Service
Data, automation, and AI enablement
Reliable numbers first, then automation where it pays, then AI only where it holds up under scrutiny.
The problem
Things we hear in the first call.
“Three departments report three different revenue figures.”
“Two people spend every Friday copying data between systems.”
“Leadership wants an AI plan and nobody knows where to start.”
What we do
Reporting foundation
One agreed definition per metric, one source per number, and reporting that does not need a spreadsheet to be believed.
Integration and automation
The manual handoffs between systems replaced with something monitored, in the order of how much handling each one costs you.
Data governance
Who may see what, how long it is kept, and where it may not go. Written before the tools are opened up.
AI use-case triage
Candidate uses sorted by value and by risk, with the ones that should not proceed named and explained.
Pilot and measurement
A narrow pilot with a success measure agreed in advance and a decision gate at the end.
Enablement
Guidance and training so staff know which tools are approved, what may be entered into them, and who to ask.
How an engagement runs
Durations are typical for a company of 10 to 250 people. Scope is confirmed before anything starts.
Discovery
Sources, definitions, and the manual work people do to bridge them. Output is a map and a shortlist.
Foundation
Fix the definitions and the pipelines under the reports leadership already uses, so later work rests on something stable.
Pilot
One automation or one AI use case, scoped narrowly, measured against the agreed criteria.
Scale or stop
If it worked, it is documented, handed over, and extended. If it did not, we stop and say why.
What you get
Documents and access you keep, whether or not the engagement continues.
- Data source and definition map
- One agreed metric set for leadership
- Automation inventory with the manual steps each removes
- Governance rules for sensitive data in AI tools
- Pilot report with a stop-or-scale recommendation
- Staff guidance for approved tools
Common questions
Usually it is the constraint. Most disappointing results trace back to inconsistent definitions and missing history rather than to the model. That is why the foundation work comes first.
It depends entirely on the contract and configuration, which is why we review both before recommending anything. Where a use case would require sending regulated data to a provider you cannot bind, we say no.
Whichever fits the workflow and the data rules. We are vendor-neutral and take no commission, so we are as willing to tell you a feature you already pay for covers it.
Almost always. Integration and reporting work sits on top of what you run today. Replacing a core system is a separate decision with its own case, and we will not smuggle it in.
Your team, with the documentation to do it. Each automation is handed over with what it does, what it touches, how it fails, and who to call. Anything we cannot document simply enough to hand over is a sign it was the wrong thing to build.
Start with the numbers you can trust.
A 30-minute call, no obligation. Send a request and we confirm a time by email within one business day.
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