Useful AI, measured and under human control.
AI agents, document assistants, Wolof voice processing and workflow automation: we start from one measurable task and put it into production with guardrails.
Discuss this needExpertise connected to your challenges.
For organizations moving from scattered experiments to AI in production that is evaluated and governed.
- Repetitive tasks that tie up your teams.
- Information that is hard to find in your documents.
- AI trials without shared success criteria.
We start by understanding usage, constraints and a useful first scope.
What you receive.
Scope and success criteria agreed before work starts.
A prioritized use case
One task, its data, its risks and its evaluation criteria.
An agent or assistant in production
Integrated with your tools, with limited permissions and an action log.
Evaluation and governance
Test sets, monitoring and human-review rules.
How to get started.
Three engagement types, from the shortest to the longest. Durations are indicative and confirmed after discovery.
AI workshop
Identify and size the use cases worth building.
Evaluated pilot
One use case in real conditions, measured on representative scenarios.
Scale-up
Rollout, monitoring and continuous improvement.
Technologies
LLMs & RAG · Agents & orchestration · Speech recognition · Data engineering · Evaluation · Observability
Selected after discovery, based on your environment and maintenance needs.Before we begin.
Answers to the questions clients ask us most before getting started.
Another question?
Write to us and we will point you to the right expertise and a sensible first step.
Ask a question01Should every automation use AI?
No. A business rule or conventional integration is sometimes more reliable and cheaper. We choose the simplest tool that achieves the result.
02Will our data train public models?
No. We choose services and contracts that exclude this use, and hosting suited to the sensitivity of your data.
Projects in this practice.
From context to results, see how we delivered these projects.
View the case studyA Wolof voice assistant to plan harvests and sell better.
Growers report their harvests by voice message; AI structures the volumes and the union organizes collection and sales.
View the case studyOrders, stock and mobile payments in a single app.
An offline-first app for 38 shops, with mobile money payments reconciled automatically.
Keep exploring.
Our practices work together: a software project is built with cloud and security in mind.
You have the challenge. Let’s make a start.
One conversation is enough to clarify the need and the next useful step.