One of the biggest limitations companies face with document processing is traditional solutions. Instead of learning from the know-how of other projects, new and expensive projects are inevitable. Parashift shows that this can be done differently with a unique solution for document extraction. Instead of frustrating limitations, the slogan is global AI training that benefits all users on the platform equally.
Swarm Learning maximizes document learning across all customers and document types without ever sharing the actual data
Parashift’s Swarm Learning technology for intelligent document processing makes this a reality for organizations. Thanks to its fundamentally different approach, no expensive projects are required to get started with powerful document extraction.
TL;DR
Because of unique Swarm Learning technology and global AI training, you don’t need an expensive project to get started with powerful automated document extraction.
Contents
1. Unique Swarm Learning technology
2. Continuous Machine Learning
3. Effortless setup of new document types
1. Unique Swarm Learning technology
Outdated infrastructure, weak solutions and methods with tedious template creation: The reasons for large and expensive document processing projects are numerous. Conventional systems that tie their learning models to document types and allow only client specific learning contribute to these limitations.
This is contrasted by the Parashift IDP platform with its Swarm Learning technology for document extraction solutions. The Swarm Learning approach is fundamentally different than that of traditional solutions.
Overview:
1. Cloud AI OCR solution: Parashift platform runs in the cloud (GDPR compliant), which makes global learning across all customers, interactions and document types possible in the first place.
2. A fundamentally different system: Instead of linking learning models to document types, Parashift uses Swarm Learning to divide learning models into extraction entities (in a software application, this could be a data field, for example). All learning models and learning methods are executed per extraction entity.
3. Learning models decoupled from document types: To ensure that extraction methods can always be related to each other in new ways, extraction entities are separated from document types and decoupled from learning models. This methodology is applied independently of the extraction approach itself.
4. Multiple models for the same field entity: Per extraction entity, learning models and extraction mechanisms can be put in competition with each other. Another model represents the evaluation of the best learning model and extraction method in each case. This allows learning models to be improved regardless of document type.
5. No additional learning effort required: This improvement means that no additional learning effort is required for the same extraction entities. There is no need to set up and train models.
6. Every client benefits from the learning of other clients: Global AI training benefits all users on Parashift IDP platform. The learning models get better, even without processing their own document types. Thus, a “swarm effect” is achieved. This allows smaller and therefore also cheaper projects.
2. Continuous Machine Learning
Parashift’s Swarm Learning is based on an autonomous Machine Learning cluster. Machine Learning algorithms train on billions of data points and across all clients. With the fully automated Machine Learning cluster and continuous learning on the job, an efficient way to automate document processes is realized not only quickly, but also without additional effort for users.
Preconfigured and -trained document types allow immediate start without expensive projects
3. Effortless setup of new document types
Another huge benefit that is only made possible by Swarm Learning is the document type editor on the Parashift platform. Due to the numerous ready-to-use and pre-trained extractors, the simple and time-reduced configuration of new document types is feasible. Individual document types can be created within a very short time by drag & drop.
The effortless setup of new document types underlines the claim that you don’t need expensive projects to get started with Document AI.
