Yameveo designs and builds multi-agent AI systems for companies that need AI that works in production, not prototypes. We do this through custom LLM integration, retrieval-augmented generation (RAG) pipelines, and agentic workflows built on LangChain, Ollama, DeepSeek, Llama, and Qwen. We use the same stack in the VANTAGE EU cybersecurity project, where we lead WP5. There we build AI pipelines that run locally, so project data never leaves the EU.
What can Yameveo actually build with AI?
We build multi-agent AI architectures, RAG pipelines, and LLM integrations into existing enterprise systems. In practice that means agentic workflows where several specialised AI agents coordinate on one task, retrieval-augmented generation that grounds model answers in your own documents and data, and open or hosted models (Llama, Qwen, DeepSeek, and others via Ollama and LangChain) wired into the applications and processes you already run.
Do you use open-source models or send our data to third parties?
We work mainly with open-weight models that can run on infrastructure you control (Llama, Qwen, and DeepSeek, served through Ollama), so your data does not have to leave your environment for inference. This matters in regulated sectors and for teams that cannot send sensitive data to a third-party API. When a hosted model really is the better fit for a use case, we will say so, and we design the data flow explicitly instead of using a hosted API by default.
How does AI change cybersecurity work like vulnerability assessment?
Multi-agent AI can automate large parts of vulnerability assessment and routine SOC tasks that used to be done by hand. That is the ground we work on in VANTAGE. In that EU Digital Europe project (€7.8M, 13 partners across 8 countries, 36 months, with Yameveo leading Work Package 5), we are building the assets that the project’s AI will be built on. You can read more about the project on our blog: Yameveo blog: VANTAGE updates.
How do you fit AI into the systems we already run?
We add AI as a layer on top of your existing stack instead of asking you to replace it. That can mean LLM-powered features, RAG over the data sources you already have, or agentic automation inside the applications and workflows already in place. We design the model layer so it can be swapped or upgraded without rewriting the system around it. Our engineering work in VANTAGE centres on the same kind of integration: connecting the project’s AI to the wider platform through clean APIs.
What does an AI engagement with Yameveo look like?
Engagements start by defining the specific outcome you need. Then we design the smallest AI architecture that delivers it. From there we build and integrate the RAG pipeline, agent workflow, or LLM feature, test it against your real data, and hand over something you can run and maintain.
Frequently asked questions
Which AI models and frameworks do you work with?
We run the pipeline locally: Ollama for inference with open-weight models (Llama, Qwen, DeepSeek), and LangChain to orchestrate the agents and RAG over your own documents. Hosted models can be added where a use case needs one, and the model layer can be swapped without reworking the rest.
Can you run AI on our own infrastructure instead of a cloud API?
Yes. Because we work with open-weight models served through Ollama, inference can run on infrastructure you control, so sensitive data does not have to leave your environment. This is often the deciding factor for regulated industries and teams with strict data-handling requirements.
What experience do you have applying AI to cybersecurity?
Yameveo is a partner in VANTAGE, an €7.8M EU Digital Europe cybersecurity project with 13 partners across 8 countries, where we lead Work Package 5. There we build AI pipelines that run locally, so project data never leaves the EU.
Do you build production systems or just proof-of-concept demos?
We build for production. The aim is to hand over AI systems you can run, maintain, and connect to your existing stack: RAG pipelines, agent workflows, and LLM features that hold up with real data instead of stopping at the demo stage.
If you have an AI problem you want engineered properly rather than prototyped, you’ll talk to Enrico Aillaud, Yameveo’s founder and lead engineer. See his full profile. Discuss your AI project →