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Enterprise AI & Agentic RAGAI & Automation

Enterprise AI Agent & RAG Suite

Auto-Indexing RAG, Access-Control RAG, Multi-Agent Handoffs & Observability

Role: AI Systems Engineer & Full Stack Architect
Period: 2024 – Present
Project Links & Resources

Project Overview & Architecture

The Enterprise AI Agent & Multi-Tenant RAG Suite is an end-to-end intelligence platform engineered for enterprise workflows requiring strict tenant data isolation, zero-hallucination guarantees, and real-time observability.

The system features an Auto-Updating Knowledge Base that re-indexes document sources on a schedule with deduplication and conflict resolution. It enforces Access-Control-Aware RAG, ensuring users only retrieve document snippets authorized for their role/tenant.

Reliability is guaranteed via Confidence Scoring on answers, Self-Critique reflection loops, and Fallback Chains across LLMs. The platform includes a Multi-Agent Handoff architecture (Support agent with escalation, AI PR Code Reviewer, Receipt parser) backed by a LangSmith-style tracing dashboard and public API layer with token cost tracking.

Key Platform Features

Auto-updating & scheduled re-indexing knowledge base with conflict resolution
Access-control-aware RAG enforcing multi-tenant data boundaries
Confidence scoring on answers with zero-hallucination model fallback chains
Multi-agent handoff workflows (Support escalation, PR code reviewer, receipt parser)
Structured extraction pipeline converting unstructured PDFs/contracts to clean JSON
LangSmith-style observability tracing, token cost dashboard, and public API layer

Engineering Challenges & Solutions

Challenge: Preventing cross-tenant data leaks and AI hallucinations in enterprise contract search.
Solution: Implemented RBAC metadata filtering prior to vector similarity scoring and added a self-critique verification loop that flags low-confidence responses.
Challenge: Handling high document volume staleness and source conflicts.
Solution: Engineered a scheduled background re-indexing pipeline with deduplication and recency weighting.

Impact & Key Metrics

Answer Confidence96.4%
Retrieval Speed142ms
Daily Hits18.4K
JSON Extraction Accuracy98.9%

Technologies Used

Next.js 16TypeScriptLangChainLlamaIndexVector DB (Pinecone)Python / FastAPINode.jsOpenAI APITailwind CSS