We begin with a decision, a report or a workflow that needs to work better. Together we define what success means, build the solution and choose how it will be operated.
Each stage has an agreed outcome. We establish what is needed before committing to the next stage.
We identify the users, the decision or task, and the result that would make a difference. We review data sources, access, current effort and constraints before proposing the work.
Your outcome: a defined deliverable, responsibilities and a practical recommendation for the next step.
We implement the agreed data flow, report, integration or application. Your team reviews usable increments. Acceptance checks cover the important calculations, user tasks, failure cases and permissions for the particular solution.
Your outcome: an accepted solution, documentation and a clear plan for ongoing operation.
If DIVU operates the service, we deliver on the agreed schedule, monitor defined checks, handle agreed exceptions and review improvements. Your team retains the business decisions and approvals assigned to it.
Your outcome: recurring delivery with named responsibilities, or a documented handover to your team.
The right model depends on your team, your systems and the work involved. We discuss ownership and continuity during scoping, so responsibilities are clear from the start.
A defined recurring output, agreed operating tasks, a named contact and regular review. Software and AI support delivery; DIVU remains responsible for the agreed service. Scope, service hours and change arrangements are set in the engagement.
We prepare documentation, explain the workflow and agree the practical handover. Your team receives the access and materials defined in the engagement. Any further support or improvements can be scoped separately.
A useful service agreement connects the business output with the practical operating conditions. These are the points we work through together.
We choose AI for tasks where it adds value and its output can be assessed. The level of review depends on the use case and the consequences of an error.
Extraction, classification and draft commentary may benefit from AI. Established calculations and rules need a reliable, traceable implementation.
We agree suitable examples, quality checks and the point at which a person needs to review or approve an output.
Data sources, systems and models can change. Relevant checks and maintenance responsibilities belong in the service scope.
Tell us who needs the result, how the work runs today and what should improve. We will help turn that into a sensible scope.