Document AI & OCR

What Is Document AI & OCR?

OCR (optical character recognition) reads text from documents — scanned files, PDFs, photos of receipts, forms. AWS Textract takes that further, identifying not just the text but where it sits on the document: form fields, table rows, line items, signatures.

The raw output, however, is messy. Fields come back in the wrong order. Values are split across lines. Confidence scores vary. A document that looks clean to a human produces output that breaks downstream logic.

Document AI is the layer that fixes this. Using prompt engineering, we take the raw OCR output, validate it against what we expect to find, structure it correctly, and return clean JSON that the rest of your system can depend on. The result is document processing that works reliably at scale — not just on the easy cases.

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Why Document AI & OCR?

If your business handles documents — and most do — there's almost certainly a person somewhere reading them and typing information out of them. Invoices, receipts, applications, claim forms, contracts, identity documents. The process is slow, prone to error, and doesn't scale.

Document AI replaces that manual step. Documents come in, data comes out — validated, structured, and ready to use — without anyone touching them unless something genuinely needs a human eye.

You might need this if:

• Staff are manually keying data from invoices, receipts, or forms into another system
• You're processing high volumes of documents and speed or accuracy is a bottleneck
• You need to extract specific fields from variable document formats reliably
• You have a downstream process — approvals, payments, reporting — that depends on clean input data

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How Do We Build Document AI?

Understand the documents first No two document types are the same. We analyse the real documents your business processes — the formats, the variations, the edge cases — before designing the extraction logic. What works for a standardised invoice won't work for a handwritten form or a low-resolution receipt photo.

Build validation into the extraction Extracting data is only half the job. We build validation rules that check extracted values against what's expected — field types, value ranges, required fields, cross-field consistency. Data that doesn't pass is flagged, not silently passed through.

Handle exceptions explicitly Blurry images, missing fields, low-confidence reads — these are guaranteed to happen at scale. We design exception handling from the start: what gets flagged for human review, what gets rejected, and what triggers a retry or alternative extraction path.

Return structured, usable output Clean JSON, mapped to your data model, ready for whatever comes next — an approval workflow, a database write, an API call. The output is designed around how your system needs to consume it, not just what the document contains.

What we build with: AWS Textract · AWS Bedrock · Prompt Engineering · REST APIs

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Key Benefits

Benefit 1: Eliminates manual data entry Documents are processed the moment they arrive. No queues, no keying errors, no delays waiting for someone to get to them. The data is in your system before a person would have opened the file.

Benefit 2: Consistent accuracy at any volume A person processing 50 documents a day makes different errors than one processing 500. Document AI applies the same validation logic to every document, every time — accuracy doesn't degrade with volume.

Benefit 3: Built for real-world document quality Receipts taken on phones, scanned forms, PDFs exported from legacy systems — real documents aren't clean. We build extraction that handles the variation you actually encounter, not just ideal-format inputs.

Benefit 4: Feeds cleanly into downstream workflows The value of document AI is what happens after extraction. Clean, structured output means approvals can run automatically, payments can be triggered, records can be updated — without a human verifying the data first.