Secure Enterprise AI Assistant (EU Enterprise)
Case Study Summary
Client: Confidential / Leading European EV Battery Manufacturer
Website: —
Industry: Automotive (EV Battery Manufacturing)
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.
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.
Our Approach
We implemented a secure hybrid architecture with full separation of concerns:
- A React-based frontend with SSO login managed user sessions and chat interactions.
- A dedicated user database stored chat history, settings, and user preferences.
- 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.
- 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.
Results & 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
Solution Overview
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)
Additional Context
- Timeline: ~4 months (architecture → deployment)
- Team Size: 4 (AI Tech Lead, Data Scientists x2, MLOps Engineer)
- Role: AI Tech Lead
- Collaboration: Worked with Data, IT, and Infra teams to align deployment with internal compliance
- Future Plans: Add per-user document memory, fine-tune internal LLMs on internal terminology, build adoption analytics
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