Vector RAG - Neural Hybrid Document Intelligence
Enterprise knowledge platform combining dense vector embeddings with semantic graph relations for contextual Q&A across unstructured data.
Structured architectural snapshot optimized for enterprise review and generative search synthesis.
Enterprise knowledge platform combining dense vector embeddings with semantic graph relations for contextual Q&A across unstructured data.
Hardened asynchronous microservice topology with end-to-end telemetry and deterministic isolation.
Document Ingestion Speed: 100 pgs/sec • Answer Citation Accuracy: 98.8%
Zero Data Leakage / ISO 27001 Aligned
Executive Summary // Architecture Brief
PRODUCTION SPECIFICATIONHigh-performance conversational intelligence engine ingesting thousands of PDF manuals, policy guides, and research documents to deliver citation-grounded answers with verifiable source quotes.
Key Impact & Verified Metrics // 2026 Telemetry
LIVE PRODUCTION BENCHMARKS01. Architecture Overview & Challenge
02. Engineering Solution
03. Measurable Production Outcomes
- Saved enterprise legal and HR teams over 15 hours per week on policy lookup workflows.
04. Technical Tradeoffs & Architecture Highlights
Scope of Work & Deliverables
Vector RAG - Neural Hybrid Document Intelligence - Architecture Topology
Live topology inspection, microservice mesh, and protocol specifications for Vector RAG - Neural Hybrid Document Intelligence.
Next.js 16 Edge Client
Global CDN distribution with server-side streaming hydration and sub-second TTFB.
YARP / Envoy Gateway
OAuth2/JWT token verification, mTLS encryption, rate limiting, and dynamic tenant routing.
SignalR WebSocket Hub
Bidirectional streaming for live telemetry, chat broadcast, and instant notifications backed by Redis.
C# .NET 9/10 Core Engine
High-throughput event sourcing with MediatR CQRS pipelines and compiled expression trees.
LangGraph AI Mesh
Stateful multi-agent execution with policy guards, token budget controllers, and tool routing.
vLLM Inference Cluster
Self-hosted Llama-3 & DeepSeek models with continuous batching and FP8 quantization.
Vectorless BM25 Engine
Zero vector database lock-in. SQLite FTS5 / Lucene BM25 index with reranking cross-encoder.
Apache Kafka / RabbitMQ
Log-based partitioned event streams for asynchronous auditing, notifications, and telemetry ingestion.
Redis 7.2 Cluster
Distributed cache with sub-millisecond lookups, distributed locks, and SignalR pub/sub backplane.
PostgreSQL Multi-Tenant DB
Strict tenant schema isolation with connection pooling (PgBouncer) and WAL archiving.
Next.js 16 Edge Client
SSR / Edge Worker
Architectural Specification
Global CDN distribution with server-side streaming hydration and sub-second TTFB.
Downstream Pipe Connectors
Verified Production Impact & Client Endorsements
Real feedback from engineering leaders, CTOs, and founders across 15+ years of production deployments.
“Vijay and the SHIVAM ITCS AI team didn't just execute the project - they elevated our entire platform. Their ability to architect complex multi-agent state machines, eliminate latency bottlenecks, enforce zero-hallucination guardrails, and create an intelligent multi-LLM orchestration layer was extraordinary. The final delivery was resilient, battle-tested, and far beyond expectations. One of the strongest AI systems engineering teams in the industry.”
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Ready to Build Enterprise Vector RAG - Neural Hybrid Document Intelligence?
Consult directly with our principal AI architects to evaluate architecture feasibility, cost optimization paths, and implementation timelines.