Use case · AI agents
Agents that answer from your data, not about it.
Build assistants and agents grounded in your own governed tables: retrieval over the lakehouse, open models fine-tuned on your corpus, and private endpoints in your region. No prompt, no document, no embedding ever leaves your perimeter.
Why Polnor
Why us for this.
Grounded in the lakehouse
Retrieval pipelines run over governed Iceberg tables, so answers cite data you actually trust, with lineage to prove it.
Models you control
Pull open models from the Hugging Face Hub, fine-tune them on your GPUs, and serve them behind your own endpoints.
Every action audited
Agent calls are API calls: logged, scoped to the workspace, and traceable like any other access.
EU jurisdiction end to end
Prompts, context and outputs stay on your European infrastructure. There is no US AI cloud in the loop.
What you can build
Internal copilots
Assistants over your docs, tickets and tables for support, sales and ops teams.
Document Q&A
Ask questions across contracts, reports and knowledge bases, with sources.
Workflow agents
Agents that query, decide and trigger jobs through the API, under quotas you set.
Set a course
Request a
demo.
See your own Iceberg tables, warehouses and notebooks running on your European cloud, usually within a week.
hello@polnor.net · OVHcloud GRA9, France 🇫🇷