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Semantic Search over Documents

Build semantic search over your documents with built-in chunking and ultra-fast embeddings — no separate pipeline.

ExtractEmbed & Retrieve
document → structured outputextract()
document
{
"title": "…",
"sections": 3,
"blocks": 24
}

Standing up semantic search usually means bolting a separate embedding pipeline onto your extractor and hoping the chunks line up.

Xberg does chunking and ultra-fast, Rust-native embeddings in the same pass as extraction, so you go from raw documents to a searchable index without a second system to run.

More use cases

document
{
"title": "…",
"sections": 3,
"blocks": 24
}

RAG Pipeline Ingestion

Turn a pile of PDFs, Office docs, and HTML into clean, chunked, embedded data for your vector database — in one call.

ExtractEmbed & Retrieve
RAGChunkingEmbeddings
parser.py
def extract(doc):
return parse(doc)
class Reader:
# 306 formats
{
"functions": 12,
"classes": 3
}

Document-Reading Agents

Give your AI agents one tool to read any document — 100+ formats, structured output, every framework.

ExtractEnrich
AgentsStructured JSONLayout
INVOICE#INV-0042
Acme Supply Co.
Brackets ×40$1,200.00
Fasteners$1,280.00
Total due$2,480.00
{
"tables": 1,
"rows": 8,
"columns": 4
}

Replace Legacy IDP

Swap brittle, template-based processing for one API that returns schema-mapped JSON — no templates to maintain.

ExtractEnrich
IDPTablesOCRStructured JSON

Open-source primitives, composed into one backend. Curated cohort of design partners. Apply to work with us.

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