Enterprise Agentic RAG Platform

Problem Statement

Enterprise adoption of Generative AI faces critical production blockers: probabilistic hallucinations without source-level verification, unacceptable retrieval latency at scale, vulnerability to prompt injection attacks, and privacy leakage of sensitive PII or credentials. Standard naive RAG architectures cannot execute multi-step reasoning, lack mathematical precision, and fail to isolate cross-departmental tenant data.

Technical Architecture & Key Innovations

1. Hybrid Retrieval Engine with PGVector HNSW

Designed and built a dual-mode high-throughput retrieval system:

• PGVector HNSW Indexing: PostgreSQL 16 pgvector integration utilizing Hierarchical Navigable Small World (HNSW) graphs (M=16, ef_construction=64) delivering sub-20ms semantic search with 2,185+ QPS throughput.
• Reciprocal Rank Fusion (RRF): Fuses dense semantic vector embeddings with sparse BM25 lexical keyword ranking, ensuring high precision for both contextual concepts and exact technical terminology.
• Resilient In-Memory Fallback: Zero-dependency SIMD/NumPy cosine similarity fallback for offline execution, unit testing, and instant development.

2. Bounded Multi-Step Autonomous Agent

Implemented a state machine orchestrator that replaces rigid linear pipelines with dynamic multi-step planning:

• Dynamic Tool Dispatch: Routes autonomously across VectorSearchTool (semantic retrieval with RBAC scope), CalculatorTool (deterministic arithmetic & cloud sizing formulas), and CitationVerifierTool (claim-to-source provenance validation).
• Loop Prevention & Execution Latency Tracking: Enforces a bounded step limit (max_steps) with comprehensive ToolExecutionTrace observability.

3. Two-Stage Deterministic Security Guardrails

Constructed a strict defense-in-depth perimeter surrounding LLM interactions:

• Pre-Execution Firewall: Screens incoming prompts for direct & indirect jailbreaks, obfuscated payloads (Base64, Hex, ROT13), SQL injections, shell command executions, and RBAC department violations.
• Post-Execution Sanitization & Grounding: Multi-pattern PII redactor (API keys, cloud tokens, credit cards, SSNs, emails, IPs) and sentence-level claim grounding verifier ensuring answers are 100% cited.

Key Results & Benchmarks

• 100% Test Pass Rate: 65 automated unit, integration, and UI tests running in under 4 seconds.
• 100% Adversarial Defense: Successfully intercepted and blocked 20/20 injection and privilege escalation vectors.
• 100% PII Masking: Verified complete redaction across 14/14 credential and personal identifier formats.
• Full Document AI Lifecycle: Multipart ingestion supporting Markdown, CSV, TSV, JSON Tables, and Scanned OCR with artifact remediation.

Tech Stack