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What's the difference between super.AI and Amazon Intelligent Document Processing (IDP)? Let's take a look at how the features of both platforms compare.
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Modern SaaS microservices
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SaaS microservices architecture
Leverages 5 different modern AI OCRs
Single, proprietary OCR
Latest AI/ML techniques
Ability to classify documents into different types
Multiple OCRs paired with multi-level AI delivers industry-leading results
Requires high volume of documents to train models to reasonable accuracy
Includes sophisticated queues, routing, custom instructions, gamification, 150+ quality assessment mechanisms, and support for any data type
Humain-in-the-loop limited to model setup/training phase
Deploy on-demand, curated human workers in your region for training, validation, and post-processing
Fuzzy matching against databases and ability to add any business logic in Python
Configurable validation rules, including referencing external data
Users define quality, cost, and speed thresholds then super.AI guarantees the results
Multiple OCRs paired with multi-level AI delivers industry-leading results
Single, proprietary AI model primarily designed for processing documents in finance, accounting, and procurement
Multiple OCRs paired with multi-level AI delivers industry-leading results
Limited to uniform, table-based documents, with content in Latin characters
Multiple OCRs paired with multi-level AI delivers industry-leading results
Unable to process complex unstructured documents such as contracts; No current capabilities for redacting sensitive data
Leverages the best AI/ML model for each use case (e.g., KYC, redaction, damage detection, image classification, etc.)
Single, proprietary AI model intake of JPEG and PNG format for documents (i.e. invoices) only
Leverages the best AI/ML model for each use case (e.g., drone footage processing, license plate extraction, damage detection, redaction, etc.)
Leverages the best AI/ML model for each use case (e.g., transcription, speaker identification, etc.)
Leverages the best AI/ML model for each use case (e.g., classification, sentiment analysis, data extraction, etc.)
NLP for processing free-form text; Additional communication features for email automation
