Some data can’t be sent to a cloud model.
Customer contracts, data-residency rules and confidential engineering data often rule out public AI services. With the model on your own GPUs, the same use cases can still go into production.
- Data-residency rules for your country or sector
- Customer contracts that keep data on-site
- Drawings, manuals and specifications that are core IP
- Fixed hardware cost instead of per-token pricing
The whole stack, on your hardware.
We choose and serve open-source LLMs on your NVIDIA GPUs.
- Model selection tested on your documents
- GPU sizing for your users and workload
- Local embedding models and vector index
Retrieval over your documents, and agents that work with your internal systems.
- Ingestion, OCR and chunking
- Answers that cite document and page
- Agents with approved access to ERP and other systems
Quality, monitoring and updates, all inside your network.
- Evaluation sets agreed at the start
- Latency, usage and quality monitoring
- Model updates tested before rollout
Start in the cloud, go live on your servers.
We build the use case on AWS or your cloud platform with documents that are cleared for it, so your team can test it on real work.
We move the same pipeline to an NVIDIA GPU server in your environment and replace the cloud model with an open-source LLM. The evaluation set is re-run to confirm quality before go-live.
If no data can leave from day one, we start directly on your hardware.
Cloud MVP, on-prem production
We built Tracium’s document intelligence pipeline this way.
Questions we hear
Which models do you run?+
Open-source LLMs, for example from the Llama, Mistral or Qwen families. We test candidates on your documents and choose the one that meets the quality target on your hardware.
What hardware do we need?+
That depends on model size, number of users and response times. Tracium’s production deployment runs on an NVIDIA RTX PRO 6000 Blackwell GPU. We size the server with you during the cloud phase.
Does anything leave our network?+
No documents, prompts or answers go to an outside AI service. The model, embeddings and vector index all run on your servers.
Can we start in the cloud?+
Yes. We can build the first version in the cloud on non-sensitive data, then move the same pipeline to your servers. That is how the Tracium project ran.
Who owns it?+
Everything we build for you is yours: pipelines, configuration, infrastructure code and documentation, on your servers and in your repositories. Our agents and accelerators come with a licence that keeps working even if you stop working with us. If you need full source access, we offer that too.
Need AI on data that can’t leave your building?
Tell us the use case and where the data has to stay.