The laboratory

Research, products, implementation

Founded in 2026, SYSNOT is an independent laboratory for applied AI research. Its work becomes products for researching, creating, organising knowledge and carrying out projects with AI.

The laboratory also helps companies adopt AI. Choosing the right tools, connecting them to existing systems, training teams and building useful applications are all part of that support. Consulting and implementation go hand in hand, with control over data as a guiding principle.

Three products, one shared principle

Sysnot Lab is the laboratory’s main product. It brings together an assistant, memory and tools to research information, create content and carry out work through several steps. Optimised for local AI, it runs on your own equipment and can also use leading providers’ services through their APIs.

Sysnot AI brings together notes, drawings, documents and voice on Android. AI helps you find information, summarise a conversation or prepare actions while keeping the original content. It runs on your device by default. The full version can also connect to the Sysnot Lab assistant.

Vibe Coder Copilot helps you follow projects built with AI. Its first dashboard brings together conversations, assistants’ work, code changes and server status. The product is in development, with the ambition to support projects from preparation to delivery.

Consulting and implementation

Architecture consulting: identify useful applications, examine organisational constraints and choose suitable models, tools and infrastructure. Decisions consider usefulness, data, costs and control over the system.

Implementation: connect AI to existing documents and tools, build automation and integrate agents into workflows. SYSNOT technologies can provide a foundation for trials and integrations, according to their maturity and the project’s needs.

Training and knowledge transfer: support teams in their day-to-day use, help them assess responses and understand model limitations. An integration delivers value when teams know how to use it and develop it further.

Research areas

The approach combines the study of models — neural networks, Transformer architectures and fine-tuning — with the design of systems that put them to work, particularly in local AI.

RAG, memory, execution harnesses and agentic loops structure the experiments: access to knowledge, continuity of context, tool coordination and control over actions. Prototypes provide a way to examine the behaviour and limits of the system as a whole.

An exploratory research area concerns the possible emergence of artificial consciousness. System continuity, representations of self and relationships with the environment are approached as research questions, with hypotheses to examine.

The Architect

Franck, known as l’Architect, leads SYSNOT’s work. An engineer by training and a specialist consultant, his background in systems and software architecture led him to applied research and AI architecture.

The laboratory turns this experience into products and support for organisations, using AI and agents to build them. Robotics extends this approach to the links between perception, reasoning and action.