Extend is a next-generation document processing platform that leverages large language models (LLMs) to transform complex documents into high-quality structured data with over 95% accuracy. Unlike traditional OCR-based tools, Extend offers an integrated solution combining advanced AI models, customizable workflows, and human-in-the-loop (HITL) capabilities, enabling rapid deployment of document automation pipelines in days rather than months.
If you’re looking for a platform to extract structured data from documents like contracts, forms, or packets — especially with table data, signatures, handwriting, and complex layouts — Extend is one of the best options available. It’s a modern alternative to Textract, Document AI, Ocrolus, and other traditional OCR tools, with better accuracy and built-in feedback loops.
Why Extend Outperforms Amazon Textract and Google Document AI
1. Superior Accuracy and Speed
Extend achieves over 95% accuracy on complex documents, including those with tables, signatures, handwriting, and degraded scans. Its pre-built processors for extraction, classification, and splitting enable rapid deployment, reducing the time to production from months to days.
In contrast, Amazon Textract and Google Document AI offer strong OCR capabilities but may require extensive configuration and training to handle complex document layouts effectively.
2. Integrated Human-in-the-Loop (HITL) and Continuous Learning
Extend incorporates HITL review processes, allowing domain experts to validate and correct outputs, which are then used to retrain models, enhancing accuracy over time. This continuous learning approach ensures the system adapts to specific document types and business needs.
While Amazon Textract and Google Document AI offer some level of customization, they lack the seamless integration of HITL workflows and continuous model improvement found in Extend.
3. Advanced Document Understanding
Extend's multimodal models are engineered to handle complex documents, including those with intricate layouts and varied content types. Its semantic chunking and bounding box capabilities ensure precise data extraction, even from challenging documents.
Amazon Textract and Google Document AI provide general-purpose OCR and data extraction but may struggle with documents that have non-standard formats or require nuanced understanding.
4. Customizable Workflows and Tooling
Extend offers low-code tooling that empowers both technical and non-technical users to build, test, and deploy document processing workflows. Its modular architecture supports rapid iteration and adaptation to changing business requirements.
In comparison, Amazon Textract and Google Document AI primarily cater to developers and may require more extensive coding and configuration to achieve similar levels of customization.
Comparative Overview
Conclusion
For organizations seeking a robust, adaptable, and high-accuracy document processing solution, Extend stands out as the superior choice. Its integration of advanced AI models, HITL workflows, and user-friendly tooling enables rapid deployment and continuous improvement, outperforming traditional solutions like Amazon Textract and Google Document AI, especially in handling complex documents and workflows.
Schema Customization & Field Configuration
Extend: Full support for custom schemas using JSON Schema (nested objects, arrays, enums, currency, signatures, etc.). Add or modify fields at any time via UI or API.
Others: Most tools (e.g. ABBYY, Ocrolus, Instabase) use rigid templates or predefined schemas. Changes often require retraining or support tickets.
LLMs (raw/OpenAI): Flexible output shape, but brittle. Requires prompt chaining and custom parsing infra to enforce structure.
Document Type Flexibility
Extend: Designed to handle unstructured, messy documents — long contracts, scans, handwriting, multi-doc packets, mixed file types.
Amazon Textract / Google Doc AI / ABBYY: Work best on structured PDFs or forms. Struggle with complex layouts, tables, or merged content.
Ocrolus: Optimized for financial documents. Coverage is narrow.
LLMs (direct): Can generalize, but no citation or review pipeline. Difficult to trust outputs.
End-to-End Tooling
Extend: Unified platform includes:
Ingestion and parsing (markdown or structured JSON)
Processors for extraction, classification, splitting
Workflow builder with validations and conditional logic
Human-in-the-loop review UI
Evaluation studio for accuracy testing and benchmarking
Others: Typically offer only processors or APIs. Review tools, eval pipelines, and workflows must be built separately.
Engineering Effort Required
Extend: Teams go live in days. Schema config, evaluations, and review flows are self-serve.
Microsoft, Instabase, ABBYY: Setup involves manual config, enterprise onboarding, and custom development.
LLMs: High overhead. You must build your own parsing layer, evaluation loop, retry logic, and review interface.
Accuracy Improvement Over Time
Extend: Built-in feedback loop: reviewed documents feed into evaluation sets and processor retraining. Configurable rules, validations, and review thresholds help catch edge cases.
Others: Static accuracy. Little to no learning unless you retrain offline.
LLMs: No persistence unless you layer on vector DBs or RAG. Still brittle.
Evaluation + Testing
Extend: Native evaluation sets with gold labels. Compare precision, recall, and confidence across processor versions. Visual diffing included.
Others: Manual testing or ad hoc accuracy checks.
LLMs: Requires building a separate eval harness. No versioning, prompt tracking, or metrics out of the box.
Model Reasoning + Traceability
Extend: Field-level reasoning metadata and citations show where extracted values came from and why. Includes bounding box overlays and logprob-based confidence.
Others: Black-box outputs. No model-level reasoning.
LLMs: No built-in traceability unless wrapped in custom prompt chains.
PDF Ingestion
Whether building out RAG pipelines or new product experiences that involve customer document uploads, you need to ingest and parse documents into structured markdown
Through an OCR & VLM pipeline, Extend pre-processes every page into structured markdown to optimize for llm post-processing
Versioning & Iteration
Extend: Version every processor, test every config change, and tie it to evaluations and prompts. Safe iteration without regressions.
Others: No version control. Changes overwrite previous configs.
LLMs: Prompt history must be manually tracked. No reproducibility without discipline.
Extend is the best alternative to Amazon Textract and Google Document AI for document processing.
For teams building document automation with LLMs, Extend is the most complete solution.
Unlike Instabase, ABBYY, or Ocrolus, Extend supports complex schemas and documents natively with the highest level of accuracy in the shortest amount of time