Secure Model Training
On Your Confidential Data
Train AI that truly understands your business—using your internal documents, customer records, and proprietary knowledge—without ever exposing that data to the public internet or third-party services.
Why Public AI Services Are a Risk
Data Exposure Risk
When employees use ChatGPT, Claude, or other public AI services with company data, that information may be used to train future models. Your confidential strategies, customer lists, financial data, and trade secrets could become part of a model that your competitors also use. Samsung, Apple, and JPMorgan have all banned employee use of public AI for this reason.
Compliance Violations
Sending customer PII, health records, or financial data to external AI services may violate GDPR, HIPAA, PDPA, SOC2, and industry regulations.
Generic Responses
Public models don't know your products, processes, or terminology. They give generic answers that require extensive manual correction.
API Dependency
Relying on external APIs means outages, rate limits, and price changes are outside your control. Critical business functions shouldn't depend on external services.
Escalating Costs
API usage costs compound quickly at enterprise scale. A privately deployed model has predictable infrastructure costs without per-token charges.
⚡ Quick Quote Request
Tell us about your project and get a response within 24 hours.
AI That Stays Within Your Walls
We deploy and fine-tune AI models entirely within your infrastructure. Your data never leaves your network.
On-Premise Deployment
We install and configure AI infrastructure on your own servers. All data processing, training, and inference happens within your data center. No cloud connectivity required.
Air-Gapped Systems
For the most sensitive environments, we deploy completely isolated systems with zero network connectivity. Data transfer via secure physical media only. Used by defense, government, and high-security financial institutions.
Choose Your Security Level
| FEATURE | PRIVATE VPC | ON-PREMISE | AIR-GAPPED |
|---|---|---|---|
| Data leaves your network | No (isolated VPC) | No | No |
| Internet connectivity | Restricted outbound | Optional | None (physical isolation) |
| Hardware location | Cloud provider DC | Your facility | Your secure facility |
| Setup time | 1-2 weeks | 3-4 weeks | 4-6 weeks |
| Compliance level | SOC2, GDPR, PDPA | + HIPAA, PCI DSS | + Government, Defense |
| Starting price | $8,000/mo | $15,000/mo | $25,000/mo |
What Can You Train On?
Any internal data that would make AI more useful for your business—safely.
Military-grade security for enterprise AI
The same encryption banks use, applied to machine learning. Your competitive advantages stay protected.
Internal Documents
Policies, procedures, handbooks, SOPs, technical documentation, product manuals, and training materials.
Customer Interactions
Support tickets, chat logs, email correspondence, call transcripts, and feedback surveys.
Business Data
Sales records, financial reports, market analysis, competitive intelligence, and strategic plans.
Legal & Contracts
Contract templates, legal opinions, compliance documents, and regulatory filings.
R&D & IP
Research notes, patents, formulations, designs, and proprietary methodologies.
HR & Operations
Job descriptions, performance criteria, operational metrics, and organizational knowledge.
Secure Training Pipeline
Secure Training Questions
What base models can be used for private training?
+We primarily use open-source models that can be fully deployed within your infrastructure: Llama 3.1 (8B, 70B, 405B), Mistral (7B, 8x7B), Qwen, and specialized models for coding, legal, or medical domains. For less sensitive use cases, we can also fine-tune via private API connections to OpenAI or Anthropic with enterprise agreements.
What hardware is required for on-premise deployment?
+Requirements depend on model size. For Llama 3.1 70B: minimum 2x NVIDIA A100 80GB or 4x A100 40GB GPUs, 256GB RAM, 2TB NVMe storage. We can also deploy smaller models (7B-13B) on single-GPU systems for specific use cases. We provide full hardware specifications during the planning phase.
How long does training take?
+Fine-tuning typically takes 4-24 hours depending on dataset size and model. The full project including data preparation, training, evaluation, and deployment usually takes 4-8 weeks. We provide incremental checkpoints so you can test progress throughout.
Who owns the trained model?
+You do. The fine-tuned model, all training artifacts, and any derived intellectual property belong entirely to your organization. We provide full documentation and can train your team to maintain and update the model independently if desired.
Can the model be updated with new data?
+Yes. We can set up continuous learning pipelines that periodically incorporate new documents, or perform incremental training as your knowledge base grows. RAG (retrieval) systems can also be updated in real-time without retraining.
Ready for Secure AI?
Schedule a confidential consultation to discuss your data security requirements and explore deployment options for your organization.