Applied Generative AI
We build systems that combine language models with your internal documentation to answer precise questions about your company. No hallucinations — with citations from the original source.
Your team gets access to an assistant that knows your processes, policies, product catalogue and past cases. Like a senior expert available at all times, for everyone on the team.
From product descriptions to commercial reports — we generate consistent content in your brand voice, without wasting time on repetitive writing tasks.
When generic models are not enough, we fine-tune models on your data to get more precise responses aligned with your sector vocabulary and specific use cases.
Extraction, classification and synthesis of information from contracts, invoices, reports and any unstructured document. What used to take hours now takes seconds.
We implement automatic evaluation systems so you know, at any moment, whether the model is performing as expected. AI without measurement is blind faith.
01
We identify the specific problem generative AI can solve. We prioritise by business impact, not technological appeal.
02
In 1–2 weeks we build a functional prototype with your real data. We evaluate feasibility before committing a large budget.
03
We measure output quality, adjust prompts, retrieval or models based on real results. Without assuming the first attempt is the right one.
04
We deploy the system integrated into your tools, with performance monitoring and complete documentation for your team.
Yes, when implemented correctly. We work with models that can be deployed on-premise or in private cloud environments, and design the architecture so your data does not reach third parties without your explicit control.
Depending on the case: OpenAI GPT-4, Claude, Mistral or open-source models. We evaluate each option based on cost, privacy and performance for your specific case. No fixed preference.
A functional RAG system over your documentation can be ready in 3–4 weeks. Complexity scales with data volume, document quality and required integrations.
With the correct architecture, significantly less than generic models. RAG systems respond based on your real documentation and cite the source. When they do not have an answer, they say so.