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Business FinanceTop 10 Best Automated Document Processing Software of 2026
Discover the top 10 best automated document processing software solutions to streamline workflows. Explore features, benefits, and compare tools here.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
UiPath Document Understanding
Active learning workflows for improving extraction accuracy during document processing
Built for teams automating data extraction for diverse business documents at scale.
Amazon Textract
Detects key-value fields and table structures with cell-level bounding boxes
Built for aWS teams automating OCR, forms, and tables at scale.
Microsoft Azure AI Document Intelligence
Layout-aware table extraction with confidence scoring for structured fields
Built for enterprises automating document extraction across diverse PDF and scanned inputs.
Comparison Table
This comparison table evaluates automated document processing platforms used for extracting structured data from documents such as invoices, forms, and contracts. It compares UiPath Document Understanding, Amazon Textract, Microsoft Azure AI Document Intelligence, Google Document AI, Kofax Intelligent Automation for Document Processing, and other leading tools across core capabilities like OCR, layout understanding, model customization, and deployment options.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | UiPath Document Understanding Automates document intake and extraction using machine learning for forms, invoices, and contracts with downstream workflow automation. | enterprise automation | 8.7/10 | 9.1/10 | 8.2/10 | 8.6/10 |
| 2 | Amazon Textract Extracts text, tables, and key-value pairs from scanned documents and PDFs with APIs for automated document processing. | API-first | 8.2/10 | 8.6/10 | 7.9/10 | 7.9/10 |
| 3 | Microsoft Azure AI Document Intelligence Uses prebuilt and custom models to extract fields, tables, and forms from documents with document layout analysis. | AI document extraction | 8.1/10 | 8.8/10 | 7.9/10 | 7.3/10 |
| 4 | Google Document AI Processes documents with trained models to extract structured data from invoices, receipts, and forms for business workflows. | cloud extraction | 8.1/10 | 8.8/10 | 7.4/10 | 7.9/10 |
| 5 | Kofax Intelligent Automation for Document Processing Automates document capture and classification with OCR and workflow orchestration for finance document operations. | enterprise capture | 8.0/10 | 8.4/10 | 7.8/10 | 7.8/10 |
| 6 | ABBYY FlexiCapture Automates data capture from high-volume documents using OCR and configurable capture workflows. | data capture | 7.3/10 | 7.8/10 | 6.7/10 | 7.1/10 |
| 7 | Rossum Automates document data extraction with an AI model training workflow for business documents like invoices and forms. | workflow extraction | 8.2/10 | 8.6/10 | 7.9/10 | 7.8/10 |
| 8 | Docsumo Extracts invoice and receipt data with OCR and machine learning and exports structured results for finance automation. | invoice extraction | 7.6/10 | 8.0/10 | 7.6/10 | 6.9/10 |
| 9 | Sardine.ai Extracts structured fields from business documents and supports routing and integration for document-heavy finance workflows. | document AI | 8.2/10 | 8.4/10 | 7.8/10 | 8.2/10 |
| 10 | OnBase Intelligent Capture by Hyland Captures and classifies documents with OCR and automation features for records and finance processing in Hyland systems. | capture platform | 7.3/10 | 7.8/10 | 6.9/10 | 6.9/10 |
Automates document intake and extraction using machine learning for forms, invoices, and contracts with downstream workflow automation.
Extracts text, tables, and key-value pairs from scanned documents and PDFs with APIs for automated document processing.
Uses prebuilt and custom models to extract fields, tables, and forms from documents with document layout analysis.
Processes documents with trained models to extract structured data from invoices, receipts, and forms for business workflows.
Automates document capture and classification with OCR and workflow orchestration for finance document operations.
Automates data capture from high-volume documents using OCR and configurable capture workflows.
Automates document data extraction with an AI model training workflow for business documents like invoices and forms.
Extracts invoice and receipt data with OCR and machine learning and exports structured results for finance automation.
Extracts structured fields from business documents and supports routing and integration for document-heavy finance workflows.
Captures and classifies documents with OCR and automation features for records and finance processing in Hyland systems.
UiPath Document Understanding
enterprise automationAutomates document intake and extraction using machine learning for forms, invoices, and contracts with downstream workflow automation.
Active learning workflows for improving extraction accuracy during document processing
UiPath Document Understanding stands out by turning unstructured documents into structured fields using trainable extraction and validation loops. It supports document classification, entity extraction, and post-processing workflows that route results into downstream automations. The solution integrates with UiPath automation tooling so extracted data can trigger actions and validations within end-to-end processes.
Pros
- High-accuracy extraction with labeling and continuous model improvement
- Strong document classification and entity capture for heterogeneous layouts
- Integrates extracted fields into UiPath automation workflows
Cons
- Training and tuning require document labeling effort
- Complex documents may need iterative validation rules
- Workflow design can become heavy for small document volumes
Best For
Teams automating data extraction for diverse business documents at scale
Amazon Textract
API-firstExtracts text, tables, and key-value pairs from scanned documents and PDFs with APIs for automated document processing.
Detects key-value fields and table structures with cell-level bounding boxes
Amazon Textract stands out for extracting text, forms fields, and tables from scanned documents using ML models hosted as AWS services. It can detect handwriting and support document analysis workflows through APIs like DetectDocumentText, AnalyzeDocument, and asynchronous job processing for larger files. Key outputs include structured key-value pairs for form fields and cell-level table data that downstream systems can consume directly. Integration into AWS-centric document pipelines is a strong fit through IAM controls and event-driven orchestration.
Pros
- Strong form and table extraction outputs with structured fields
- Reliable OCR for mixed layouts and multi-page documents
- Asynchronous jobs handle large documents without manual chunking
Cons
- Setup and IAM configuration add overhead for non-AWS teams
- Layout complexity can still require custom post-processing
- Model accuracy depends on scan quality and document consistency
Best For
AWS teams automating OCR, forms, and tables at scale
Microsoft Azure AI Document Intelligence
AI document extractionUses prebuilt and custom models to extract fields, tables, and forms from documents with document layout analysis.
Layout-aware table extraction with confidence scoring for structured fields
Microsoft Azure AI Document Intelligence stands out for deep extraction support across scanned documents and PDFs using layout-aware models. It provides OCR, key-value extraction, form recognition, table extraction, and receipt and invoice oriented pipelines. The service integrates with Azure AI Search, Azure Functions, and custom apps through document analysis APIs and customizable models. It also supports prebuilt models for common document types alongside workflows for training and labeling custom document layouts.
Pros
- Strong OCR plus layout analysis for forms, receipts, and tables
- Prebuilt models for common document types and fields
- Custom model training for organization-specific document layouts
- Good integration path into Azure workflows and downstream systems
Cons
- Achieving consistent results requires careful document preprocessing
- Custom training and evaluation adds operational overhead
- Complex document variants can increase error rates without tuning
Best For
Enterprises automating document extraction across diverse PDF and scanned inputs
Google Document AI
cloud extractionProcesses documents with trained models to extract structured data from invoices, receipts, and forms for business workflows.
Document AI custom processor training for domain-specific extraction rules
Google Document AI stands out for its tight integration with Google Cloud data tooling and document understanding services. It automates extraction of structured fields from forms, receipts, invoices, and other document types using prebuilt and custom models. It also supports human review workflows and exports results to storage and downstream systems for operational processing at scale. The main differentiator is how well it fits enterprise document pipelines built around Google Cloud storage, orchestration, and data governance.
Pros
- Strong extraction for forms and key document types with prebuilt processors
- Custom model training supports domain-specific fields and document layouts
- Integrates smoothly with Google Cloud storage, data processing, and security controls
Cons
- Custom workflows require building and tuning Google Cloud pipelines
- Performance depends on input quality such as scans, skew, and template consistency
- Operational setup complexity rises when scaling document types and routing logic
Best For
Enterprises automating structured extraction from documents using Google Cloud workflows
Kofax Intelligent Automation for Document Processing
enterprise captureAutomates document capture and classification with OCR and workflow orchestration for finance document operations.
Kofax machine-learning extraction and classification to populate fields and route documents
Kofax Intelligent Automation for Document Processing stands out with strong document ingestion and automation for high-volume processing. It combines OCR, extraction, classification, and rule-based workflows to route documents to back-office systems. The solution is built for traceable, operationally controlled processing rather than lightweight document search alone.
Pros
- End-to-end capture to workflow routing for structured document processing
- Robust OCR and data extraction designed for high-volume operations
- Workflow orchestration supports consistent handling and auditability
Cons
- Modeling workflows takes more configuration effort than simpler OCR tools
- Iterative tuning is often needed to maintain accuracy across document variations
- Integration work can be heavy for organizations with complex target systems
Best For
Enterprises needing controlled document automation with OCR, extraction, and routing
ABBYY FlexiCapture
data captureAutomates data capture from high-volume documents using OCR and configurable capture workflows.
Configurable FlexiLayout zones and recognition workflows for precise field extraction
ABBYY FlexiCapture stands out for industrial-strength document capture and classification that supports high-volume workflows beyond simple OCR. It combines configurable recognition templates with rules-based extraction to turn scanned documents and PDFs into structured data. Human review and quality controls help manage confidence-driven field verification at scale.
Pros
- Flexible capture configuration for complex, template-driven document extraction
- Strong quality controls using confidence scoring and review workflows
- Supports batch processing for high document volumes and repeatable results
Cons
- Template setup and tuning take time for varied document formats
- Advanced workflows require administrator expertise and careful monitoring
- Less suited for lightweight, ad-hoc extraction without process design
Best For
Organizations automating data capture from many document types with review controls
Rossum
workflow extractionAutomates document data extraction with an AI model training workflow for business documents like invoices and forms.
Human-in-the-loop review combined with active learning for continual model improvement
Rossum stands out for document understanding driven by an active learning workflow that reduces extraction errors over time. It supports automated data extraction from forms and invoices using configurable templates and AI models, then routes results to downstream systems. The platform emphasizes auditability with confidence scores and review steps for human corrections. It is built for teams that want scalable document processing without writing extraction logic for every new document variation.
Pros
- Active learning loop improves extraction accuracy after reviewer corrections
- Field-level confidence scores help prioritize human verification
- Template-driven extraction covers invoices, forms, and semi-structured documents
- Exportable outputs integrate cleanly with downstream workflows
Cons
- Complex document families require more configuration and ongoing review
- Model tuning can feel opaque for edge-case layouts
- Higher automation depends on reliable input quality and scans
Best For
Operations teams automating invoice and form extraction with human-in-the-loop QA
Docsumo
invoice extractionExtracts invoice and receipt data with OCR and machine learning and exports structured results for finance automation.
Human-in-the-loop correction workflow using confidence scoring for extracted fields
Docsumo stands out for converting messy documents into structured data through a mix of AI extraction and configurable field templates. It supports invoice, bank statement, and other common document types with extraction rules, validation, and export to business systems. The platform emphasizes reviewing extracted results in a human-in-the-loop workflow using confidence scoring and corrections. It also provides integrations for moving output into tools like spreadsheets, CRM, and accounting workflows.
Pros
- Template-based extraction improves accuracy across repeat document formats
- Human-in-the-loop review helps correct low-confidence fields quickly
- Exports structured data to downstream tools for faster processing
Cons
- Document-type coverage can require setup work for niche formats
- Complex extraction logic may feel less flexible than developer-built pipelines
- Automation depends heavily on consistent input quality
Best For
Operations teams automating invoice and statement data capture without heavy engineering
Sardine.ai
document AIExtracts structured fields from business documents and supports routing and integration for document-heavy finance workflows.
Human review checkpoints tightly integrated into document extraction workflows
Sardine.ai focuses on automating document extraction and classification through a workflow-first approach. It supports ingestion from common document types and routes outputs into structured fields for downstream use. Human-in-the-loop review features help correct edge cases where documents vary in layout or quality.
Pros
- Structured field extraction with configurable outputs for downstream workflows
- Human review loop supports higher accuracy on messy real-world documents
- Workflow routing reduces manual triage of incoming document batches
- Document classification helps separate handling paths for different document types
Cons
- Layout variations can require additional tuning for consistent extraction
- Complex multi-step workflows take longer to configure than simple extraction
- Less suited for highly bespoke extraction rules without iterative refinement
Best For
Teams automating extraction and validation for mixed-format document processing
OnBase Intelligent Capture by Hyland
capture platformCaptures and classifies documents with OCR and automation features for records and finance processing in Hyland systems.
Intelligent Capture auto-classification and field extraction to drive automated indexing
OnBase Intelligent Capture by Hyland stands out for combining capture, classification, and routing into a unified intake experience for enterprise document workflows. It supports automated ingestion of paper and electronic documents with configurable recognition and extraction for common business types. It also integrates with OnBase workflow, indexing, and records management capabilities to move captured content into downstream business processes. The solution targets environments that already need governed content management with auditability and scalable ingestion pipelines.
Pros
- Strong document classification and data extraction for structured capture
- Tight integration into OnBase workflow and content management
- Supports enterprise governance with indexing and traceable processing
Cons
- Implementation effort is higher than simpler OCR and capture tools
- Configuration and workflow setup can require specialist administration
- Best results depend on document quality and well-defined capture rules
Best For
Enterprises automating governed intake workflows with OnBase-centric processes
Conclusion
After evaluating 10 business finance, UiPath Document Understanding stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Automated Document Processing Software
This buyer’s guide covers how to evaluate Automated Document Processing Software by mapping document capture, extraction, and routing capabilities to real workflow needs. It references UiPath Document Understanding, Amazon Textract, Microsoft Azure AI Document Intelligence, Google Document AI, Kofax Intelligent Automation for Document Processing, ABBYY FlexiCapture, Rossum, Docsumo, Sardine.ai, and OnBase Intelligent Capture by Hyland.
What Is Automated Document Processing Software?
Automated Document Processing Software captures scanned documents and PDFs, extracts structured fields and tables, and routes results into downstream workflows with validations and reviews. These tools reduce manual keying by turning unstructured layouts into structured outputs such as key-value fields and cell-level table data. Tools like Amazon Textract focus on extraction through APIs for text, forms, and tables, while UiPath Document Understanding connects extracted fields to downstream workflow automation inside an end-to-end process.
Key Features to Look For
Document-processing accuracy and operational fit depend on the extraction engine, the model or template training approach, and the way the platform handles human review, confidence, and routing.
Active learning loops for continually improving extraction accuracy
UiPath Document Understanding uses active learning workflows that improve extraction accuracy as labels and reviewer feedback are added. Rossum also pairs human-in-the-loop review with active learning to continually improve model performance on recurring invoice and form variation.
Layout-aware table and field extraction with confidence scoring
Microsoft Azure AI Document Intelligence delivers layout-aware table extraction with confidence scoring for structured fields, which helps teams prioritize verification on uncertain rows and cells. Amazon Textract provides cell-level bounding boxes for table structures and supports structured key-value outputs that downstream systems can consume directly.
Custom training and domain-specific extraction models
Google Document AI supports custom processor training for domain-specific extraction rules, which matters when invoices or forms include organization-specific field formats. Azure AI Document Intelligence similarly supports custom model training for organization-specific document layouts when prebuilt models are not enough.
Human-in-the-loop review driven by confidence scores
Docsumo offers a human-in-the-loop correction workflow using confidence scoring for extracted fields, which speeds up fixing low-confidence outputs. ABBYY FlexiCapture adds quality controls that use confidence scoring and review workflows to manage verification at high volume.
Configurable capture workflows for batch processing at volume
Kofax Intelligent Automation for Document Processing combines OCR, extraction, classification, and rule-based workflow orchestration built for high-volume processing with auditability and traceability. ABBYY FlexiCapture supports batch processing with configurable capture workflows for repeatable outcomes across many document inputs.
Workflow-first routing and downstream integration
Sardine.ai emphasizes workflow-first routing where document classification and human review checkpoints feed into structured field outputs for downstream use. OnBase Intelligent Capture by Hyland integrates intelligent capture with OnBase workflow, indexing, and records management so extracted fields drive governed intake workflows.
How to Choose the Right Automated Document Processing Software
The right choice depends on document variety, the tolerance for labeling or template configuration effort, and how extraction results must be routed into existing systems.
Match the extraction approach to document variability
If document layouts vary widely across forms, invoices, and contracts, UiPath Document Understanding fits because it uses trainable extraction with labeling and validation loops for heterogeneous layouts. If the goal is fast, API-driven extraction for text, tables, and key-value pairs from scanned documents and PDFs, Amazon Textract fits well because it returns structured key-value fields and cell-level table data.
Choose the model customization path that fits the team’s workload
Teams that can manage labeling and continuous improvement should evaluate UiPath Document Understanding and Rossum because both emphasize active learning workflows. Enterprises that want to start with prebuilt models for common document types and then extend with custom training should evaluate Microsoft Azure AI Document Intelligence and Google Document AI.
Design for verification using confidence and human review checkpoints
If workflows require humans to correct extracted fields to protect downstream accuracy, Docsumo and Rossum provide human-in-the-loop review tied to confidence scoring. If verification must be operationally controlled at scale with confidence-driven field verification, ABBYY FlexiCapture supports review workflows that help manage repeatable batch processing.
Plan routing and integration based on where extracted data must land
For teams that already run automation flows inside UiPath, UiPath Document Understanding can integrate extracted fields into UiPath automation tooling so extracted data triggers actions and validations. For organizations that need enterprise content management and governed indexing, OnBase Intelligent Capture by Hyland integrates capture, classification, extraction, and indexing into OnBase workflow and records management.
Validate setup complexity against expected document throughput
If the implementation must scale to high-volume operations with auditability, Kofax Intelligent Automation for Document Processing offers OCR plus extraction plus classification plus rule-based orchestration for controlled handling and routing. If throughput is large but rules and templates must be tuned for complex formats, ABBYY FlexiCapture and Google Document AI require additional configuration work to sustain consistent results across layout variations.
Who Needs Automated Document Processing Software?
Automated Document Processing Software benefits teams that need structured data extraction from scanned documents and PDFs and then want to reduce manual triage, keying, and downstream rework.
Teams extracting diverse business documents at scale
UiPath Document Understanding is designed for teams automating data extraction for diverse business documents at scale because it supports trainable extraction with validation loops. Sardine.ai is also a strong fit for mixed-format document processing because it combines document classification with human review checkpoints and workflow routing.
AWS teams automating OCR, forms, and table extraction via APIs
Amazon Textract fits AWS-centric pipelines because it extracts text, tables, and key-value pairs from scanned documents and PDFs using DetectDocumentText and AnalyzeDocument style workflows. It is best aligned when structured outputs must include cell-level table data and form key-value field structures.
Enterprises standardizing extraction across scanned documents and PDFs in Azure or Google Cloud
Microsoft Azure AI Document Intelligence is best for enterprises automating document extraction across diverse PDF and scanned inputs using layout-aware models and prebuilt pipelines. Google Document AI fits enterprises already using Google Cloud storage and orchestration because it integrates structured extraction with Google Cloud governance and supports custom processor training.
Operations teams focused on invoices and forms with human-in-the-loop QA
Rossum matches teams automating invoice and form extraction with human-in-the-loop QA because it combines active learning with reviewer corrections. Docsumo is also well aligned for invoice and statement capture without heavy engineering because it uses template-based extraction and a human correction workflow driven by confidence scores.
Common Mistakes to Avoid
Common failure modes appear when teams underestimate labeling or template configuration effort, choose extraction paths that do not match document variability, or neglect verification and routing design.
Selecting an extraction tool without planning for labeling or tuning work
UiPath Document Understanding requires document labeling effort and iterative validation rules for complex documents, which can slow early deployments. ABBYY FlexiCapture also needs time to set up and tune templates for varied document formats, which can impact timelines when document families are not stable.
Assuming every document can be extracted perfectly without review and confidence handling
Complex document variants can increase error rates in Microsoft Azure AI Document Intelligence without tuning, so confidence scoring and review workflows must be built into the process. Docsumo, Rossum, and Sardine.ai all rely on human review checkpoints tied to confidence to correct low-confidence fields where accuracy must be protected.
Overlooking routing and integration requirements for downstream systems
Kofax Intelligent Automation for Document Processing supports end-to-end capture to workflow routing, but integration work can become heavy when target systems are complex. OnBase Intelligent Capture by Hyland is tightly coupled to OnBase workflow, indexing, and records management, so it should be selected when OnBase-centric intake governance is already required.
Underestimating setup overhead for cloud permissions and pipeline integration
Amazon Textract adds setup and IAM configuration overhead for non-AWS teams, which can delay production readiness. Google Document AI and Microsoft Azure AI Document Intelligence can increase operational setup complexity when scaling document types and routing logic, so pipeline design should be addressed early.
How We Selected and Ranked These Tools
we evaluated each tool on three sub-dimensions. Features carry weight 0.40 because document extraction quality, workflow routing, and integration behavior determine day-to-day results. Ease of use carries weight 0.30 because labeling effort, workflow configuration complexity, and operational overhead affect time to deploy. Value carries weight 0.30 because teams need workable extraction workflows that match their document volume and process control requirements. The overall rating is the weighted average of those three values where overall equals 0.40 × features + 0.30 × ease of use + 0.30 × value. UiPath Document Understanding stands out because it scores strongly on features with trainable extraction and active learning workflows that connect extracted fields into UiPath automation tooling, which supports end-to-end validation rather than extraction-only pilots.
Frequently Asked Questions About Automated Document Processing Software
Which automated document processing tool works best for form fields and tables extracted from scans?
Amazon Textract is designed to extract text, forms fields, and tables from scanned documents using hosted ML models and cell-level table data. Microsoft Azure AI Document Intelligence also supports table extraction and key-value extraction with layout-aware models.
How do UiPath Document Understanding and Rossum reduce extraction errors over time?
UiPath Document Understanding improves extraction accuracy with trainable extraction workflows that include validation loops. Rossum uses active learning with human-in-the-loop corrections and confidence scoring to drive continual improvement.
What solution is strongest for document automation pipelines already built around a cloud data ecosystem?
Google Document AI fits teams with Google Cloud storage and orchestration because it integrates tightly with Google Cloud workflows and governance patterns. Amazon Textract similarly fits AWS-centric pipelines through API access and IAM-controlled event-driven orchestration.
Which tools support routing extracted fields into end-to-end business automation workflows?
UiPath Document Understanding integrates extracted entities into downstream automations within the UiPath tooling ecosystem. OnBase Intelligent Capture by Hyland routes captured content into OnBase workflow, indexing, and records management so extracted fields drive intake processes.
Which platform is best when human review and audit trails are required for compliance-grade accuracy?
Rossum emphasizes auditability with confidence scores and review steps for human corrections. Kofax Intelligent Automation for Document Processing provides traceable processing with controlled ingestion, classification, OCR, extraction, and routing.
How do teams handle new document variants without rebuilding extraction logic from scratch?
Rossum and Docsumo both rely on templates and active learning style feedback so field extraction improves as variations appear. ABBYY FlexiCapture supports configurable recognition templates plus rule-based extraction to handle many document types with consistent field mapping.
Which option is optimized for receipt and invoice processing with confidence scoring?
Microsoft Azure AI Document Intelligence includes receipt and invoice oriented pipelines with confidence scoring and structured field output. Docsumo also supports invoice-focused extraction with human-in-the-loop review and confidence-based corrections.
What differentiates Kofax Intelligent Automation from lightweight OCR tools?
Kofax Intelligent Automation for Document Processing combines ingestion, OCR, classification, extraction, and rule-based routing designed for high-volume back-office processing. This focus emphasizes operational control and traceability rather than document search alone.
Which tools include built-in mechanisms to validate extracted fields during processing?
UiPath Document Understanding uses validation loops that check extracted results and drive post-processing workflows. Sardine.ai uses workflow-first extraction with human-in-the-loop review checkpoints to correct edge cases that automated parsing misreads.
What should teams consider when integrating document processing output into their systems?
Amazon Textract outputs structured key-value pairs and table cell data that can feed downstream systems through asynchronous APIs. Google Document AI and Microsoft Azure AI Document Intelligence provide analysis APIs that integrate with storage, search, and workflow components such as Azure Functions and Azure AI Search.
Tools reviewed
amazon.comReferenced in the comparison table and product reviews above.
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