---
title: "How to Build an AI Document Extraction and OCR Platform 2026"
author: "Nate Laquis"
author_role: "Founder & CEO"
date: "2028-02-23"
category: "How to Build"
tags:
  - build AI document extraction OCR platform
  - intelligent document processing
  - LLM document parsing
  - OCR automation
  - data extraction AI
excerpt: "OCR alone is not enough anymore. Modern document extraction combines computer vision, LLMs, and layout analysis to pull structured data from any document format with 95%+ accuracy, even from handwritten forms and messy scans."
reading_time: "15 min read"
canonical_url: "https://kanopylabs.com/blog/how-to-build-an-ai-document-extraction-ocr-platform"
---

# How to Build an AI Document Extraction and OCR Platform 2026

## Why Traditional OCR Falls Apart on Real-World Documents

Tesseract, ABBYY FineReader, and even early cloud OCR APIs were built for a simpler world. They assumed clean scans, consistent fonts, and predictable layouts. Feed them a crisp W-2 form and they perform admirably. Feed them a crumpled receipt photographed at an angle under fluorescent lighting, and you get garbage. The problem is not character recognition anymore. Modern OCR engines recognize characters with 99%+ accuracy on clean text. The problem is everything that surrounds the characters: layout interpretation, table detection, field association, and semantic understanding of what each piece of extracted text actually means.

Consider an invoice. A traditional OCR engine can read every word on the page. But it cannot tell you that "$4,372.50" is the invoice total rather than a line item subtotal unless you hard-code positional rules for that specific vendor layout. When a new vendor sends invoices with a completely different format, those rules break. Multiply this by 200 vendors and you have a maintenance nightmare that consumes engineering hours every single week.

The shift happening in 2026 is fundamental. Document extraction platforms now combine three capabilities that did not coexist before: pixel-level OCR for character recognition, layout analysis models (like LayoutLMv3 and Donut) for understanding document structure, and LLMs for semantic interpretation and structured output generation. This three-layer approach handles documents that would have required months of custom template engineering just two years ago. The accuracy gap between the old approach and the new one is not marginal. On semi-structured documents like invoices, purchase orders, and insurance forms, LLM-powered extraction achieves 93 to 97% field-level accuracy compared to 75 to 88% for template-based OCR pipelines.

![Developer writing code for an AI document extraction platform on a laptop](https://images.unsplash.com/photo-1555949963-ff9fe0c870eb?w=800&q=80)

The business case is equally stark. Companies processing 50,000+ documents per month through manual data entry spend $150,000 to $500,000 annually on labor alone. Error rates for human data entry hover around 1 to 4%, which sounds acceptable until you realize that a 2% error rate on 50,000 financial documents per month means 1,000 records that need correction. An AI extraction platform cuts that cost by 85 to 95% while actually reducing the error rate below human performance. That is not a marginal improvement. It is a category shift in operational efficiency.

## The Modern Extraction Pipeline: Five Stages from Raw Document to Structured Data

Building a production-grade extraction platform requires a clear pipeline architecture. Each stage has a distinct responsibility, and separating them makes the system debuggable, testable, and independently scalable. Here is the architecture we use for every extraction platform we build.

**Stage 1: Pre-processing and normalization.** Raw documents arrive in wildly different formats. PDFs, JPEGs, PNGs, TIFFs, HEIC photos from mobile phones, multi-page scans as single files. The pre-processing stage normalizes everything into a consistent format for downstream consumption. PDFs get split into individual page images at 300 DPI (the sweet spot for OCR accuracy without excessive file size). Images get deskewed using OpenCV or the ImageMagick deskew function. Contrast enhancement (adaptive histogram equalization) improves readability on faded or low-contrast scans. Pages get rotation-corrected using the Tesseract OSD (orientation and script detection) module, which detects page orientation with 98%+ accuracy. Store normalized images in S3 or GCS and create a processing manifest in your database.

**Stage 2: OCR and text extraction.** For born-digital PDFs (created from software rather than scanned), you can skip OCR entirely and extract text directly using libraries like PyMuPDF or pdfplumber. This is faster, cheaper, and more accurate. For scanned documents and images, run OCR to convert pixels to text. The best OCR engines in 2026 are Google Cloud Vision API ($1.50 per 1,000 pages), AWS Textract ($1.50 per 1,000 pages for basic text detection), and Azure AI Document Intelligence ($1.00 per 1,000 pages). All three achieve 98 to 99% character-level accuracy on printed text. For handwritten text, accuracy drops to 85 to 93% depending on legibility. Tesseract remains a viable free alternative for simpler use cases, though accuracy trails the cloud APIs by 3 to 5 percentage points on challenging documents.

**Stage 3: Layout analysis and document understanding.** This is the stage that separates toy demos from production systems. Layout analysis identifies the structural components of a document: headers, footers, tables, key-value pairs, paragraphs, signatures, stamps, and logos. Tools like LayoutLMv3 (Microsoft), Donut (Naver), and Unstructured.io detect these elements and preserve their spatial relationships. Table detection is especially critical. A well-extracted table preserves row/column associations so that "Widget A" stays linked to "Qty: 50" and "Price: $12.00." Without layout analysis, OCR output is just a stream of text with no structural meaning. If you want to go deeper on parsing tool selection, our [comparison of Unstructured, LlamaParse, and Docling](/blog/unstructured-vs-llamaparse-vs-docling-document-parsing) breaks down the trade-offs in detail.

**Stage 4: LLM-powered extraction and structuring.** This is where the magic happens. You take the OCR text and layout information from the previous stages and send it to a vision-capable LLM (GPT-4o, Claude 3.5 Sonnet, or Gemini 2.0 Pro) with a prompt that specifies exactly which fields to extract and the target JSON schema. The LLM reads the document semantically, understands context, and returns structured data. A single well-crafted prompt handles hundreds of layout variations for the same document type. No templates. No regex. No vendor-specific rules. For a thorough walkthrough of the end-to-end pipeline architecture, see our guide on [building an AI document processing pipeline](/blog/how-to-build-an-ai-document-processing-pipeline).

**Stage 5: Validation, scoring, and output.** Every extracted field gets validated against business rules and scored for confidence. Fields that pass validation go directly to your downstream systems. Fields that fail get routed to a human review queue. The output stage writes structured data to your target systems via adapters: database inserts, ERP API calls, webhook notifications, or file exports. Build idempotency into every output adapter so retries never create duplicate records.

## LLM-Powered Extraction vs. Template Matching: When to Use Each

This is the decision that shapes your entire platform. Template matching and LLM extraction are not just different tools. They represent fundamentally different approaches to the problem, and choosing wrong costs you months of wasted effort.

**Template matching** works by defining exact zones on a document where specific fields appear. You tell the system: "The invoice number is always at coordinates (420, 85) to (580, 105) on Vendor A invoices." The system reads those coordinates and extracts the text. This approach is fast (milliseconds per document), cheap (no LLM API costs), and extremely accurate when the template is correct. AWS Textract Queries, Azure custom models, and Google Document AI custom processors all support template-based extraction. The catch is obvious: every new vendor layout requires a new template. If you process documents from 50 vendors, you need 50 templates. If a vendor updates their invoice design, the template breaks silently and starts extracting wrong values until someone notices.

**LLM extraction** skips coordinate mapping entirely. You describe what you want in natural language: "Extract the invoice number, vendor name, line items with quantities and prices, subtotal, tax, and total from this document. Return JSON." The LLM uses its understanding of document semantics to find and extract the right values regardless of where they appear on the page. One prompt covers hundreds of vendor layouts. Adding a new vendor requires zero engineering effort. The trade-off is cost ($0.03 to $0.15 per page vs. $0.001 to $0.01 for template matching) and latency (2 to 8 seconds vs. 100 to 500 milliseconds).

Here is our decision framework after building extraction platforms across industries:

- **Use template matching** when you process more than 500,000 documents per month of the same 1 to 3 fixed layouts, cost per document must stay below $0.01, and latency requirements are under 500ms.
- **Use LLM extraction** when you process documents from more than 10 different sources or layouts, you need to support new document types without engineering effort, documents are semi-structured or unstructured, or accuracy above 93% matters more than per-document cost.
- **Use a hybrid approach** when you have a mix of high-volume structured documents (template match these) and variable-layout semi-structured documents (LLM extract these). Route documents to the appropriate extraction method based on classification.

Most teams we work with land on the hybrid approach. They template-match their top 3 to 5 highest-volume structured document types and LLM-extract everything else. This keeps costs reasonable while maintaining the flexibility to handle any document that arrives. The classification step (which itself can be an LLM call costing $0.001 per document via GPT-4o mini) routes each document to the right extraction path automatically.

## Handling Edge Cases: Rotations, Poor Scans, Handwriting, and Mixed Languages

Edge cases are where extraction platforms earn their keep. Any system can extract data from a clean, well-formatted PDF. The question is what happens when reality intervenes. And reality always intervenes.

**Rotated and skewed pages.** Documents scanned on flatbed scanners or photographed with phones frequently arrive rotated 90, 180, or 270 degrees, or skewed by 5 to 15 degrees. Your pre-processing pipeline needs to detect and correct orientation before OCR. The Tesseract OSD module detects page orientation and script with high reliability. For programmatic correction, OpenCV provides affine transformation functions that straighten skewed images. Run deskew correction with a minimum angle threshold (typically 0.5 degrees) to avoid unnecessary resampling on already-straight documents. One subtlety: some documents are intentionally rotated (landscape tables embedded in portrait documents). Your pipeline should detect these and handle them as sub-regions rather than rotating the entire page.

**Poor scan quality.** Faded thermal receipts, documents with coffee stains, photocopies of photocopies, faxed documents (yes, fax machines still exist in healthcare and legal). The pre-processing stack for low-quality scans includes adaptive thresholding (Otsu method or Sauvola method), noise removal (median filtering or non-local means denoising), and contrast enhancement (CLAHE, contrast limited adaptive histogram equalization). For severely degraded documents, run OCR with multiple engine configurations and compare outputs. Google Cloud Vision handles degraded scans better than AWS Textract in our testing, particularly for receipts with faded thermal print. Set a minimum quality threshold and route documents below that threshold to human processing rather than producing unreliable extractions.

![Data center server infrastructure powering high-volume AI document processing workloads](https://images.unsplash.com/photo-1558494949-ef010cbdcc31?w=800&q=80)

**Handwritten text.** Handwriting recognition has improved dramatically but remains the hardest OCR problem. Google Cloud Vision and Azure AI Document Intelligence both offer handwriting recognition APIs, achieving 85 to 93% character accuracy on reasonably legible handwriting. For forms that mix printed and handwritten fields (the most common scenario), use layout analysis to identify handwritten regions and process them separately with handwriting-specific models. Set confidence thresholds aggressively low for handwritten fields (accept only above 80% confidence) and route the rest to human review. Do not promise clients 95%+ accuracy on handwritten documents. It is not achievable with current technology on real-world samples.

**Mixed languages and scripts.** Global companies process documents in dozens of languages. The good news is that modern LLMs handle multilingual extraction natively. GPT-4o and Gemini 2.0 Pro both process documents in 50+ languages without any language-specific configuration. The OCR layer needs more attention. Tesseract requires language-specific models. Cloud OCR APIs (Google, AWS, Azure) auto-detect language in most cases but struggle with documents that mix multiple scripts on the same page (common in shipping documents with Chinese, English, and Arabic). For multi-script documents, use language detection per text block and route each block to the appropriate OCR configuration. Google Cloud Vision handles this best among the cloud providers, correctly processing mixed-script documents 94% of the time in our benchmarks.

**Tables and nested structures.** Table extraction is deceptively hard. A table that looks obvious to a human is a complex spatial reasoning problem for a machine. Cells can span multiple rows or columns. Headers can appear on the left, top, or both. Some tables have visible grid lines while others rely on whitespace alignment alone. For reliable table extraction, use a dedicated table detection model (Table Transformer or DETR-based detectors) to identify table regions, then extract cell contents and structure separately. AWS Textract Tables API and Azure Layout API both offer table-specific extraction that preserves row/column structure. For complex nested tables, LLM-based extraction with a detailed schema prompt outperforms all rule-based approaches.

## Structured Output Schemas and API Design for Downstream Integration

Your extraction platform is only as valuable as the structured data it produces. Sloppy output schemas create integration headaches that haunt you for years. Getting the data model right from the start saves enormous rework later.

**Define schemas per document type.** Every document type in your platform should have a formal JSON schema that specifies required fields, optional fields, data types, and validation constraints. For an invoice extraction schema, that looks like: invoice_number (string, required), vendor_name (string, required), vendor_address (string, optional), invoice_date (ISO 8601 date string, required), due_date (ISO 8601 date string, optional), line_items (array of objects, each with description, quantity as number, unit_price as number, and amount as number), subtotal (number, required), tax (number, optional), total (number, required), currency (ISO 4217 code string, required), and payment_terms (string, optional). Use JSON Schema or Zod (for TypeScript platforms) to define and validate these schemas programmatically.

**Enforce structured output from LLMs.** Never let the LLM return free-form text and then try to parse it. Use structured output modes: OpenAI JSON mode with a response_format schema, Anthropic tool use with a defined tool schema, or Gemini controlled generation with a response schema. These modes guarantee that the LLM response conforms to your schema, eliminating an entire class of parsing errors. When using Claude for extraction, define a tool whose input schema matches your extraction schema, and the model will populate every field precisely.

**Include confidence metadata.** Every extracted field should carry a confidence score alongside its value. This is not just a nice-to-have. Downstream systems need confidence scores to make routing decisions. A field with 95%+ confidence goes straight to the ERP. A field with 70 to 94% confidence gets flagged for quick human verification. A field below 70% gets sent to full manual review. Structure your output as an array of field objects, each containing field_name, value, confidence (0.0 to 1.0), and source_location (page number and bounding box coordinates for audit trails).

**API design for consuming applications.** Your extraction platform should expose a clean REST or gRPC API that downstream applications can integrate with. The core endpoints are: POST /documents (upload a document, receive a job ID), GET /documents/{id}/status (check processing status), GET /documents/{id}/results (retrieve structured extraction results), and POST /documents/{id}/corrections (submit human corrections for feedback loops). Support both synchronous mode (wait for result, suitable for single-document processing under 30 seconds) and asynchronous mode (submit and poll, required for batch processing or complex multi-page documents). Include webhook support so consuming applications can receive real-time notifications when extraction completes rather than polling. For batch processing, expose a POST /batches endpoint that accepts ZIP archives or S3 manifest files containing hundreds or thousands of documents.

**Versioning and backward compatibility.** Your extraction schemas will evolve. New fields get added. Validation rules change. Output formats shift. Version your API and your extraction schemas independently. Use semantic versioning for the API (v1, v2) and date-based versioning for extraction schemas (2026-01, 2026-06). Never remove a field from an existing schema version. Instead, deprecate it and add the replacement in a new version. This discipline prevents breaking downstream integrations every time you improve the extraction logic. For details on building robust data pipelines that feed extracted data into enrichment workflows, see our guide on [AI data extraction and enrichment pipelines](/blog/how-to-build-an-ai-data-extraction-and-enrichment-pipeline).

## Human-in-the-Loop Validation and Accuracy Benchmarking

No extraction platform should run without a human review layer. Even the best LLM-based extraction makes mistakes, and for business-critical documents like financial records, contracts, and compliance forms, a single wrong value can be expensive. The goal is not to eliminate human involvement entirely. The goal is to reduce it to the 5 to 15% of documents where the system is genuinely uncertain.

**Designing the review queue.** Your review interface should show the original document image alongside the extracted data in an editable form. Reviewers need to see exactly what the system extracted, compare it visually to the source document, and correct any errors with minimal clicks. Group fields by confidence level: high-confidence fields appear pre-filled and greyed out (reviewers can still override them but do not need to check each one). Low-confidence fields appear highlighted in yellow or red, drawing the reviewer directly to the fields most likely to contain errors. This targeted review approach lets a trained reviewer process a flagged document in 30 to 60 seconds rather than re-entering every field from scratch.

**Feedback loops that improve accuracy over time.** Every human correction is a training signal. Store corrections in a structured format: original_value, corrected_value, field_name, document_type, and a hash of the source document region. Aggregate these corrections weekly and analyze patterns. If reviewers consistently correct the "due_date" field on a specific vendor invoices, your extraction prompt for that field needs refinement. If handwritten address fields are always wrong, your confidence threshold for handwritten text is too high. Build a dashboard that tracks correction rates by field, by document type, and by source. Over a 3-month period, a well-maintained feedback loop reduces the human review rate from 15 to 20% down to 5 to 8%.

**Accuracy metrics that matter.** Track four metrics at minimum. First, field-level accuracy: the percentage of individual extracted fields that match the ground truth value exactly. Target: 95%+ for structured documents, 92%+ for semi-structured, 88%+ for unstructured. Second, document-level accuracy: the percentage of documents where every single field is extracted correctly. This is always lower than field-level accuracy because one wrong field fails the entire document. Target: 85%+ for most document types. Third, human review rate: the percentage of documents routed to human review. Lower is better, but pushing this below 5% often means you are letting errors through. Target: 5 to 12%. Fourth, end-to-end processing time: from document upload to structured data available in downstream systems. Target: under 30 seconds for single documents, under 5 minutes per document for batch processing.

![Lines of code on a monitor displaying document extraction validation logic](https://images.unsplash.com/photo-1461749280684-dccba630e2f6?w=800&q=80)

**Building your benchmark dataset.** You need a labeled test set of at least 200 documents, ideally 50+ per document type you support. For each document, manually extract every field to create ground truth labels. Store these as JSON files alongside the source document images. Run your extraction pipeline against this benchmark after every prompt change, model upgrade, or pipeline modification. Automate this as a CI/CD step. If a change drops field-level accuracy by more than 0.5 percentage points on any document type, block the deployment. This discipline is the single most important practice for maintaining extraction quality over time. Without it, accuracy degrades silently as prompts evolve and models get updated.

**A/B testing extraction approaches.** When evaluating a new extraction prompt, a different LLM, or a pipeline configuration change, run the new version in shadow mode alongside the production version. Both versions process every document, but only the production version feeds results to downstream systems. Compare accuracy metrics between the two versions on live traffic for at least 1,000 documents before promoting the new version. This catches real-world regressions that synthetic benchmarks miss, particularly around document formats and quality levels that your test set does not represent.

## Scaling to High-Volume Processing: Architecture and Cost Optimization

Processing 1,000 documents per month is straightforward. Processing 1,000,000 documents per month requires deliberate architectural choices around concurrency, cost management, and infrastructure scaling. Here is what changes at volume.

**Queue-based architecture is non-negotiable.** Every document enters a message queue (SQS, Google Cloud Tasks, or BullMQ on Redis) immediately upon upload. Worker processes pull documents from the queue at a controlled rate. This decouples ingestion from processing, prevents overloading your LLM API rate limits, and provides automatic retry with exponential backoff for transient failures. At 100,000+ documents per month, run separate queues for different processing stages (pre-processing, OCR, extraction, validation) so that a bottleneck in one stage does not block the others. Use dead-letter queues to capture documents that fail repeatedly, and build alerting around dead-letter queue depth.

**Tiered model selection based on document complexity.** Not every document needs GPT-4o. A clean, structured W-2 form can be extracted accurately by GPT-4o mini at one-tenth the cost. Build a classification step that routes simple documents to cheaper, faster models and reserves expensive models for complex documents. A practical tiering strategy: use GPT-4o mini or Claude 3.5 Haiku ($0.01 to $0.03 per page) for structured documents with fixed layouts, GPT-4o or Claude 3.5 Sonnet ($0.05 to $0.12 per page) for semi-structured documents, and GPT-4o or Claude Opus ($0.10 to $0.25 per page) for complex unstructured documents like contracts. This tiered approach reduces average per-document cost by 40 to 60% compared to routing everything through the most capable model.

**Caching and deduplication.** In high-volume environments, the same document frequently gets submitted multiple times (duplicate email forwards, re-scans, retry logic in upstream systems). Hash every incoming document (perceptual hash for images, content hash for PDFs) and check against a cache of recently processed documents. If a match exists, return the cached result instantly. This eliminates 5 to 15% of redundant processing in typical enterprise environments. Beyond exact deduplication, cache extraction results for recurring templates. If you process 10,000 invoices per month from the same vendor with the same layout, cache the extraction prompt and schema so the system skips classification for known formats.

**Horizontal scaling with serverless compute.** AWS Lambda, Google Cloud Functions, and Azure Functions all scale automatically from zero to thousands of concurrent executions. For the compute-light stages (pre-processing, validation, output), serverless is ideal. The extraction stage (which calls external LLM APIs) is also a good fit for serverless because the function just waits for an API response. At very high volumes (500,000+ documents per month), consider dedicated container instances (ECS Fargate, Cloud Run, or Kubernetes pods) for the pre-processing stage, which benefits from GPU acceleration for image manipulation. Keep the LLM extraction stage on serverless to take advantage of automatic concurrency scaling.

**Cost projections at scale.** Here are realistic monthly costs based on platforms we have built and operated:

- **10,000 documents/month:** LLM extraction $80 to $350 (tiered models). OCR $15 to $65. Infrastructure (compute, storage, queues, database) $50 to $150. Total: $145 to $565.
- **100,000 documents/month:** LLM extraction $600 to $2,500. OCR $100 to $500. Infrastructure $200 to $600. Total: $900 to $3,600.
- **1,000,000 documents/month:** LLM extraction $4,000 to $15,000. OCR $800 to $3,000. Infrastructure $800 to $2,500. Total: $5,600 to $20,500.

At every volume tier, the AI extraction platform costs a fraction of manual processing. A single full-time data entry operator costs $3,500 to $5,000 per month and processes roughly 3,000 to 5,000 documents. At 100,000 documents per month, you would need 20 to 33 data entry operators costing $70,000 to $165,000 monthly. The extraction platform handles the same volume for under $4,000.

## Getting Started: Timeline, Stack Recommendations, and Next Steps

If you have read this far, you are probably evaluating whether to build an extraction platform for your organization or your clients. Here is a pragmatic roadmap based on dozens of extraction platforms we have shipped.

**Phase 1: Proof of concept (1 to 2 weeks).** Pick your single highest-volume document type. Collect 50 real sample documents. Build a minimal pipeline: upload a document image, send it to GPT-4o or Claude 3.5 Sonnet with a structured extraction prompt, and display the JSON output. No queue, no validation, no review UI. Just prove that the extraction accuracy is viable on your specific documents. Measure field-level accuracy manually against the 50 samples. If you hit 90%+ accuracy, proceed. If not, investigate whether pre-processing improvements (better OCR, deskewing, contrast enhancement) close the gap.

**Phase 2: MVP with validation (3 to 5 weeks).** Add the full pipeline: pre-processing, OCR, classification, extraction, validation rules, and output to your target system. Build a simple review UI for flagged documents. Integrate with one downstream system (your database, ERP, or accounting software). Deploy on serverless infrastructure. Process your first 500 real documents and measure accuracy on a labeled benchmark set of 100 documents. Budget: $20,000 to $45,000 if working with an experienced team.

**Phase 3: Production hardening (4 to 6 weeks).** Add support for 3 to 5 document types. Implement tiered model routing. Build comprehensive validation rules per document type. Add audit logging, error tracking (Sentry or Datadog), and monitoring dashboards. Implement the feedback loop from human corrections back to prompt refinement. Load test to your target volume. Set up CI/CD with automated benchmark testing. Budget: $35,000 to $75,000 additional.

**Phase 4: Scale and optimize (ongoing).** Add new document types as business needs evolve. Optimize per-document costs through model tiering and caching. Fine-tune classification models on your specific document mix. Expand API integrations for new downstream consumers. Continuously improve accuracy using correction data from the feedback loop.

**Our recommended technology stack:**

- **Primary extraction:** GPT-4o for documents with complex tables, Claude 3.5 Sonnet for long-form documents and contracts. GPT-4o mini or Claude 3.5 Haiku for high-volume structured documents.
- **OCR layer:** Google Cloud Vision API for general OCR. Azure AI Document Intelligence for handwriting. PyMuPDF for born-digital PDF text extraction.
- **Layout analysis:** Unstructured.io for multi-format parsing. LayoutLMv3 for custom layout models. Table Transformer for dedicated table extraction.
- **Infrastructure:** AWS Lambda or Google Cloud Functions for compute. S3 or GCS for document storage. PostgreSQL for metadata and results. SQS or Cloud Tasks for queuing. Redis for caching and deduplication.
- **Monitoring:** Datadog or Grafana for pipeline metrics. Sentry for error tracking. Custom dashboards for accuracy tracking and review queue depth.

Document extraction is one of the highest-ROI AI investments a company can make. The technology is mature, the accuracy is proven, and the cost savings are immediate and measurable. The difference between a system that works in a demo and one that works in production comes down to engineering rigor: proper pre-processing, robust validation, human-in-the-loop workflows, and continuous benchmarking.

If you want to skip the trial-and-error phase and go straight to a production-grade extraction platform built by a team that has done this before, [book a free strategy call](/get-started). We will assess your document types, recommend the right architecture for your volume and accuracy requirements, and give you an honest timeline and budget to get to production.

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*Originally published on [Kanopy Labs](https://kanopylabs.com/blog/how-to-build-an-ai-document-extraction-ocr-platform)*