Why traditional Intelligent Document Processing (IDP) solutions are complex and costly for many enterprises? More and more, it makes sense for enterprises to look for solutions that distance themselves from specialized niche solutions. A centralized solution for all document extraction requirements from all business areas should be the goal of enterprises.
Thanks to powerful AI technologies, Parashift’s IDP solution brings enterprises exactly that: a reduction in complexity and operational costs to an absolute minimum. This enables them to save up to 95% of use case costs compared to traditional IDP solutions.
Cost impact is the primary driver for Intelligent Document Processing adoption, as companies seek to derive tangible benefits from the technology, followed closely by improved operational efficiency and productivity.
Everest Group
Overview: These are why centralized IDP solutions are less complex and costly
1. One platform for all documents
2. Global data network
3. Up to 4x less setup effort
1. One platform for all documents
A single platform for everything related to document extraction not only makes things simpler for enterprises. Another essential aspect of a single AI-based platform is that it also minimizes operating costs.
On one hand, this results from the fact that all use cases will run via just one Intelligent Document Processing (IDP) platform and via one interface. On the other hand, this will enable enterprises to gradually replace specialized niche solutions and thus save high maintenance costs in the future.
Some of the numerous use cases that are covered out-of-the-box (no configuration, no training) with Parashift IDP platform include the following:
- Banking: Mortgage lending
- Real Estate: Dossier for rental of apartments
- Insurance: Claims processing
- Accounting: Invoice processing
- Government: Tax return processing
- Logistics: Bill of lading processing
Parashift Intelligent Document Processing platform now processes any type of document – that wasn’t always the case.
2. Global data network
Parashift IDP platform is based on propriety Document Swarm Learning. Here, the machine learning algorithms train on billions of data points and globally across all customers, thus sharing the learning without sharing the actual data. This creates a global data network, realizing rapid time-to-solution.
Proprietary and innovative Document Swarm Learning stands as the foundation for a breakthrough methodology and powerful and universal Intelligent Document Processing. In a market of niche solutions, Parashift is therefore on its way to becoming the most powerful universal Intelligent Document Processing provider.
“Parashift’s technology enables companies to reduce their document processing costs by more than 80% while easily and quickly solving new, previously “impossible” use cases. All without replacing existing business applications.” Fabian Seimer, Head Information Management, Inacta AG
3. Up to 4x less setup effort to reduce time and costs
Obviously, a new document extraction solution must have specific factors that can be measured. Cost savings from up to 4x less setup effort compared to traditional solutions, for example.
The Parashift IDP solution combines everything into a single solution without missing out on anything:
- Ease of use: The IDP solution is built on the no-code principle, which ensures ease of use.
- Maximum learning synergies: Document Swarm Learning maximises learning synergies.
- Minimal time-to-solution: The global data network enables minimal time-to-solution.
- Maximum flexibility and agility: The IDP platform is a cloud-first service, which means maximum flexibility and agility for businesses.
- Low operational costs: Enterprises break free from high licensing and deployment costs by paying only based on document volume.Traditional Intelligent Document Processing (IDP) solutions are associated with high costs and complexity. A universal IDP solution changes that.
Now that you know why traditional Intelligent Document Processing (IDP) solutions are complex and costly, it is time to test this ⬇️
