On-Premise AI Strategy & Advisory
Design your secure, zero-egress AI infrastructure. Standardize your hardware, models, and compliance perimeters.
The Problem
Regulated mid-market enterprises need to leverage LLM automation but lack the systems expertise to evaluate, design, and configure secure local hardware (such as dedicated GPU servers) or Private VPCs without data leakage.
Our Solution
We act as your independent architectural advisor. We design secure zero-egress blueprints, benchmark the latest enterprise open-weight models (Llama 4 Scout, DeepSeek V4, Qwen 3.6), set up local evaluation harnesses, design custom prompt/context templates, and guide your IT team through local hardware, Private VPC, or secure cloud provider deployment.
Deliverables
- Local AI Architectural Design & Feasibility Blueprints
- Hardware & Infrastructure Advisory (Dedicated GPU Servers vs. Private VPC)
- Enterprise Model Selection & Quantization Guidelines (Llama 4, DeepSeek V4, Qwen 3.6, Mistral)
- Local LLM Evaluation Harnesses & Benchmarking Test Suites
- Prompt & Context Engineering Blueprints (System Prompts, RAG Optimization, Agentic Tool-Use)
- Multi-Provider & Hybrid Cloud Integration Specifications (AWS Bedrock, Azure AI, GCP Vertex AI, OpenAI, Anthropic, OpenRouter, Mistral, DeepSeek)
- Open-Source Web UI & RAG Stack Architecture (LibreChat/Qdrant)
- Vendor Evaluation & Implementation Oversight
FAQ
No. We are independent consultants, not hardware vendors. We design the blueprints, spec the exact procurement bills of materials, and provide implementation oversight. You purchase the hardware directly, ensuring complete control over assets and zero vendor lock-in.
Physical appliances (such as dedicated GPU cluster servers) are ideal for regulated mid-market firms with strict physical facility audits and zero-egress mandates. For smaller teams or non-regulated businesses, we recommend deploying open-weight models inside a Private VPC (AWS/Azure) or using high-spec local workstations (Mac Studio), which provide data sovereignty at a fraction of the hardware maintenance overhead.
If you choose the on-premise hardware setup, 100% of data processing, vectorization, and inference occurs inside the physical box on your local network, and external internet access can be completely severed. If you choose a Private VPC, data remains encrypted within your private cloud instance and is never sent to public APIs or used for model training.
We typically benchmark and recommend quantized versions of top open-weight models. For example, Llama 4 Scout (109B parameters, 10M context window) is ideal for long-context documentation processing, while DeepSeek V4-Flash or Pro offers state-of-the-art logical reasoning and coding under a permissive MIT license, and Qwen 3.6 or Mistral Large 3 excel in multilingual and complex agentic workflows. However, model selection is highly dependent on your specific usecase; we will be there to evaluate, run local tests, and provide the best model tailored precisely to what you need.
Yes. While we specialize in zero-egress open-weight model deployment, we build flexibility into your architecture. We can run local tests, prompt engineer, harness engineer, and deliver everything you need to leverage either local on-premise AI or your own chosen AI cloud provider. This includes native integration specs for AWS Bedrock, Azure AI, GCP Vertex AI, Anthropic, OpenAI, OpenRouter, Mistral, and DeepSeek, enabling you to switch providers with zero configuration rewrite.