A defined outcome
We agree the question, inputs, deliverables and what completion means before building.
Services / Overview
What working with
Kove looks like.
Start with a business question. Agree a focused engagement. Build capability your team can use.
From first question to handover
The engagement
journey
Your proposal sets the stages, outputs and follow-up for your engagement. This is the path from the first question to a workflow your team can run.
Capability that stays with your team.
- Stage 01
Signal
Understand the decision, the current workflow and the information already available.
You leave withA shared problem statement and an initial view of the data.
- Stage 02
Scope
Choose a useful starting point and agree the outputs, boundaries and review responsibilities.
You leave withA proposal describing the work and what completion means.
- Stage 03
Prove
Try the workflow on an agreed sample and compare its output with source information.
You leave withA reviewable example, with assumptions and exceptions visible.
- Stage 04
Wire
Where the scope calls for it, make the workflow repeatable through structured inputs or agreed connections.
You leave withA defined route from source information to output.
- Stage 05
Run
Work through the process with the people who will use it and agree what needs checking.
You leave withPractical operating instructions and human review points.
- Stage 06
Hand back
Build the client team’s confidence through explanation, practice and a clear handover.
You leave withCapability the team can continue developing, with any follow-up agreed in scope.
The engagement commitments
Know what you are agreeing to.
A written fee
Scope and pricing are confirmed upfront. Additional requirements are agreed separately.
A usable handover
Your team receives the agreed workflow, documentation and a walkthrough, with review responsibilities made clear.
Capability that stays
Build AI Champions
inside your business.
Our aim is to work ourselves out of a job. That takes someone inside your business who can run the workflow, question its output and help colleagues use it well.
We help you identify an AI Champion close to the work and build their confidence through hands-on practice. Give them time, access to the right people and a clear route to the decision-maker.
Curiosity with a purpose
Start with a recurring business problem. Ask what decision would improve before choosing a tool.
Judgment before acceptance
Challenge plausible answers. Check sources, assumptions and exceptions, and know when to pause for review.
Care with information
Use agreed accounts and access controls. Understand which information belongs in the workflow and who may see it.
A habit of small experiments
Test one change against a known example. Record what improved, what failed and what still needs checking.
Generosity with knowledge
Show a colleague how the work runs. Keep instructions understandable and build a backup, so one person never becomes the bottleneck.
Ownership of the outcome
Measure the effort to reach an accepted result. Keep the method current and raise changes that affect its reliability.
Practise it. Explain it. Run it again.
We work through a real task together, then let your Champion lead the next run. The handover should include input requirements, review checks, exception handling and instructions the team can maintain. We agree follow-up support in the scope, including 30, 60 and 90-day check-ins where useful.
The test of self-sufficiency: your team can run the next cycle, explain the result, recognise a problem and decide when further help is needed.
Choose a starting point
Automate
One use case, built from existing files, with a fixed scope.
Integrate
Bring multiple inputs together through exports or agreed source-system connections.
Operate
A fractional resource on an agreed recurring retainer.
Your next step
Bring us one piece of work
you want to improve.
Tell us what takes time, who uses the result and what a better outcome would look like.