Yameveo designs and builds multi-agent AI systems for companies that need working, production-grade AI rather than prototypes — through custom LLM integration, retrieval-augmented generation (RAG) pipelines, and agentic workflows built on LangChain, Ollama, DeepSeek, Llama, and Qwen. We apply this same stack in the VANTAGE EU cybersecurity project, where we build AI modules for automated vulnerability assessment and SOC automation, so the patterns we deliver to clients are proven on a real, funded engineering programme.
What can Yameveo actually build with AI?
We build multi-agent AI architectures, RAG pipelines, and LLM integrations into existing enterprise systems. Concretely, that means agentic workflows where multiple specialised AI agents coordinate on a task, retrieval-augmented generation that grounds model answers in your own documents and data, and integration of open and hosted models (Llama, Qwen, DeepSeek, and others via Ollama and LangChain) into the applications and processes you already run. Our focus is engineering AI that ships and holds up in production, not one-off demos.
Do you use open-source models or send our data to third parties?
We work primarily with open-weight models that can run on infrastructure you control — Llama, Qwen, and DeepSeek served through Ollama — which means your data does not have to leave your environment for inference. This matters for regulated sectors and for teams that cannot send sensitive data to a third-party API. Where a hosted model is genuinely the better fit for a use case, we will say so and design the data flow explicitly rather than defaulting to it.
How does AI change cybersecurity work like vulnerability assessment?
Multi-agent AI can automate large parts of vulnerability assessment and routine SOC tasks that were previously manual, which is exactly the ground we work on in VANTAGE. In that EU Digital Europe project (€7.8M, 14 partners across 6 countries, 36 months, with Yameveo leading Work Package 5), we build AI modules for automated vulnerability assessment and SOC automation. That work directly informs the agentic and RAG patterns we bring to client AI engagements. 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 integrate AI as a layer on top of your existing stack rather than asking you to replace it. That means adding LLM-powered features, RAG over your existing data sources, or agentic automation into the applications and workflows already in place, with the model layer designed so it can be swapped or upgraded without rewriting the surrounding system. Our engineering work in VANTAGE centres on exactly this kind of integration — connecting AI modules to a wider platform through clean APIs.
What does an AI engagement with Yameveo look like?
Engagements start by defining the specific outcome you need and then designing 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. Because Yameveo is an engineering firm led by its founder, the same people who scope the work also build it.
We apply the same engineering to identity data: Yameveo Analytics for CIAM (SAP CDC).
Frequently asked questions
Which AI models and frameworks do you work with?
We build primarily with LangChain for orchestration and Ollama for running open-weight models such as Llama, Qwen, and DeepSeek. We can integrate hosted models where that is the right choice for a use case, and we design the architecture so the model layer can be changed later without reworking the rest of the system.
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 14 partners across 6 countries, where we lead Work Package 5 and build AI modules for automated vulnerability assessment and SOC automation. The multi-agent and RAG patterns we develop there carry directly into our client AI work.
Do you build production systems or just proof-of-concept demos?
We build for production. Our aim is to hand over AI systems you can run, maintain, and integrate with your existing stack — RAG pipelines, agent workflows, and LLM features that hold up with real data rather than staying at the demo stage.
Have an AI problem you want engineered properly rather than prototyped? Enrico Aillaud, Yameveo’s founder and lead engineer, is the person you’ll talk to — see his full profile. Discuss your AI project →