We deploy on-premises AI systems that match cloud API accuracy for regulated industries (PCI/DSS/HIPAA). Our methodology transfers frontier leaders intelligence into local models — without sending a single byte of sensitive data to the cloud.
We transfer frontier intelligence into on-premises models through structured prompt engineering — no fine-tuning, no weight manipulation, no data leaving your network.
Run your domain task through a frontier cloud API and the target on-prem model using the same evaluation rubric. We use sanitized, synthetic test data — no real PII ever touches the cloud.
Systematically identify where the local model disagrees with the frontier model by more than an acceptable threshold. These aren't random errors — they're consistent blind spots.
Analyze the frontier model's signals that the local model missed. Gambling detection? Income contamination? Document fraud patterns? Each gap becomes a named, addressable deficiency.
Write a model-specific calibration checklist — explicit verification steps the local model must perform. This is the knowledge transfer artifact: plain text, fully auditable, no PII.
Re-run the local model with the supplemental rubric and confirm agreement reaches the target threshold. Push the rubric to the client appliance. Repeat when new models emerge.
The performance gap between frontier and on-prem models was primarily about what to look for, not about reasoning capability.
The supplemental rubric is a plain-text document — typically 1-2 pages of structured verification instructions. It contains zero client data, zero model weights, zero proprietary information. Only domain-specific evaluation criteria.
This is the only artifact that crosses the network boundary. Your data stays on your hardware. Always.
Choose the deployment model that matches your compliance requirements and operational preferences. Every option includes our continuous retraining pipeline.
GPU appliance hosted in our facility with a dedicated, IP-restricted network path to your infrastructure. Documented data flow. Zero egress beyond your whitelist.
Physical appliance deployed in your data center. Fully air-gapped or with a narrow, authenticated channel for rubric-only updates. Maximum compliance posture.
Multi-appliance deployments, custom domain development, API integration with your existing systems, and dedicated retraining pipeline with your edge cases.
Cloud AI APIs deliver exceptional results — but PCI, HIPAA, and data residency requirements prohibit sending sensitive records to third-party endpoints. Running open-source models locally seems like the answer, until you see the accuracy gap.
PCI-DSS, HIPAA, SOC 2, GDPR — each framework restricts how and where sensitive data can be processed. Cloud APIs, no matter how secure, create audit and liability exposure that many organizations simply cannot accept.
Cloud AI providers can revoke access without warning. Lending, licensed medical distributors, regulated gaming platforms — entire verticals get deplatformed when provider risk policies shift. On-premises models eliminate that dependency entirely.
Traditional model distillation requires massive labeled datasets, ML engineering teams, and months of iteration. For specialized domains like financial screening or medical coding, that expertise rarely exists in-house.
Validated on production financial screening cases against Claude Sonnet 4.6 baseline. The supplemental rubric quadrupled underwriting match rates and eliminated the local model's optimistic scoring bias.
| Metric | Before | After |
|---|---|---|
| Avg diff vs frontier | +1.6 | −0.43 |
| Model scored higher | 75% | 13% |
| Model scored lower | 3% | 28% |
| Outliers (diff > 2) | >20 | 2 |
After calibration, the on-prem model leans slightly conservative (−0.43 avg). This is the preferred direction for regulated screening:
FrostWeb has been operating regulated-compliant hosting infrastructure for years. Our AI practice is built on peer-reviewed research and production deployments — not pitch decks.
Tell us about your use case and compliance requirements. We'll assess whether distillation-by-prompt is a fit and outline a deployment path — typically within 48 hours.
No sales deck. No generic demo. We start with your specific regulatory constraints and work backward to a solution.
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