Engineering from Morocco · For your next stage
Make your next workflow less manual.
Repetitive work is a useful starting point for automation when the inputs, decisions and exceptions are understood. OSIS helps businesses explore AI engineering and connected workflows, from information retrieval to operational handoffs. The first question is which task should change and how your team will know that the change works.
Discuss your project01 / THE APPROACH
Choose a process with a measurable outcome
Map the process before adding AI: where information comes from, who acts on it, what rules apply and which exceptions require judgment. A document intake workflow may need extraction and review, while a repetitive system update may only need a reliable integration. We discuss whether conventional automation, AI assistance or a combination is appropriate for the task.
- Document and information workflows with human review
- Knowledge retrieval over approved business information
- Connected operational workflows across existing tools
02 / THE APPROACH
Keep access, decisions and exceptions explicit
An AI integration should have a defined boundary. Decide which records it can read, which actions it can suggest and which actions require approval. For a system that updates business data, permissions and auditability matter as much as the model response. Sensitive workflows should have a clear escalation path when information is incomplete or a result cannot be verified.
- Access rules and the scope of available business data
- Evaluation examples for normal cases and exceptions
- Human approval for consequential actions and a fallback process
03 / THE APPROACH
Pilot before expanding
Start with a limited workflow and representative examples. Compare the current process with the proposed process using criteria such as completion time, correction rate and manual intervention. Define those criteria with your team before the pilot. Expansion should follow evidence from the workflow, including the cost of review and maintenance, rather than a promised percentage improvement.
- A discovery discussion around the current process
- A focused pilot with agreed success criteria
- Integration and operational handover based on the pilot findings
Before we build
Does every automation project need AI?
No. Stable rules and structured inputs may be better served by conventional software integrations. AI is worth evaluating when the task involves language, unstructured information or variable inputs, and its outputs can be checked.
Can an AI tool work with our existing systems?
Integration depends on the APIs, permissions, data formats and policies of those systems. Bring the names of the tools involved and an example of the workflow so we can assess the practical constraints.
How do we evaluate whether a pilot is worthwhile?
Agree on a baseline and a small set of acceptance criteria before building. Include the time spent reviewing outputs, handling failures and maintaining the integration when comparing the pilot with the current process.
Read before your next decision
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Bring the problem. We’ll discuss the scope.
Tell us about your users, your current process and the outcome you need. We can use the first conversation to identify requirements and practical next steps.