Hands-on AI implementation
You've already seen what AI could do for one of your processes. Getting from that promising demo to something your team uses every morning is integration work, messy data and a hundred edge cases — and it doesn't happen in one shot. That part is my hands on it.
One person, working directly with you · Fixed-price pilot · You own everything
The one-shot problem
This is the gap almost every company falls into. AI looks like it works immediately — because on three hand-picked examples, it does. Then it meets your actual data, your actual systems and your actual edge cases, and stops being impressive.
All genuinely useful — as evidence it's worth doing. None of it survives contact with Monday.
Nine things, none of which a chatbot does for you. This is the part I'm hired for.
What I do
Four things, usually in this order. Most people start at the first and decide about the rest once they've seen something working.
I sit with the people doing the work and follow it end to end. Some processes are perfect for this; some just need a fixed form and no AI at all. You get a straight answer on which yours is before you spend real money.
Document processing, ticket triage, an assistant over your internal knowledge, a workflow that touches real systems. Built against your real data, with a test set written before I start so "it works" is a number rather than an opinion.
The unglamorous half. Your CRM, your ERP, your ticketing system, the shared drive, the mailbox. If people have to leave their normal tools to use it, they won't — so it goes where the work already happens.
Models get deprecated, data drifts, someone changes a form and the whole thing quietly degrades. I'll either train your team to own it or watch it for you on a small retainer. Both are priced up front, so I'm not steering you.
How I de-risk it
1
Person on your project — the one who scopes it is the one who builds it. No account layer, no handoff to a junior.
2 wks
From kickoff to something working you can put in front of real users, not a slide about it.
100%
Of the code, prompts, tests and documentation is yours, in your repositories. No black boxes.
0
Lock-in. It runs in your cloud on your accounts, and you can fire me without losing anything.
How it goes
You can stop after any stage and keep everything built so far. That's deliberate — it's what stops me from selling you a stage you don't need.
I watch the work happen rather than read a description of it — the gap between those two is usually where the opportunity hides. You end up with a shortlist of what's worth building, what your data can actually support, and an honest note on anything I think you should leave alone.
One process, built against your real data, with the definition of "working" written down before I start. At the end you have something your team can use and a clear number on whether it beats how you do it today.
Integration, permissions, error handling, monitoring — and the part everyone underestimates, getting people to actually change how they work. One team first, fix what breaks, then widen.
I'd rather leave you independent than keep you dependent. Training, documentation and a support window — or a light retainer if you'd rather I kept watching it while your team gets comfortable.
Where this usually starts
These are illustrative scenarios, not past client results. I'm early on my own book of work and would rather show you exactly how I'd approach something than borrow numbers from someone else's case study. Ask me on a call and I'll walk you through the actual build.
Invoices, claims, applications or contracts arriving as PDFs and email attachments and getting keyed in by hand. Extraction with confidence scoring, only the uncertain cases going to a person, every decision logged.
Inbound tickets classified, pulled together with the right account context, and answered with a draft the agent edits and sends. The person stays in charge; the blank page and the context hunt go away.
Policies, runbooks, contracts and past projects made answerable in plain language — with citations back to the source document, and permissions that respect who's allowed to see what.
Every company I talk to has already tried AI on something. They got a demo that half-worked and then hit the wall. The wall isn't the model — it's the plumbing, the edge cases and the fact that nobody has time to sit with it until it's right. That's the job I take.
Direct
You get one experienced pair of hands, not a proposal, a bench and a project manager billing you to relay messages.
Questions
For plenty of things you can, and I'll tell you when that's the answer. Where it breaks down is the moment it has to touch your real systems and run unattended: reading your messy documents rather than clean ones, knowing when it's unsure, respecting who's allowed to see what, and not silently degrading three months later. That's engineering work, and it's the difference between a tool one person uses and a process the business depends on.
It's a fair concern and it cuts both ways. The upside is that the person who scopes your project is the one who builds it, so nothing is lost in translation and you're not funding a bench. The protection on the downside is that everything lives in your repositories and your cloud accounts, documented well enough that another competent engineer can pick it up. You're never holding something only I can maintain.
No — and assuming otherwise is one of the main reasons these projects stall. Working out what state your data is really in is part of the first two weeks, and it's often the most useful thing you get out of them.
Most of what I build removes the tedious middle of a job rather than the job. I'll be straight with you about where something genuinely reduces how many people you need versus where it just frees up capacity — and I'll say it during the first two weeks, not after you've signed.
You do. Code, prompts, test sets, infrastructure definitions and documentation, all in your repositories and running in your cloud accounts. There's no platform of mine you'd have to keep licensing.
Data stays inside your environment by default, on enterprise model endpoints that don't train on your inputs, with access control and audit logging built in from the first version rather than bolted on later. If you're in a regulated sector, bring your compliance lead to the first conversation.
The first stage is a small fixed fee. The build is fixed-price and scoped after it, so the number reflects your actual problem instead of a guess. I'll give you a range on the first call — I'd rather rule myself out early than waste your time.
Thirty minutes, no deck. Tell me how the work happens today and I'll tell you honestly whether AI is worth pointing at it — and roughly what it would take.