Regulated industries
For teams whose data can't leave the environment.
The data can't leave, so the model has to come to it
Regulation, customer contracts or classification decide that, not you. Cloud model APIs are a non-starter regardless of price.
The model has to run on approved hardware inside your boundary, and keep working without a connection out.
Closed models don't ship weights
The best proprietary quality is simply unavailable inside your boundary, at any price.
Pulling open weights off the hub gives you a model, but not accuracy on your task, not a serving stack for your hardware, and not a path to retraining as the workload drifts.
Trained and served inside your boundary
We train specialized models that deploy entirely inside your environment: your VPC, on-premise or air-gapped, with edge and on-device options down to 100M parameters.
Retraining runs from your own traces on your own hardware, with approval workflows before anything ships. Nothing leaves your environment.
Where it applies
- Query generation over internal data. Natural language to queries against your own systems. This is the Rocketgraph deployment below, running air-gapped on IBM Power.
- Threat and log classification. Security telemetry classified on-premise. Octodet runs this at 88% accuracy against 63% for the LLM baseline.
- PII redaction. Strip personal data without sending it to a third party first.
- Internal copilots. Assistants over documents and code that never leave the boundary they're indexed in.
- Document processing on-premise. The same extraction and classification workloads, on hardware you control.
In production
Rocketgraph
Rocketgraph ships AI-powered query generation inside an air-gapped, self-contained deployment: the distil labs SLM and their graph platform run side by side on the same IBM Power hardware.
Read the Rocketgraph case study →“For customers in regulated industries, this means AI-powered query generation with complete data privacy. Nothing ever leaves their environment.”
Octodet
Octodet analyzes cybersecurity logs with a distil labs model that runs entirely on-premise to meet strict privacy requirements.
88% vs 63%
threat classification accuracy, fine-tuned SLM vs the LLM baseline
30x
smaller than the LLM it outperforms
On-premise
deployed inside the customer environment
Find out what your workload should cost
We identify where you are overspending, evaluate the optimizations available, and show you the lowest-cost configuration that meets your quality bar. It starts with an export or one day of traffic.