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Synthetic Data for AI agents
We simulate expert-level reasoning and domain-specific processes to generate training data that builds true specialization into your models. Synth handles the cold start problem, covers the long tail of edge cases through engineered simulations, and runs entirely on-premise to keep sensitive data under your control.
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Fully Open Data for AI
We curate and structure the world's largest rights-cleared and provenance-based dataset for LLMs - government records, legal archives, scientific literature, multilingual sources - so you can plug it directly into your models, RAG pipelines, and MCP servers.
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AI-Native Tooling for Agents
Turn your messy siloed documents into a single, structured, compliant data asset for your agentic AI workflows. Your AI systems and agents get not only the right information but rich trustworthy context - higher accuracy on a wider range of processes. A built-in privacy firewall handles PII before anything leaves the secure zone. Deployable fully on-premise.
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BlogPleias trained a 600-million-parameter specialized model for RATP to detect and interpret safety signals in Parisian Subway users’ messages - combining a fully synthetic training pipeline and designed for on-premise deployment. After only three months of development, the model, beating closed models 200x times its size, is now in production at RATP’s sovereign infrastructure on Scaleway.
BlogToday at VivaTech, Pleias, in collaboration with NVIDIA, is releasing Nemotron-Personas-Belgium, a statistically grounded synthetic persona dataset covering the Belgian population at the level of regions, language communities, and communes. It is the second European dataset in the Nemotron Personas series, following Nemotron-Personas-France, announced in March 2026.
BlogThe most powerful models Europe lost access to this year happen to be called the Fable series. It is the kind of coincidence you cannot improve on, because the suspension did not create a European vulnerability so much as expose a fable Europe had been telling itself for the better part of the post-chatGPT boom: that it did not need to build the substrate of artificial intelligence, only to use it well. Own the application layer, the story went, and let others burn the capital underneath. When the layer underneath was switched off from Washington, the story switched off with it.
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Use CasePleias trained a 600-million-parameter specialized model for RATP to detect and interpret safety signals in Parisian Subway users’ messages - combining a fully synthetic training pipeline and designed for on-premise deployment. After only three months of development, the model, beating closed models 200x times its size, is now in production at RATP’s sovereign infrastructure on Scaleway.
Use CaseResearchers built an AI medical assistant that works without internet for health workers in rural West Africa. It runs on old Android phones and handles local languages to give workers quick treatment guidance.
Use CasePleias and SpineDAO are partnering to build AI systems that safely scale expert spine care for back pain, the world's leading cause of disability. The project tests whether small, structured-reasoning models can outperform large generic LLMs in high-stakes clinical settings.
