Enterprise AI & Production RAG Systems
Moving beyond fragile AI demo prototypes to battle-tested, deterministic systems. Architecting enterprise Retrieval-Augmented Generation (RAG), vector databases (pgvector/Pinecone), semantic reranking, and autonomous workflow automation.
Why Enterprise AI Fails in Production
Naive vector search on unstructured PDFs routinely returns hallucinations, loses critical tabular context, and provides untraceable answers that executives cannot trust. In production, AI must be deterministic, audited, and strictly cited against authoritative data.
I engineer AI pipelines that blend lexical full-text indexing with vector similarity (HNSW pgvector), run cross-encoder rerankers to purge false positives, enforce source citation guardrails, and integrate directly into your operational software.
AI Solutions I Architect
Production RAG Document Intelligence
Hierarchical semantic chunking, parent-child windowing, table structure preservation, and verbatim source attribution for legal, financial, and technical documents.
PostgreSQL pgvector & Vector Search
Single-engine relational and vector similarity indexing (HNSW & IVFFlat), eliminating the synchronization friction and data leaks of detached vector SaaS products.
Autonomous Business Agent Workflows
LangChain, LlamaIndex, and custom tool-calling agents that execute structured database writes, dispatch automated emails, and orchestrate business operations.
Evaluation & Hallucination Guardrails
Automated RAGAS testing suites, token budget optimization, latency caching, and strict output schema validation using Pydantic and Zod.
Deep-Dive AI Architecture Case Study
Read the exact end-to-end technical implementation of my production RAG engine:
Read Production RAG Case StudyFrequently Asked Questions
Can we keep our proprietary data private and secure?
Yes. I architect solutions with zero-data-retention API policies (such as Azure OpenAI or self-hosted open models like Llama 3 / Mistral) where your customer data is never used for foundation model training.
What is the typical timeframe to launch a production RAG system?
An initial production ingestion pipeline, pgvector schema, and evaluation benchmark can typically be deployed to staging within 2 to 4 weeks depending on the complexity of your document formats.
Ready to deploy enterprise-grade AI?
Let's review your documents, data structures, and accuracy targets.