Better Retrieval Starts with Better Chunks
Your RAG system is only as good as your document preparation. We help companies process their entire document library with chunking strategies optimized for retrieval accuracy.
Common Retrieval Challenges
If your RAG application is returning irrelevant results or missing important context, the problem is likely in your document preparation.
Poor Retrieval Results
Generic chunking leads to irrelevant results and hallucinations in your AI applications.
Lost Context
Important information gets split across chunks, breaking semantic meaning.
Missing Metadata
Chunks without rich metadata can't be filtered, ranked, or contextualized effectively.
One Size Doesn't Fit All
Different document types need different chunking strategies and schemas for optimal retrieval.
What We Do
We provide end-to-end document processing services designed to maximize your RAG retrieval quality.
Retrieval Strategy Audit
Analyze your current setup and identify chunking improvements that will boost retrieval accuracy.
Custom Schema Design
Build metadata schemas tailored to your use case—categories, topics, document types, and hierarchy.
Metadata Enrichment
Enrich chunks with titles, keywords, summaries, and custom fields that improve retrieval filtering and relevance.
Full-Library Processing
Process your entire document library with optimized chunking and enrichment at scale.
Quality Validation
Test retrieval accuracy before and after to measure improvement and ensure production readiness.
"ChunkForge helped us process over 1,000 university extension guides with custom metadata schemas designed for our specific retrieval needs. The results transformed our ability to match pest control solutions to specific crops and geographic regions."
AcreBlitz
Agricultural Technology Platform
1,000+
Documents Processed
20+
State-Specific Guides
AcreBlitz needed to extract pest control recommendations from dense agricultural documents, matching specific pests to crops and states. Standard chunking broke critical context—our custom metadata enrichment preserved the relationships that made retrieval accurate.
Our Process
We work closely with your team to understand your needs and deliver results that improve retrieval.
Discovery Call
Understand your RAG application and retrieval challenges
Sample Analysis
Process a sample set and benchmark retrieval quality
Strategy Design
Design chunking approach tailored to your documents and use case
Processing
Process your full document library at scale
Validation & Handoff
Verify retrieval improvements and deliver production-ready chunks
Request a Consultation
Tell us about your project and we'll reach out to schedule a discovery call.