Extract, validate, and structure data from PDFs, Word files, scans, images, and handwriting—without rebuilding the model whenever a format changes. AgenticSwift adapts at runtime, flags uncertainty, and sends clean data into the systems your team already uses.
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Validated extraction accuracy
Faster document processing
Faster document processing
Automated pipeline
No reformatting, template cleanup, or document redesign required. AgenticSwift reads structured, semi-structured, and unstructured documents as they arrive—whether they are digital, scanned, photographed, or handwritten.
Extract data from digital and scanned PDFs, including multi-page files and changing layouts.
Process DOC and DOCX files across different templates, sections, and formatting styles.
Read JPG, PNG, and TIFF files, including clear mobile photos of physical documents.
Convert scanned paperwork into searchable, structured, machine-readable data.
Recognize cursive and hand-printed text using intelligent character recognition, with confidence scoring for review.
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Select your industry to see the documents AgenticSwift can process and the workflows your team can automate.
Conventional OCR can read familiar text, but document workflows fail when layouts change, handwriting appears, or extracted values need validation. AgenticSwift combines intelligent document processing with runtime adaptation and field-level controls to turn changing documents into usable data.
| The challenge | How AgenticSwift’s AI Doc Parser solves it |
|---|---|
| Hours lost to manual data entryTeams repeatedly re-key names, dates, totals, and line items from forms, invoices, and contracts. | Every field extracted in one pass Names, dates, totals and line items into structured data in seconds — no typing, no copy-paste. |
Costly errors and rework A mistyped value can cause rework, compliance exposure, delayed decisions, or lost revenue. | Confidence-scored accuracy Score extracted fields and route exceptions for targeted review instead of allowing silent errors.confidence scoring |
Too many formats to handle New forms, revised schedules, unusual layouts, and handwritten notes create exceptions conventional pipelines cannot handle. | Reads any format, any layout Process new and changing document structures without a model-retraining cycle for every variation. |
Volume that demands more headcount Manual review effort rises with document volume, backlogs, and seasonal peaks. | Scale without growing your team Run documents through an always-on pipeline and reserve human attention for exceptions. |
No templates to build and no extraction rules to rewrite for every new layout.
Upload files, connect an inbox or folder, or send documents through the API. Process PDFs, Word files, scans, images, and handwritten documents from the sources your workflow already uses.
AgenticSwift classifies each document, recognizes printed and handwritten content, extracts the required fields, and applies confidence checks. When the structure changes, the parser adapts rather than immediately failing to a manual queue.
Deliver clean, machine-readable data to your ERP, CRM, claims platform, policy administration system, database, or review interface through APIs and integrations.
Reading text is only the first step. AgenticSwift combines multiple document AI technologies to interpret content, layout, and context across the documents businesses actually receive.
Reads printed text from PDFs, scans, and images.
Recognizes hand-printed and cursive handwriting and assigns field-level confidence.
Understands document layout, tables, checkboxes, stamps, signatures, and spatial relationships.
Uses context to identify the right entities and fields in unstructured content.
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Choose a document type to see how the parser identifies fields, structures the output, and shows a confidence score for each extracted value.
AgenticSwift is built for production document workflows: adaptable extraction, visible uncertainty, and structured outputs that connect to your existing systems.
Process changing layouts and previously unseen document variations without starting a new model-training cycle each time.
When a document deviates from the expected structure, the parser adapts at runtime instead of simply breaking or sending the entire file to manual review.
Confidence scores show exactly which values may need attention, so reviewers focus on exceptions instead of rechecking every field.
Process scans, photos, handwriting, tables, signatures, and irregular layouts—not only clean, template-based files.
Send validated outputs to ERP, CRM, claims, policy administration, database, and review systems through APIs and integrations.
Work with AI engineers to scope document types, configure outputs, integrate systems, and support production deployment.
Manual Review vs. Generic OCR vs. Adaptive Document Intelligence
| What matters to you | Manual data entry | Generic OCR tools | AgenticSwift’s AI Doc Parser |
|---|---|---|---|
| Changing document formats | Requires manual interpretation | Often needs templates or reconfiguration | Adapts at runtime without retraining for each format |
| Unstructured and handwritten content | Handled slowly by people | Limited or inconsistent | OCR, ICR, computer vision, and NLP work together |
| Visibility into uncertainty | Depends on the reviewer | Often limited | Confidence scoring at field level |
| Human review workload | Review every document | Review many extraction failures | Review the exceptions that need attention |
| Output | Manually entered data | Text or basic exports | Structured, validated, machine-readable data |
| Scaling volume | Requires more people | Can plateau on complex documents | Parallel, always-on document processing |
Replace the values below with measured results from a customer deployment or controlled pilot before publishing.
Faster processing <pFrom document receipt to usable data
Verified accuracy
Extraction and validation performance on supported documents.
Verified throughput
Documents processed in parallel at peak volume
Verified cost reduction
Lower manual data-entry and review cost
Document workflows may contain confidential and regulated information. AgenticSwift supports deployment controls designed to protect files, limit access, and preserve traceability.
Pricing is based on document volume, workflow complexity, and the systems you need to connect. Begin with a focused pilot using your own documents, then expand after the results are measured.
Test representative files and define the fields, validations, and outputs your workflow requires.
Align pricing with document volume, processing complexity, and integration requirements.
Scope document types, configure outputs, connect downstream systems, and move from pilot to production with engineering support.
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See What AgenticSwift Can Automate for You
Share a representative sample and use a 15-minute discovery call to identify the fields we can extract, the exceptions that require review, the systems we can connect, and the measurable time your team could save.
AgenticSwift processes digital and scanned PDFs, DOC and DOCX files, JPG, PNG and TIFF images, clear document photos, and handwritten content. It can work with structured, semi-structured, and unstructured layouts.
Yes. Intelligent character recognition helps read hand-printed and cursive text. Each extracted field can include a confidence score so uncertain values can be reviewed before they move downstream.
No model-retraining cycle is required for every new format or layout variation. The parser is designed to adapt at runtime when document structure changes. Configuration may still be required when you introduce a new business objective, new target fields, or new validation rules.
Accuracy depends on document quality, complexity, field type, handwriting, and validation requirements. During a pilot, AgenticSwift measures performance on representative documents and defines how low-confidence values should be reviewed. Publish a specific accuracy percentage only after it has been verified for the stated use case.
Generic OCR primarily converts printed text into machine-readable text. AgenticSwift combines OCR, ICR, computer vision, NLP, document classification, field extraction, confidence scoring, and runtime adaptation to deliver structured data for business workflows.
Yes. It can identify and extract relevant information from unstructured and semi-structured files, including documents whose content or layout varies from one submission to another.
Computer vision can identify layout elements such as tables, checkboxes, stamps, and signatures. The exact detection and validation requirements should be confirmed during the pilot for your document set.
Yes. Structured outputs can be delivered to ERP, CRM, claims, policy administration, database, or review systems through APIs and configured integrations.
See AgenticSwift Work on Your Documents
Bring a representative file to a 15-minute discovery call. We will map the fields you need, show how the parser structures the data, identify where validation or human review belongs, and outline a focused pilot—without requiring a credit card or long-term commitment.
No credit card required. Evaluation terms apply to submitted sample documents.
