Secure Enterprise AI Assistant
Case Study Summary
Client: Confidential / Leading European EV Battery Manufacturer
Industry: Automotive (EV Battery Manufacturing)
Role: AI Tech Lead
Impact Metrics:
- Eliminated risk of data leakage from external AI tools (100% local processing)
- Reduced onboarding document review time by ~70%
- Scaled seamlessly from 20 → 300 concurrent users with <2s latency
- Enabled secure multilingual translation across 5+ languages
- Continuous ingestion of company news and onboarding documents
Built a secure, enterprise-grade AI assistant, a ChatGPT-like tool deployed fully on-premises. The system combined a deterministic RAG pipeline using a centralized knowledge base, real-time translation, and a React-based UI. It enabled global employees to interact with a private AI assistant without leaking any sensitive information.
The Challenge
Employees were increasingly using public tools (ChatGPT, Google Translate) for document digestion and translation. This posed two core risks: 1. Data leakage to external AI platforms 2. Inconsistent document analysis quality
The client required a fully local AI assistant, scalable, multilingual, and compliant with internal data governance.
The Solution
→ Implementation
I implemented a secure hybrid architecture with full separation of concerns. A React-based frontend with SSO login managed user sessions and chat interactions, while a dedicated user database stored chat history. A vector database (PostgreSQL + pgvector) held embedded company knowledge, optimized for RAG use. Distributed Ollama LLM endpoints were deployed across a local 9-GPU HPC cluster, and event-driven pipelines handled ingestion of documents, embedding generation, translation, and news updates. Each component was isolated and containerized, enabling scalable, fault-tolerant operations while enforcing data boundaries between chat storage and knowledge retrieval.
→ Solution Architecture
A secure local AI assistant architecture using a React-based UI, user DB, Ollama endpoints on 9x Nvidia GPUs HPC, and pgvector-based RAG pipelines.
→ Tech Stack
- Infrastructure: On-prem HPC cluster (9x Nvidia GPUs)
- Frontend: React-based UI with SSO
- LLM Runtime: Ollama endpoints (local, GPU distributed)
- Vector DB: PostgreSQL + pgvector
- User DB: PostgreSQL (chat metadata, settings)
- Ingestion Pipelines: Python-based, event-triggered
- Containerization & Scaling: Docker, optionally Kubernetes (bare-metal)
Key Learnings
- Data Sovereignty: Complete local deployment eliminated external dependencies and data leakage risks.
- Scalability: Distributed inference on GPU clusters maintained low latency (<2s) even with 300+ concurrent users.
- Data Hygiene: Separating user metadata from vector knowledge prevented knowledge base pollution.
- User Adoption: Integrating with existing SSO and providing a familiar UI (React-based) smoothed the transition from public tools.
Measurable Impact
- Achieved complete local deployment, removing all dependency on external AI services
- Improved onboarding efficiency by ~70% through instant document summarization
- Prevented knowledge base "pollution" by separating user metadata from vector knowledge
- Maintained <2s latency for 300+ concurrent users via GPU-distributed inference endpoints
- Enabled fast, private multilingual translation across departments
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