datathere

Connect Anything

Document Intelligence

AI vision extraction turns scanned PDFs, invoices, and complex documents into structured schemas.

Structured Data Parsing

JSON, XML, CSV, and JSONL files parsed with hierarchy preservation, nested unwrapping, and type inference.

API Schema Detection

Connect an API and datathere detects the response format, handles pagination, and builds the schema from live data.

PDFs are not flat files. datathere does not treat them like one.

Most integration tools stop at text extraction. datathere uses AI vision to understand the structure of a document — tables, hierarchies, entity relationships, and nested sections — and builds a schema from what it finds. Scanned pages, digital text, and mixed documents are all handled through the same pipeline.

  • AI vision extraction identifies entities, tables, and relationships across pages
  • Hybrid processing: digital text used when available, OCR fills the gaps
  • Template caching for repeated document types accelerates future processing
  • Editable extraction prompts let you guide what the AI looks for
PDF Extraction
Source
invoice_march_2026.pdf
48 pages, mixed scan + digital, 3 table regions detected
Extracted Schema
vendor object
name string
tax_id string
line_items array
description string
quantity number
unit_price number
totals object
subtotal number
tax number
Method AI Vision + Digital Text
Fields extracted 24 fields, 3 levels deep

Nested, wrapped, inconsistent — it does not matter

Real-world data files are rarely clean. JSON responses nested six levels deep with arrays of arrays. XML with namespaces and mixed content. JSONL files where every line has a different schema. datathere parses the structure as-is and builds a hierarchical schema that preserves parent-child relationships, array boundaries, and type information.

  • JSON: automatic wrapper unwrapping, nested object traversal, array expansion with dot-notation paths
  • XML: namespace preservation, attribute extraction, recursive element-to-schema conversion
  • CSV: delimiter auto-detection, header inference, encoding fallback for malformed bytes
  • JSONL: schema union across variant records, malformed line recovery, per-line error tracking
Schema Extraction
Input
{"response": {"records": [{"customer": {"name": "...", "addresses": [...]}, "orders": [...]}]}}
Extracted
customer object
name string
addresses array
city string
zip string
orders array
total number
status string
Wrapper response.records unwrapped automatically.

Point datathere at an API. The schema builds itself.

Provide a URL and credentials. datathere calls the endpoint, detects the response format, follows pagination to the end, and extracts a complete field schema from the live data. JSON, XML, CSV, and NDJSON responses are all detected automatically. Nested data paths are resolved through JSONPath expressions.

  • Response format auto-detection from Content-Type headers and content sniffing
  • Supports common pagination styles including offset, cursor, link headers, and next-URL
  • Incremental sync with template variables for delta fetches
  • API key, Basic, Bearer, and OAuth2 authentication with token refresh
API Source
Connection
GET https://api.example.com/v2/transactions
Format
JSON (auto-detected)
Pagination
Cursor-based
Auth
Bearer token
Data path
$.data.transactions
Detected Fields
transaction_id string
amount number
merchant object
name string
category string

Every format becomes the same clean schema

Whether the source is a scanned invoice, a nested API response, or an XML feed with namespaces, datathere standardizes it into the same hierarchical field structure. Parent-child relationships are preserved. Array boundaries are marked. Types are inferred. Sample values are collected. The schema is ready for mapping the moment the source is connected.

  • Hierarchical field paths with parent-child relationships preserved
  • Type inference from actual values, not metadata declarations
  • Sample values stored per field for downstream mapping and quality analysis
  • Multi-file schema merging: upload new versions and the schema evolves
Standardized Schema
PDF | vendor.name string
JSON | customer.addresses.city string
XML | orders.order.line_items.sku string
API | transactions.merchant.category string
CSV | order_total number
Same field structure. Same type system. Same sample values. Regardless of where the data came from.

Bring the messiest source you have