Applied AI — to real processes, not to shiny demos.
The AI conversation is full of demos and light on return. We start where there's already pain: a slow process, a funnel that drops, a repetitive task without criteria. We apply AI when it truly adds value and with a human in the loop where it belongs.
Cases where it fits
Internal assistant
Over your documentation, processes and data. Answers your team with real context, not generic.
Automatic qualification
Classifies leads, opportunities or tickets by business criteria, not keywords.
Assisted generation
Proposals, summaries, responses or descriptions created with criteria and reviewed by human.
Data extraction
From emails, PDFs, forms or transcripts — to structured data ready for your system.
Analysis and reporting
Pattern detection, early alerts and useful explanations about what the data shows.
LLM workflows
Reasoning chains where AI decides, acts and calls tools within the process.
How we approach it
Specific cases, not promises
We start with the process that consumes time today and adds nothing. If AI adds value there, we apply it. If not, we say no.
With a human in the loop where it belongs
AI alone never decides on sensitive matters. Human review where required; full automation where not.
Over your data, not generic
Useful assistants know your context: your product, your documentation, how you talk to your client.
With return metrics
Before starting, we define what to measure so we know if the system adds value or not. Then we measure.
What process do you want to accelerate with AI?
Tell us the specific case. If it makes sense to apply AI, we propose scope and measurement. If it doesn't, we also say so.