Private AI for Startups Build Your Data Moat
Deploy AI infrastructure that keeps your data proprietary, scales with usage instead of headcount, and creates competitive advantage that SaaS AI tools cannot match. Built for startups preparing for Series C and beyond.
Private AI Solutions
AI infrastructure that VCs ask about at Series C due diligence.
AI Inference Hosting
Run open-source models on dedicated GPU infrastructure. No per-token pricing, no data leaving your environment.
RAG Implementation
Retrieval-augmented generation that connects AI to your proprietary data for accurate, contextual responses.
Model Fine-Tuning
Custom-train models on your codebase, documentation, and domain knowledge for superior results.
Copilot Alternative
Code completion that understands your codebase at a fraction of per-seat pricing. See our cost calculator.
How We Build Your Private AI
Discovery: map use cases and data sources
Architecture: design infrastructure for your scale
Deploy: provision GPU infrastructure and models
Fine-tune: train on your proprietary data
Integrate: connect to your applications and workflows
Optimize: ongoing model updates and performance tuning
Frequently Asked Questions
Why do VCs care about private AI?
Private AI creates a defensible data moat. Companies using SaaS AI tools have no proprietary advantage because the same capabilities are available to every competitor.
How does private AI help with compliance?
Sensitive data never leaves your infrastructure. This simplifies SOC 2 and HIPAA compliance by eliminating third-party data processing concerns.
What models can we run?
Any open-source model including Llama, Mistral, CodeLlama, and specialized domain models. We select and optimize models for your specific use cases.
At what team size does private AI make sense?
Private AI becomes cost-effective at around 20 employees. Use our cost calculator to compare against your current per-seat spending.
Build Your AI Moat Before Competitors Do
Schedule a discovery call to map your private AI roadmap.