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Enterprise RAG platform, built for production.

We design and build production RAG platforms from scratch: hybrid vector search, LangGraph agentic orchestration, multimodal document intelligence, and continuous evaluation. Not a demo — a platform that runs and improves over time.

→ Why most RAG projects fail in production

RAG is one of the most overpromised and underdelivered capabilities in enterprise AI. The vector index works in the demo. In production it fails silently — retrieving the wrong chunks, missing context across modalities, returning hallucinated citations, and having no evaluation mechanism to even know when it's breaking.

The common failure modes: generic chunking that doesn't match document structure, pure vector retrieval that misses lexical precision, no reranking layer, no evaluation baseline, and no feedback loop from production errors back to the pipeline. Our reference engagement (SponsorUnited) started from scratch and reduced manual review by 90%+ in production — because we engineered the pipeline, not just the model call.

How we build production RAG

1. Data architecture and document ingestion

Document structure analysis, format-specific parsing (PDF, DOCX, video transcripts, audio), and extraction pipelines designed for your content types. Metadata extraction and enrichment. Ingestion via Airbyte, NiFi, or custom pipelines.

2. Chunking, embedding, and hybrid indexing

Chunking strategy matched to your document structure — not generic page-level or sentence-level defaults. Embeddings tuned or selected for your domain. Hybrid index combining dense vector search with sparse lexical (BM25). Continuously updated as content changes.

3. Retrieval with reranking

Multi-stage retrieval: broad recall, metadata filtering, cross-encoder reranking for precision. Configured retrieval scoping so queries hit the right document subsets. Monitored for retrieval quality against a golden evaluation dataset.

4. Agentic orchestration on LangGraph

LangGraph-based orchestration for multi-step reasoning, tool use, and human-in-the-loop workflows. Claude as the reasoning model for complex queries and long-context document processing. Explicit execution graphs that are debuggable in production.

5. Monitoring, evaluation, and iteration

Continuous evaluation against golden datasets. Drift detection as documents change. Human review loops for low-confidence outputs. The feedback loop that determines whether the platform improves or degrades over time.

[ FAQ ]

Questions about enterprise RAG platforms.

What separates a production RAG system from a RAG demo?
Production RAG requires: chunking strategy matched to your document structure (not generic defaults), hybrid lexical/semantic retrieval with reranking, metadata filtering to scope retrieval to relevant document sets, continuous evaluation against a golden dataset, drift detection as documents change, and human review loops. A demo works on 50 clean documents. A production system works on 500,000 documents with varying quality and you can measure retrieval quality over time.
What's the difference between LangChain and LangGraph, and which do you use?
LangChain is suited for linear pipelines with predictable execution. LangGraph adds stateful orchestration with explicit execution graphs, loops, and conditional branching — essential for agentic workflows. Most production agentic RAG systems we build use LangGraph because it gives you execution state visibility, better debugging, and explicit human-in-the-loop checkpoints. We use whichever is appropriate for the workload.
How do you handle multimodal documents — PDFs with charts, video transcripts, mixed content?
Each modality has its own processing path. PDFs with charts go through document structure parsing followed by chart OCR or vision model extraction. Video transcripts get ASR processing, speaker diarization, and timestamped chunk extraction. Mixed content is processed per-modality and re-unified at the metadata layer, so retrieval can scope to specific modalities or cross-modal context. The unified vector index ingests all of it under a consistent schema.

Building enterprise RAG that needs to work in production?

Tell us about your document types, query patterns, and current state. We'll talk through the architecture decisions that matter for your specific workload — and what we'd build to make it reliable over time.

Schedule a conversation → Multimodal AI services