Enterprise RAG Pipelines & Autonomous Multi-Agent Systems
Transform experimental LLM prototypes into hardened, low-latency, production systems. I engineer custom Retrieval-Augmented Generation architectures with dense-sparse vector indexing, deterministic guardrails, and sub-450ms token streaming.
Architectural Breakdown & Deliverables
Every enterprise AI implementation includes complete source code, Docker configs, and automated test suites.
Dense-Sparse Hybrid Vector Retrieval
Combining 1536-dimensional dense neural embeddings (OpenAI / Cohere) with BM25 sparse keyword vectors inside Qdrant or PostgreSQL pgvector. Merged via Reciprocal Rank Fusion (RRF, k=60) to eliminate semantic blind spots.
Deterministic Guardrails & Fallback Gates
Enforcing cosine similarity rejection thresholds (0.78 cutoff) to filter out out-of-domain inquiries before they reach generation. Structured JSON validation using Pydantic and Zod ensures 0% schema breakage on autonomous tool calling.
Edge Streaming & Real-Time UI
Sub-450ms Time-To-First-Token streaming delivered over HTTP Server-Sent Events (SSE). Client interfaces built with Next.js 14/15 App Router featuring live Markdown rendering, interactive source citations, and copyable code blocks.
Autonomous Multi-Agent Tool Orchestration
Multi-agent workflows powered by LangChain and LCEL with stateful Redis BullMQ queues, database read/write tools, webhook listeners, and human-in-the-loop escalation paths for complex business automations.
Low-Latency RAG Knowledge Agent with Semantic Guardrails
Read the architectural breakdown detailing 94.8% retrieval recall, 42% hallucination reduction, and sub-450ms token delivery in a live customer support deployment.
Need a Production RAG MVP in 2 to 4 Weeks?
I am available for contract builds, architectural reviews, and technical lead roles. Every project includes architectural documentation, automated test suites, and Docker manifests.