Concrete use cases
We solve a real problem first. The technology comes afterwards.
AI isn't a product, it's a capability. And like every capability, it only adds value when integrated into a real process. Forget generic chatbots, we talk about AI applied to your operations.
Automatic document classification, structured data extraction, content generation, semantic search over your internal knowledge. Integrated where you need it.
If any of these sounds familiar, we can probably help.
We receive hundreds of documents a month and someone has to read and classify them one by one.
We want to use AI but we don't know where to start without falling into the generic chatbot.
We have huge volumes of internal data that nobody searches because it takes half an hour to find anything.
We tried ChatGPT for something concrete and it half works, but we don't know how to industrialize it.
We're worried about privacy and we don't want to send our data to a public model.
We need AI to decide, not just suggest, and that means integrating it into our processes.
We solve a real problem first. The technology comes afterwards.
OpenAI, Anthropic, Google, local models. We use whatever fits best.
Architectures designed to keep your data where you want it.
Objective evaluations to know whether the model actually works in your case.
Three clear phases from the first call until the system is running in your operation.
Phase 01
We talk to your team and map real workflows, bottlenecks and priorities. We leave with a clear picture of what to solve first.
Phase 02
Architecture, integrations with your current systems, risks and a phased plan. Before writing any serious code, we agree on the how.
Phase 03
Controlled rollout, team training, monitoring and early adjustments. We stay with you during the first weeks until everything runs on its own.
Concrete outcomes. Not slides — things that run.
Both. The decision depends on the case: data privacy, acceptable latency and expected volume. Sometimes cloud wins on quality, sometimes local wins on sensitive data or long-term cost.
We define objective metrics at the start of the project (accuracy, recall, human-acceptance rate, cost per call) and build an automated evaluation suite that runs with every change.
No, unless you explicitly decide otherwise. We use providers with clear no-training-on-customer-data policies, or local models when privacy is critical.
Yes. Via prompt engineering, RAG over your documentation, or fine-tuning when the case justifies it. We pick the cheapest technique that solves the problem, not the most sophisticated one.
Web apps, mobile apps and tailored tools. Built within monthly capacity, not as a separate quote every time.
See service →Lightweight ERPs, CRMs, panels and internal tools that match how your company works — not a generic product.
See service →We connect the systems you already use so information flows without manual re-entry or endless patches.
See service →If this is an ongoing need, a Suto plan may fit better than a closed project. We will tell you honestly which option makes sense.
Talk to Suto