02 · AI / LLM integration
Artificial intelligence on your own data.
Language models wired into the systems you already use, grounded in your information, with the quality controls that separate a demo from something that survives production.
A generic language model doesn’t know your products, your policies or your customer history. That’s why most AI pilots impress in the demo and disappoint in daily use: they answer well in general and badly exactly where your business is specific.
The real work isn’t calling an API, it’s building the layer around it: where the context comes from, how the right information is retrieved, what happens when the model doesn’t know, and how you measure whether an answer is acceptable. That layer is what turns AI into a feature rather than an experiment.
01 · AI / LLM integration
What’s included
Use case assessment
We review where AI adds measurable value and where a simple business rule solves it better. Not every problem needs a model.
RAG over your information
Retrieval-augmented generation: we index your documents, manuals, catalogues or knowledge base so the model answers with your data and can cite the source.
Integration with your systems
Connection to ERPs, CRMs, databases and existing APIs, respecting each user’s permissions and roles.
Fine-tuning when needed
Model tuning when tone, output format or a very specific domain can’t be solved with context alone.
Evaluation and quality control
A test set built from real cases to measure accuracy before and after every change, plus response logging to audit what the system answered.
02 · AI / LLM integration
When it makes sense
- Your team loses hours searching for information that already exists in internal documents.
- You get a lot of repeat questions that require checking several sources to answer.
- You ran an AI trial that worked in the demo but didn’t hold up in real use.
- You need AI to respect permissions: each user sees only what they should.
03 · AI / LLM integration
What we work with
- Models
- Claude, GPT, open models deployed on your own infrastructure
- Retrieval
- Vector databases, embeddings, hybrid search, reranking
- Quality
- Evaluation sets, response traceability, cost and latency control
04 · How we work
A short, transparent process with deliveries every two weeks.
Discovery
We map the process, the users and the expected return. Scope and metrics defined.
Design & architecture
Clickable prototype and validated technical decisions before writing code.
Iterative development
Two-week sprints, each ending with a demo. No surprises.
Launch & improve
Deployment, monitoring and product evolution driven by real data.
05 · FAQ
Frequently asked questions
No. We work with configurations where your data isn’t used for training, and when the case demands it we deploy open models inside your own infrastructure.
RAG gives the model your information at answer time, and suits data that changes. Fine-tuning adjusts the model’s behaviour, and is for locking in format, tone or a very particular domain. Most cases are solved with RAG, sometimes with both.
With retrieval that forces answers to rest on citable sources, instructions to declare when there isn’t enough information, and an evaluation set of real cases that runs on every change.
Yes. AI builds on your existing systems through their APIs or databases; it doesn’t replace them or force a migration.