Extend alternative
Extend turns documents into clean output for the search and agents you build. Meibel turns them into one queryable dataset, then runs search, SQL and agents on it.
Meibel vs Extend
Document-processing infrastructure, and good at it: 35+ file types, layout-aware output, per-field citations and confidence, a Review Agent, a reviewer interface and versioned workflows. Its stated output is clean markdown and blocks for the retrieval and agents you bring.
Documents become data. Data becomes agents. Meibel reads any document zero-shot into a schema you define, stores it for search and SQL, scores every step, and runs agents on it inside our guardrails, we call them Hardrails.
What you still build
25+ formats, tables kept as data, handwriting, every value at its source location.
Ships it, and well: 35+ formats and per-field citations.
You decide what a low score triggers: a retry, a person or a gate.
Ahead of us here: reviewers edit, reject or reclassify, with a written justification.
Semantic search on the same corpus, built in.
Not in their documentation. Their output feeds a retrieval stack you build.
Built in. The database does the math, not the model.
Not documented.
One dataset, with no file-count ceiling.
COLLECT reasons across a package of up to 50 files.
Agents built, versioned and run on the same data, inside Hardrails that set what each one can see and call.
Document workflows that deliver to a webhook. Per-tenant query isolation isn't documented.
What Meibel adds
"What was the mean across these four thousand certificates?" is a query, and Meibel answers it in SQL.
No embedding service, vector database or reranker to buy and run beside the parser.
Run them in Claude, Copilot, your own apps or in batch, with every step scored and sourced.
Yes, on correcting a field: editing, rejecting and reclassifying with a written reason. We say so. Meibel's strength is everything after the document: one dataset, search, SQL and agents on the same data.
The parse and extraction move over cleanly. What Meibel replaces is what you built after it: the embedding step, the vector database, the retrieval tier and the orchestration around them.
We don't publish accuracy claims we can't source. Run both on your own documents and measure them against your own answers.
When document processing is the whole job and the output goes into a webhook and a system of record. When someone needs to ask across all of it, or an agent works on it, that's Meibel.
Try it on the whole corpus
A batch in twenty different shapes from twenty sources. Get one schema back across all of them, then query it and build an agent on it.
Based on Extend's public documentation, retrieved September 2026: parsing, workflows, review. Where their documentation is silent we say "not documented". We update this page when products change. See the use cases.