From an operational problem to a real solution
At Possition we do not start with code. First we look at the process. Then we define what to automate, how, and in what order. That way the final system is what your company needs to operate better.
Stages of the work
- 01
Diagnostic
We analyze the problem or process you want to improve and assess whether it makes sense to automate it or build a system.
On your side: You receive a concrete assessment of viability and approach.
- 02
Discovery
We document how the process works today: who is involved, which systems are used, and where the bottlenecks are.
On your side: We need access to the people who run the process.
- 03
Prioritization
We define which part of the problem should be solved first so visible results arrive as soon as possible.
On your side: You validate the order of priorities with us.
- 04
Functional design
We specify what the system will do: screens, flows, rules, and use cases, in a document you can understand.
On your side: You approve the scope before development starts.
- 05
Architecture
We design the technical components, the data, the integrations, and the security of the solution.
On your side: You receive a description of the technical solution and its operating costs.
- 06
Development
We build the system iteratively, showing working progress in each delivery.
On your side: You see real progress, not percentage reports.
- 07
Testing
We validate features, integrations, and performance with real process data.
On your side: You take part in testing with your team.
- 08
Implementation
We deploy the system to production and connect it to day-to-day operations.
On your side: The system is running in your operation.
- 09
Training
We train the people who will use and administer the system, with documentation included.
On your side: Your team is able to operate the solution.
- 10
Evolution
We measure results, fix issues, and add features according to real usage.
On your side: We define the improvements of each cycle together.
Scope and result
How scope is validated
Scope is written down before development: screens, flows, rules, and success criteria. You approve it; later changes are agreed, they do not appear on the invoice.
How the result is measured
We define the metrics before we implement: hours saved, errors avoided, response times. The system evolves with those data, not with impressions.
How we decide where to apply AI
We don't start from the technology: we start from the expected impact on capacity, cost, productivity, and profitability. If the analysis doesn't show a real improvement, we don't recommend it. Once implemented, we keep testing with the same logic: lower the cost per result, not add technology for its own sake.
We go deeper on this in Green AI →Tell us which process you'd like to improve
We'll look at how it works today and send back a concrete diagnostic: what to automate, how, and what result to expect. No cost, no commitment.