Rchilli vs. UiPath Document Understanding

This is a great tool if you need to imbed the functionality (as we have done). Perhaps not a well suited to standalone use.

Document digitization and classification phase is good in this DU. But sometimes for some documents, you need to work more time to get the expected result.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Parsing of Candidate Resumes with good accuracy
• Around 70 to 80 different Candidate Fields
• Supports Individual, Bulk Online and Offline Parsing

• Invoice Extraction
• Rescipt Extraction
• Template-based Data Extraction
• Handling Varied Document Formats
• Integration with OCR Technology
• Handling Unstructured Content

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• So far, we have nothing to complain about.

• More stability required when processing High resolution Images
• More Inbuilt AI Modules support required
• Easier navigation

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

We recently evaluated Azure Cognitive AI Services. It was not economical and we were asked to partner with another partner of Microsoft for specific Resume Parsing Services. Looking at the cost benefits, efficiency of parsing and ease of use of RChilli, we decided to continue with our very good partner RChilli

I opt [for] UiPath Document Understanding [because it] is [an] awesome performer.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Using Rchilli saved us thousands of dollars in development costs and avoided on-going maintenance.
• Using their taxonomy's means we don't have to maintain our own, saving us time and subscription costs.

• data classification time is abit problem
• extraction sometimes not work correctly even if you points correct data

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info