AI Strategy & Implementation
Identifying processes, designing the solution, implementing it, and training the team
Every company is experimenting with AI. Far fewer are turning it into real results.
We start with your business, not the model. We identify the processes where AI can create real value, design solutions around your team and your data, implement them with clear rules, and train your team to use, manage, and evolve them.
What changes for you
- Time back. Repetitive work is automated, freeing your team to focus on work that needs judgement.
- A clear map. You know where AI makes sense in your organisation, where it doesn’t, and what to tackle first.
- Control. Every use has an owner, a defined level of human oversight, and a clear way to pause or switch it off.
- Your data stays yours. Privacy and security are built into the solution from the start, not patched in later.
- A team that works with AI. AI becomes part of how your people work, rather than another tool they have to work around.
How we work
- Identify the processes. We map how your organisation works and find where AI can save time, improve quality, or create new opportunities, with a focus on practical, measurable value.
- Design the solution. We build around your team, your data, and the way you work, not around the latest tool.
- Implement it. From simple automations to AI agents built on large language models, we integrate solutions into the tools your team already uses. We monitor them in real use, with clear ways to correct, pause, or switch them off when needed.
- Train the team. We make sure your people know how to use, manage, and improve the solution. Through training, adjustment, and support, AI becomes part of everyday work rather than an experiment on the side.
Case Studies
IA Chatboot Assistant
The assistant on our website answers questions using our own content, in English, Spanish, and Catalan, with a clear source for every answer.
Our AI agents research and draft our articles, while our team reviews and approves everything before it is published. We recommend what we use ourselves first.
We use it ourselves
We run part of our own work on AI agents. Every night, on a dedicated machine, a chain of agents reads the sources we curate, researches them, and drafts content for our team to review.
Building and running them taught us what we now bring to clients: keep each agent’s task small and bounded, verify facts with simple rules rather than asking another model to check them, and decide where a human needs to step in before writing the first prompt.
