Accounts Reconciliation
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Digital Archive
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Loan Ops
Docutwin
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Use AI Document Data Extraction to turn documents into structured fields and tables your teams can use across operations. Collatio extracts values with page-level context, preserves table structure, and supports review for edge cases.
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Trusted by teams that extract high-volume document data every day
Document data extraction starts once Collatio digitizes your files into machine-readable content. From there, it detects where information sits on each page, extracts it with context, and structures it into consistent attributes across varied layouts, vendors, and regions.
Collatio accepts machine-readable PDFs, scanned PDFs, and images such as JPEG, JPG, PNG, and TIFF. It also supports business formats such as Excel, CSV, Word, PPT, XBRL/XML, JSON, ZIP archives, and mail. Teams handle mixed inputs through one flow, instead of creating separate processes for each format. This supports large operations where documents arrive in different file types daily.
Collatio uses bounding boxes to locate text blocks, cells, tables, and visual regions on the page. This mapping keeps extracted values tied to the exact source location. Reviewers can confirm outputs faster because they can reference the page region that produces the value. Region-based extraction also holds up when layouts shift across vendors, templates, or scanned versions of the same document.
Collatio performs Document Data Extraction for key-value pairs from printed text and structured layouts. After extraction, document context ontology applies meaning in context. For example, “PO” maps to Purchase Order in procurement documents. This keeps attribute naming consistent and preserves relationships across fields. Teams get structured outputs that reflect business intent, not isolated text fragments.
OCR output often breaks tables into disconnected text and loses row and column meaning. Collatio reconstructs tables so relationships stay intact. This helps teams extract line items, totals, taxes, quantities, and multi-page tables without manual reformatting. Preserved structure supports downstream analysis and reduces errors that come from flattened table text, especially in line-item-heavy documents.
Collatio extracts content beyond standard text fields. It supports handwritten text, signatures, checkboxes, equations, currency notations, and embedded visual fields when they appear in documents. This matters when teams handle scanned inputs, mixed-quality images, and photo captures. The same extraction flow covers these elements, which reduces exception volume and keeps outputs consistent across varied document sources.
Collatio extracts information from visual content such as flow diagrams and charts, including line graphs, pie charts, and bar charts. It detects these regions during extraction and captures the relevant components for structured use. This helps teams process reports and statements where important values sit inside visuals rather than plain text. Review stays faster because the system points to the exact visual region.
Structured document data extraction improves accuracy and speed by converting mixed formats into usable fields and tables with less manual effort.
Digitization accuracy for machine-readable PDFs
Accuracy for scanned PDFs and images
Table extraction accuracy for complex multi-page tables
Language support for contextual understanding across document sets
Collatio extracts fields, tables, and visual regions, then structures them into consistent attributes with page-level traceability for review.
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Collatio digitizes scanned inputs with neural OCR and uses native text for machine-readable PDFs. This creates readable content that extraction can use across varied scan quality and layouts.
Collatio identifies page regions through bounding boxes for text, cells, tables, and visual elements. These anchors improve extraction accuracy and speed up human review.
Collatio extracts key-value pairs, table rows, and embedded elements such as checkboxes, signatures, handwriting, equations, and currency notations when present.
Collatio structures extracted values into standard attributes. Ontology applies the correct meaning in context and preserves relationships across fields and tables.
When confidence drops, Collatio directs reviewers to the exact region on the page. Teams then export structured results through supported outputs such as JSON or CSV, or through API delivery.
ISO 27001 and SOC 2 Type II aligned controls support secure infrastructure and data handling.
GDPR, HIPAA, and CCPA alignment supports lawful processing across environments.
Access control, audit trails, and role-based permissions support accountability.
Collatio supports delivery through API-based workflows and export-ready structured outputs so extracted fields and tables can be used in business processes without manual re-entry.
Extract key terms and line items across layouts, then review only what needs attention.
Discover how SCRY AI solutions bring accuracy and innovation in document processing, conversational AI, and IoT operations.
Below are answers to common questions about Document Data Extraction, from structured field capture and table retention to scanned-file processing and reviewer verification.
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