Airparser

Airparser accepts documents through email forwarding, direct upload, or API and applies GPT-powered extraction rules to pull named fields into structured output. The core workflow is declarative: you tell the tool what fields to extract, it learns the pattern from examples, and every subsequent document of that type gets processed against the same schema. Output lands in JSON, CSV, or directly in connected tools via webhooks and integrations. No code is required to set up basic extraction — which is the point for operations teams who are not running an engineering function.

The differentiating feature is the breadth of document types it handles without format-specific preprocessing. Invoices, purchase orders, CVs, contracts, handwritten forms, and email bodies all pass through the same extraction interface rather than requiring separate tools or parsers per file type. For teams receiving documents in mixed formats from external sources they do not control, this matters — the alternative is either a purpose-built parser per document category or significant manual triage upstream.

Airparser fits cleanly inside mid-volume document intake workflows where the document structure is reasonably consistent and the goal is eliminating data entry rather than building a decision engine. It breaks down when documents contain tables that span multiple pages, when extracted fields need to be validated against external data sources before they land, or when business logic beyond extraction — approval routing, anomaly flagging — needs to be embedded in the same pipeline. Teams that need those capabilities typically add a workflow automation layer on top, or migrate to a document processing platform that bundles extraction with process logic. The API is available for teams that want to push documents programmatically rather than through the web interface.