Private AI deployment in your cloud or on-prem infrastructure when data privacy, compliance or latency makes public APIs unsuitable.
Self-hosted AI infrastructure — models, retrieval and orchestration inside your boundary, with no data leaving your environment.
Regulated industries (finance, healthcare, legal, government)
Sensitive internal data
Latency-critical workloads
Air-gapped environments
Sovereignty and data-residency requirements
Private LLM deployment (open or licensed models)
VPC / on-prem architecture
Access control and audit layer
Performance-tuned inference stack
Compliance-aware operations runbook
Open-source or licensed models hosted in your cloud or on-prem — no third-party APIs.
VPC-isolated or air-gapped, with data never leaving your boundary.
Role-based access control and per-request audit logs for compliance and forensics.
Inference stack tuned for predictable latency, throughput and cost on your hardware.
Assess data-control, compliance, latency and throughput requirements.
Choose models, hardware, isolation model and access / audit approach.
Deploy in a controlled environment, validate performance and compliance posture.
Roll out inside your boundary, operate and tune, extend to more workloads.
Open-source models (Llama, Mistral, Qwen, Gemma and their fine-tunes) or licensed models that support private deployment. Choice depends on the task, latency budget and licensing constraints.
Yes. We've deployed inside networks with no external connectivity, including model updates through controlled offline pipelines.
We size hardware to actual workload, tune inference (quantization, batching, KV cache) and, where relevant, blend smaller specialized models with a larger fallback to control cost.
For many enterprise tasks — retrieval-grounded Q&A, extraction, classification, structured agents — a well-deployed open model reaches quality comparable to frontier APIs. We measure it on your data, not on generic benchmarks.
Book a technical call. We'll review your workflow, data, integrations and constraints, then recommend what is worth prototyping.
