On-Premise AI Strategy & Architectural Advisory

Executive Summary

A highly regulated manufacturing and logistics enterprise needed to parse, analyze, and extract data from millions of sensitive client shipping logs, legacy partner contracts, and internal standard operating procedures. Due to strict corporate data-at-rest policies and GDPR compliance constraints, sending this proprietary data to public multi-tenant cloud APIs was legally and operationally impossible.

Rather than acting as a hardware vendor, West Summit was engaged as an independent systems architect and technical advisor. We audited their requirements, drafted the procurement specifications for a local enterprise GPU server cluster, benchmarked and quantized their target open-weight models (the specific models current at the time of engagement — Llama 4 Scout, DeepSeek V4-Flash, and Qwen 3.6-35B), designed local testing harnesses and custom prompt templates, and guided their internal IT team through secure LAN VLAN integration. The design kept all processing inside the client’s network with no cloud egress, and — on the client’s own cost and usage assumptions — projected an estimated 14-month payback window.

The Challenge

The enterprise faced significant technical and regulatory bottlenecks when adopting AI:

• Compliance Perimeters: GDPR and SOC 2 audits prohibited the transmission of customer PII and supply chain contracts outside the physical walls of the enterprise.
• Prohibitive Cloud Scaling: On the client’s own volume estimate of over 10 million tokens daily, they projected cloud API token costs would exceed US$18,000 annually, with unpredictable price fluctuations.
• System Integration Gaps: The client’s internal IT department possessed deep network administration expertise but lacked the specialized MLOps bandwidth to build, quantize, and run vLLM and TensorRT engines locally.
• Redundancy and Reliability: The client required a strategy that fit their existing corporate server infrastructure, avoiding single-point-of-failure home setups.

The Solution

We executed a three-phase local AI advisory and architectural mapping strategy:

Key Architectural Phases

1. Ingestion Audit & Requirements Mapping: We analyzed a bounded, non-live sample of 5,000 unparsed shipping logs and contracts inside our isolated developer sandbox, establishing baseline data accuracy and schema structures under strict NDA.

2. Technical Architecture, Optimization & Testing Blueprint: We designed the complete software stack and evaluation framework: benchmarking and quantizing the Llama 4 Scout and DeepSeek V4-Flash models using TensorRT-LLM and vLLM frameworks. We developed local testing suites, engineered custom prompt templates, and drafted guidelines for integrating local Qdrant vector databases and a secure LibreChat interface.

3. High-Fidelity Demonstration: Established a secure VPN connection to run a live demonstration of multi-turn reasoning and contract analysis, proving token throughput speeds and verifying 0% packet egress to public endpoints.

4. Hardware Spec & Procurement Advisory: We provided the client’s IT team with detailed hardware specifications and procurement lists for an on-premise enterprise GPU hardware server cluster. The hardware acquisition cost was absorbed by the client directly as upfront CapEx.

5. Software Snapshot, Testing & Deployment Oversight: We prepared a verified Docker container snapshot containing the RAG pipeline, local evaluation harnesses, and prompt engineering assets. We guided the client’s infrastructure engineers through flashing the target OS (NVIDIA DGX OS) and deploying the container, integrating configurable API wrappers to support hybrid fallback to external enterprise providers (AWS Bedrock, Azure AI, GCP Vertex AI, OpenAI, Anthropic, OpenRouter, Mistral, and DeepSeek) for non-sovereign workloads.

6. LAN VLAN Integration & Security Lock: Provided configuration templates to sever public internet access to the hardware unit, assigning a static internal IP and setting up firewalls behind their enterprise DMZ.

The Outcomes

• Zero-Egress Architecture, Verified in Testing: The architecture is designed so that data processing stays inside the client’s secure physical walls. During our high-fidelity demonstration we verified zero packet egress from the appliance to public endpoints. Ongoing compliance with the client’s internal and external audits is the client’s own responsibility and outcome.
• Independent Asset Ownership: The client owns the hardware assets directly, avoiding restrictive vendor contracts and proprietary software lock-in.
• Estimated 14-Month Payback: On the client’s own cost and usage assumptions, the combined hardware and advisory investment was projected to pay back within roughly 14 months. This is an estimate based on client-supplied inputs, not a guaranteed or independently audited figure.
• Low Local Latency: Running on the local network avoided public-internet round-trips, and token generation in the demonstration was fast and responsive. We did not run a controlled head-to-head benchmark against commercial cloud APIs.

Measurement & Limitations

• Nature of the engagement: West Summit acted as an independent architect and technical advisor. We designed the zero-egress blueprint, specified procurement, benchmarked models, and oversaw deployment; the client procured, owns, and operates the hardware. Outcomes therefore depend in part on the client’s own implementation and operation.
• Baseline & cost figures: The token volume (over 10 million tokens daily) and the US$18,000/year cloud-cost figure are the client’s own estimates, captured during discovery. The 14-month payback is a projection derived from those client-supplied inputs against the hardware CapEx and advisory fee — an estimate, not an independently audited result.
• Zero-egress claim: We verified zero packet egress from the appliance to public endpoints during a controlled demonstration. This reflects the tested configuration and design intent; we make no claim about the outcome of the client’s own third-party compliance audits.
• Latency: Local token generation was fast and responsive in the demonstration. We did not run a controlled benchmark against commercial cloud APIs, so no head-to-head speed comparison is claimed.
• Technology currency: Model names, specifications, and licensing reflect what was current at the time of engagement and change frequently; selections are re-evaluated per engagement.
• Provenance: The client is anonymised at their request. The architectural blueprint, benchmarking approach, and procurement specifications can be reviewed under NDA. No independent third-party audit of these figures has been performed.

Technology Stack Designed

• Target Hardware Spec: Enterprise GPU server (e.g., PCIe GPU cluster with high VRAM capacity), dual high-speed SmartNICs
• Local Inference Stack: vLLM Engine, TensorRT-LLM, NVIDIA DGX OS
• Open-Weight Models (current at the time of engagement; open-weight model names and specs move quickly): Llama 4 Scout (FP4 quantized; ~109B total / 17B active MoE, with an advertised context window up to 10M tokens), DeepSeek V4-Flash, Qwen 3.6-35B
• Integration Connectors: Hybrid API wrappers for AWS Bedrock, Azure AI, GCP Vertex AI, OpenAI, Anthropic, OpenRouter, Mistral, and DeepSeek
• Evaluation Frameworks: Custom local LLM benchmarking test suites and prompt/harness engineering templates
• RAG & Search: Qdrant Local Vector DB, LibreChat secure custom UI
• Deployment: Docker containerization on isolated internal VLAN/DMZ