Enterprise AI Chat Platform

Overview

Architected and implemented a production-grade enterprise AI chat platform featuring advanced RAG capabilities, multi-model LLM support, and an innovative agentic tool framework. The platform enables organizations to leverage their knowledge bases through intelligent conversational AI with real-time streaming responses.

Technical Architecture

RAG Pipeline

• AWS Bedrock: Core LLM inference with multi-model support
• OpenSearch: Vector search and semantic retrieval at scale
• AWS Step Functions: Orchestrated document ingestion, chunking, and embedding workflows
• Intelligent Chunking: Optimized document segmentation for context preservation

Multi-Model LLM Support

• Claude (Anthropic): Primary model for complex reasoning and generation
• Amazon Nova: Cost-effective alternative for simpler queries
• Mistral: High-performance open model for specific use cases
• Dynamic model routing based on query complexity and cost optimization

Real-Time Communication

• WebSocket Integration: Low-latency streaming responses
• Token-by-token streaming for improved user experience
• Connection state management and automatic reconnection
• Concurrent session handling at scale

Agentic Tool Framework

Developed a sophisticated agentic architecture enabling the AI to perform complex multi-step tasks:

Knowledge Retrieval Agent

• Semantic search across enterprise document repositories
• Contextual ranking and relevance scoring
• Source attribution and citation generation

Lesson Planning Agent

• Automated curriculum generation based on learning objectives
• Adaptive content structuring for different skill levels
• Integration with educational content repositories

Quiz Generation Agent

• Dynamic assessment creation from source materials
• Multiple question formats (MCQ, short answer, essay prompts)
• Difficulty calibration based on content complexity

Safety & Evaluation

Bedrock Guardrails

• Content moderation for input and output
• PII detection and redaction
• Topic filtering for off-limits subjects
• Custom policy enforcement for enterprise compliance

Evaluation Pipeline

• DeepEval Integration: Comprehensive LLM evaluation framework
• 20+ evaluation metrics including faithfulness, relevance, and coherence
• Automated regression testing for model updates
• A/B testing infrastructure for prompt optimization

Cost Optimization

Usage Metrics & Monitoring

• Token consumption tracking per user/organization
• Cost attribution and chargeback reporting
• CloudWatch Integration: Real-time monitoring and alerting
• Automated cost anomaly detection

Optimization Strategies

• Intelligent caching for repeated queries
• Model tiering based on query complexity
• Prompt optimization for token efficiency
• Batch processing for non-interactive workloads

Key Features

• Enterprise SSO: Seamless authentication integration
• Role-Based Access: Granular permission control
• Audit Logging: Comprehensive activity tracking
• Multi-Tenancy: Isolated environments per organization
• API Gateway: RESTful APIs for third-party integration

Technologies Used

• LLMs: Claude, Amazon Nova, Mistral (via AWS Bedrock)
• Vector Search: OpenSearch
• Orchestration: AWS Step Functions, Lambda
• Real-Time: WebSocket, API Gateway
• Monitoring: CloudWatch, DeepEval
• Languages: Python, TypeScript
• Infrastructure: AWS CDK, Docker

Impact

• Production Deployment: Serving enterprise customers at scale
• Multi-Model Flexibility: Optimized cost-performance across use cases
• Agentic Capabilities: Advanced tool use beyond simple Q&A
• Enterprise-Grade Safety: Comprehensive guardrails for production use
• Observable AI: Full visibility into system performance and costs