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.
- 1,000+hours a year saved
- Across finance, operations and compliance teams
- First tools live in under three months
- Built inside strict governance
Hours Ledger · Sheet 1
1,000+
hours a year saved — time measured before the build; teams report the work now takes 50–80% less.
§ 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
A European financial services firm.
MemorandumCase study 01
ClientFinancial services, Europe
FootprintMulti-office · cross-functional
Engagement[ ongoing ]
1 · Situation
Skilled finance, operations and compliance teams were spending too much of their week on manual checking, re-keying and chasing information between systems — while every new initiative competed for the same few senior people.
2 · What we measured
Structured discovery with each team to map where the time really goes, then a single prioritised pipeline of opportunities, each with an owner and a measured time cost, ranked by value, effort and risk.
3 · What we built
- LiveRegulatory and policy monitoring, so changes reach the right people faster.
- LiveFinance process automation, with clear review steps built in.
- LiveStructured intake workflows that replace email chains.
- With the teamScaling a proven team process across the wider business.
- In scopingFurther automation in audit and reporting.
4 · Result
1,000+hours a year saved
50–80%less time on the work, as reported by the teams
Tools in daily use, owned by the teams that run them, and a growing number of colleagues building their own.
5 · Governance
Delivered within the firm's AI policy and security review. A person checks every output before it is used, and data stays inside the firm's own environment.
Time measured before the build. Reductions as reported by the teams.Client name withheld.
§ 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.