Service

Production RAG Pipelines

Document ingestion, vector search, hybrid retrieval, and AI answers with source citations. Built for scale.

What We Build

A production RAG pipeline is not a LangChain demo. It is two separate systems working together: an offline ingestion pipeline that processes your documents once, and a real-time query pipeline that retrieves relevant context and generates accurate answers with source citations.

Document Ingestion

Layout-aware PDF parsing, semantic chunking by document structure, embedding generation, and storage in pgvector with full metadata.

Hybrid Retrieval

Combined vector similarity and keyword search with cross-encoder reranking. Sub-2-second latency on corpora with hundreds of thousands of chunks.

Response Generation

LLM answers grounded in retrieved context with inline source citations. Hallucination guardrails that flag when confidence is low.

Monitoring & Evaluation

Retrieval quality metrics, answer faithfulness scoring, and automated regression testing so your RAG system improves over time.

How It Works

1. Document Audit

Analyze your document corpus — formats, volume, structure, update frequency. Define chunking strategy and embedding model based on actual content, not defaults.

2. Pipeline Architecture

Design ingestion and query pipelines as separate systems. PostgreSQL with pgvector for storage, Ollama or cloud APIs for inference, Docker for deployment.

3. Build & Deploy

Full implementation with Next.js frontend, API layer, and containerized infrastructure. Health checks, automated restarts, and production monitoring included.

4. Evaluate & Iterate

Measure retrieval precision, answer quality, and latency. Tune chunking, reranking, and prompts until the system meets production standards.

Built & Deployed

These are not concepts — these are systems running in production.

Compliance RAG System

38 API routes, full document ingestion pipeline, pgvector hybrid search, and AI-generated compliance answers with source citations. Serves compliance teams daily.

Intelligence Brief System

Real-time web research with multi-source retrieval, automated summarization, and structured intelligence reports. Processes hundreds of sources per brief.

20+ Production Systems

RAG components integrated across a fleet of 20+ applications — CRM, security tools, content systems, and internal knowledge bases.

Frequently Asked Questions

How long does it take to build a production RAG pipeline?

2–4 weeks for a standard deployment. This includes document audit, chunking strategy design, pipeline build, and production deployment with monitoring. Complex corpora with mixed document formats may take longer.

What document formats do you support?

PDF, DOCX, HTML, Markdown, plain text, and structured data formats like CSV and JSON. Layout-aware parsing handles multi-column PDFs, tables, and headers correctly.

Can I use my own LLM or do I need a cloud API?

Both. We deploy Ollama for local inference when data stays on-premises, cloud APIs (OpenAI, Anthropic) for maximum capability, or hybrid setups that route based on sensitivity and complexity.

How do you handle hallucinations?

Confidence scoring on every response, source citation requirements, retrieval quality metrics, and automated regression testing. When the system isn't confident, it says so instead of guessing.

Deep Dive

Building a Production RAG Pipeline: Architecture, Chunking, and Retrieval That Actually Works →

Ready to Build?

Production systems, not demos. Tell us what you need.

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