Discover
We discuss the business problem, intended users, current workflow, existing applications, available data, and constraints.
Typical output:A shared understanding of the problem and potential project boundary.
HOW WE WORK
Clear scope. Practical engineering. Defined delivery.
AI projects work best when the problem, workflow, and expected outcome are understood before implementation begins.
Our process is designed to move from an initial conversation to an agreed engineering scope, with testing and operational considerations built into the plan.
DOTENUM / OUR APPROACH01 → 05
THE PROCESS
Our process
Scope
We define the proposed solution, deliverables, dependencies, responsibilities, acceptance criteria, timeline, and commercial terms.
Typical output:An agreed project scope or proposal.
Build
We implement the agreed workflow, application, or integration and review progress against the scope.
Typical output:Working software and agreed development deliverables.
Validate
We test representative scenarios, integration behavior, error conditions, and cases requiring human review.
Typical output:Test findings and a review against the agreed acceptance criteria.
Deploy and hand over
We support the agreed deployment and provide the documentation, knowledge transfer, and post-launch arrangements included in the project.
Typical output:An agreed handover and ownership plan.
BEFORE A PROJECT STARTS
What we clarify before a project starts
- What problem are we solving?
- Who will use the solution?
- Which systems and data are involved?
- What must the solution do—and what is outside scope?
- How will the result be evaluated?
- Who owns approvals, access, deployment, and ongoing operations?
The answers shape the proposal. They also help determine whether AI is appropriate for the task.
LET’S TALK
Start with the problem.
You can contact us with a workflow description, a product requirement, or a technical brief. You do not need to arrive with a complete architecture.