Introduction
Upload a PDF. Search it in milliseconds. Parse any document to clean Markdown.
Context212 gives you an API to search, parse, and ingest documents at scale. Build knowledge-retrieval pipelines without managing vector databases or OCR models.
What you can do
Build a searchable knowledge base
Ingest documents once, then query them. A persistent, indexed corpus you can search or ask questions over.
Ingest
Upload files to a workspace and Context212 turns them into a searchable index automatically, running the whole document-understanding pipeline for you.
Search
Hybrid semantic + lexical search with reranking across your document corpus. Returns ranked chunks with scores, source metadata, and optional page images.
Ask
Retrieval-augmented generation in one call: a grounded LLM answer with the sources it used, returned synchronously or streamed token by token.
Classify and organise documents
Workspaces
Partition your corpus into isolated containers per team, customer, or tenant.
Tags
Group documents into reusable collections that cut across workspaces.
Ontology
Model concepts and relations, then filter Search and Ask by graph connections.
Process documents on the fly
Convert or extract from a document in a single call. Nothing is stored, useful for feeding your own pipeline.
Parse
Convert PDFs, Office files, and images to structured Markdown. Sync mode for quick jobs, async for large documents. Nothing stored either way.
Extract
Pull typed fields out of documents using a JSON Schema you provide. Sync mode for small files, async mode for large jobs.