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Document Swarm Learning®: decentralized, self-organized document extraction agents

To achieve full autonomy in document extraction, we scale AI training efforts in an unprecedented way.

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Independent AI agents specialized in a particular task achieve powerful performance.

Parallelized learning using different approaches and methodologies lead to superior extraction quality.

Parashift’s document AI trains on field-level entities and can scale training across documents.

Swarm predictions from a network of AIs drastically improve the outcome of prediction problems.

A first-of-its-kind approach to solve document extraction

Contrary to conventional systems, Parashift Platform does not link learning models to specific document types. Instead, the platform splits models into extraction entities. Extraction entities represent a structured, normalized value to be found. In a software application, this can be a data field, for example.

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A first-of-its-kind approach to solve document extraction

Contrary to conventional systems, Parashift Platform does not link learning models to specific document types. Instead, the platform splits models into extraction entities. Extraction entities represent a structured, normalized value to be found. In a software application, this can be a data field, for example.

Get a demo

Learning models decoupled from document types

Parashift’s breakthrough methodology is applied independently of the extraction approach itself. The extraction entities are separated from the document types and decoupled from the learning models. This allows the extraction methods to be related to each other in a new way.

Multiple models for the same field entity

Per extraction entity, learning models and extraction mechanisms can be placed in competition with each other. Another model maps the evaluation of the best learning model and extraction method in each case. In this way, it’s possible to improve learning models independently of document type. The improvement leads to the fact that for the same extraction entities no additional learning effort has to be driven for the respective tenant. This procedure also enables the simple and time-reduced configuration of new document types with “pre-learned” extraction entities.

Multiple models for the same field entity

Per extraction entity, learning models and extraction mechanisms can be placed in competition with each other. Another model maps the evaluation of the best learning model and extraction method in each case. In this way, it’s possible to improve learning models independently of document type. The improvement leads to the fact that for the same extraction entities no additional learning effort has to be driven for the respective tenant. This procedure also enables the simple and time-reduced configuration of new document types with “pre-learned” extraction entities.

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Ready to fundamentally change the way you handle documents?

Integrate Parashift to dramatically cut costs and increase efficiency

Parashift helps you automate your structured and unstructured document processing, easily and seamlessly. We integrate with your existing software to provide a value-added service to your business processes.

Ready-to-use document types make it easy to get started. Or, quickly create new document types with our user-friendly document type editor.

Stop wasting valuable resources on manual data entry. Let Parashift power your automated document processing, with fast and reliable results.

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