a grade varies the price. It’s machine-assisted and configured to give the same answer twice, and the output feeds a rule that decides.
Reporting comes from a layer built for it, and the cost of every AI call is logged against a budget as it happens.
Some hardware couldn’t be audited by the main application, for reasons that belong to its operating system, so those devices used to sit outside the records altogether.
Decisions worth explaining
The bench app, the client portal and the wholesale side are deliberately different products over one asset record, because one interface would suit nobody.
Devices that couldn’t be priced were falling out of the logic with no error raised. They’re now sorted by the reason they failed and queued for a person, and somebody is on the hook for each of them.
Evidence reconstructed afterwards is what an auditor rejects, so destruction records, test results and audit reports come out of the same flow that does the work.
Stack. C# / .NET and Python, MongoDB, Azure storage and hosted vision models, across desktop, browser and web clients.
“We brought Ashley in to find where the business could run better, optimise it and build what it needed. He delivered far more than we asked for. Processing that took a technician most of a morning now takes minutes. The system he built has helped us win pitches with large corporate clients. And I no longer spend every day in the weeds: I run the business.”
Work with me
Tell me the problem you’re trying to solve.