Pipeline
Hybrid strategy preview
Combine layout-aware, semantic, and overlap strategies in one pipeline and preview the result before you commit a single embedding.
The semantic chunking platform · 2026
Master the Split. Perfect the Retrieval.
CHUNKZA AI replaces crude character-count chunking with layout-aware splitting, semantic boundary detection, and parent-child chunking — visualized end to end, so your RAG systems retrieve the right context, every time.
- Recall@5
- +18.2%
- Boundary acc.
- 96.7%
- Avg. latency
- 42ms
chunkza · diagnosticlive
v1.4Source · paper.pdf
Chunks · 5
Parent · §2.1parent
318 tokens
Child · semanticchild
92 tokens
Child · semanticchild
74 tokens
Child · overlapoverlap
41 tokens
Layout · tablelayout
56 tokens
Recall@5
91.4%+18.2
Boundary acc.
96.7%+11.4
Latency
42ms-3.1x
Trusted by AI teams building retrieval at scale
Northwind Labs
Helix AI
Vectorwise
Lumen Research
Atlas Knowledge
Sentinel RAG
Quanta
Beacon AI
Why CHUNKZA
Garbage in, garbage out — fix it at the source.
In advanced RAG, retrieval quality is decided long before the model. It's decided by how you split. CHUNKZA treats chunking as a first-class engineering discipline.
01
Layout-aware splitting
We parse document structure — headings, tables, lists, captions — before we cut. Your chunks respect the document, not the character count.
02
Semantic boundary detection
A trained model predicts natural topic shifts inside long passages, so chunks end where meaning ends — never mid-sentence, never mid-thought.
03
Parent-child chunking
Retrieve on tight, cheap child chunks. Return the rich parent context to the model. You get recall and precision in a single pipeline.
04
Visualized end to end
See chunk boundaries, metadata injection, and embedding distribution in real time. Diagnose retrieval failures at the source, not in production.
Capabilities
All featuresA diagnostic panel for your retrieval pipeline.
Every feature is built to make the invisible visible — and the unfixable fixable.
Metadata
Metadata injection
Automatically tag each chunk with section path, source URI, page, and custom schema fields — query-aware retrieval out of the box.
Vectors
Embedding distribution map
Project your chunk embeddings into 2D space and spot clusters, outliers, and silent duplicates that drag down retrieval.
Compare
Boundary diff
Compare chunking strategies side by side. See exactly which boundaries moved, merged, or split — and what it does to recall.
Replay
Retrieval replay
Replay real queries against any chunking version and watch which chunks surfaced, in what order, with what score.
Structure
Schema-aware tables
Tables stay tables. Headers propagate to every row chunk, so numeric and structured retrieval stays coherent.
The pipeline
From raw document to semantic capsule — in four moves.
A clean, observable pipeline beats a clever black box. Every stage is inspectable, every decision reversible.
01
Ingest
Drop in PDFs, DOCX, Markdown, HTML, Notion, or raw text. Layout and structure are preserved.
02
Parse & split
Layout-aware segmentation feeds semantic boundary detection. Parent and child chunks are linked automatically.
03
Visualize
Inspect boundaries, metadata, and embedding distribution in real time. Diff strategies before you commit.
04
Retrieve
Export chunked corpora to your vector store. Replay queries and watch retrieval quality climb.
Where it shines
Built for the teams who can't afford bad retrieval.
From enterprise knowledge bases to research search, CHUNKZA fits wherever the quality of the answer depends on the quality of the chunk.
Enterprise KBUse case
Enterprise knowledge bases
Unify PDFs, wikis, Slack threads, and Confluence under one chunking policy. Engineers ship faster; domain experts trust the answers.
- 10M+ pages supported
- Role-aware metadata
- Source-grounded citations
RAG copilotsUse case
Domain-specific copilots
Build copilots that actually understand your product docs. Parent-child retrieval returns coherent context windows, not fragments.
- Coherent context windows
- Fewer hallucinations
- Per-section recall tuning
ResearchUse case
Research & academic search
Chunk long papers by section and citation. Retrieve claims with their supporting methods and figures attached.
- Section-aware splits
- Figure & table retention
- Citation graph metadata
SupportUse case
Customer support retrieval
Surface the right article snippet — not the whole FAQ. Overlap-aware chunking keeps multi-step guides intact end to end.
- Guides stay intact
- Ticket-aware routing
- Live policy sync
By the numbers
Retrieval quality, measured at the chunk.
CHUNKZA is benchmarked continuously against the chunking strategies most teams still ship by default. The gap is not subtle.
+18.2%
Recall@5 lift
vs. fixed-size 512-token baselines, averaged across 7 enterprise corpora.
96.7%
Boundary accuracy
Human-annotated section boundaries recovered without manual tuning.
42ms
Median chunk latency
Per-page layout-aware splitting, measured on standard indexing workers.
3.1×
Less context bloat
Parent-child retrieval trims tokens sent to the model by 68% on average.
Recall@5 by chunking strategy
Same corpus, same embedding model, same retriever.
Baseline CHUNKZA
Fixed 512-token73% → 91%
Recursive splitter76% → 93%
Markdown header split81% → 95%
Sentence-window79% → 94%
Start chunking smarter today
Replace crude character counts with layout-aware, semantically-boundary-aware chunking. See your retrieval quality rise from the source.