01
Discover
I sit with your team and measure where the hours go. Task by task. Then I rank what AI should take on by hours saved, risk and effort.
- Timeline
- [ 2–3 weeks ]
- Output
- A ranked shortlist, costed in hours.
AI enablement · LondonFor finance and professional-services teams
AI that your team actually uses.
I help finance and professional-services teams find the work AI should take off their desks. Then I build it, ship it and get people using it.
- Engineer-led: I build what I recommend
- Across finance, operations and compliance teams
- Working tools in weeks, not quarters
- Built inside strict governance
Hours Ledger · Sheet 1
1,000
hours of routine work, one cell each. An illustration of what discovery maps before anything is built.
§ 01The problem
Lots of AI ideas, nothing in production.
Most firms I meet have a list. Twenty ideas, sometimes a hundred. A pilot or two. A licence nobody opens. The ideas aren't wrong. They're unmeasured, unranked and unowned. So nothing gets built properly, and nothing gets used.
a.A long list, ranked by enthusiasm.
b.Pilots that never leave the pilot.
c.Tools bought, not used.
§ 02Services
Three pieces of work. Usually in this order.
02
Build
I build working tools myself, in days or weeks. In the stack you already run, with access controls and an audit trail from day one.
- Timeline
- [ 1–6 weeks per tool ]
- Output
- Governed tools, live in your stack.
03
Enable
Workshops for the team. Then 1-2-1s at people's desks, on their own work. I stay until the tools are part of the week.
- Timeline
- [ 4–8 weeks ]
- Output
- People using the tools, usage tracked.
§ 03How I work
Measure first. Build second. Stay until it's used.
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§ 04Work
What an engagement looks like.
MemorandumIllustrative · not a client
Typical clientA regulated firm with a long list of AI ideas
TeamsFinance, operations, compliance
Shape[ 2–5 days a week · 3–6 months ]
1 · Starting point
Capable teams losing hours every week to checking, re-keying and chasing information between systems. Plenty of AI ideas, none of them in production.
2 · Measure
Time at the desks with each team. Every recurring task is timed and logged in one pipeline, with an owner and a cost in hours, then ranked by value, effort and risk with the team leads.
3 · Build
- Step 1Agree the first few tools with the team leads, starting where the hours are.
- Step 2Build them in the stack the firm already uses, governed from day one.
- Step 3Iterate with the people who use them every day.
- Step 4Train the team at their desks so they can run and extend what was built.
- Step 5Move down the pipeline to the next set of opportunities.
4 · Outcome
Tools in daily use, owned by the teams that run them, and the hours they free up measured against the starting point.
5 · Governance
Inside your AI policy and security review. A person checks every output before it is used, and your data stays inside your own environment.
Illustrative engagement. Every firm is different.Happy to talk through real work on a call.
§ 05About
Adam Samuel Gilbert
Founder, SUITER-AI LTD · London
I'm an engineer. I've spent my career shipping software people use.
I measure the work before I suggest anything.
I build the tools myself, in your stack.
I stay until your team uses them.
Previously
Builder.ai·Fanvue
§ 06Contact
Tell me where the hours go.
Thirty minutes. Bring the list. We'll find the two or three ideas worth building.