KlearStack AI V/s Template-based Data extraction

(Last Updated On: January 27, 2022)

KlearStack AI V/s Template-based Data extraction

Let us see what makes KlearStack the best software for invoice processing automation. Here we compare the top invoice automation solutions in the industry that require users to draw templates, i.e. the boxes around invoice details in order to get trained. Also read how KlearStack is purely AI based algorithm that overcomes these limitations.

Existing Template-based solutions in Industry	KlearStack
User needs to define templates for each different layout of invoices and this takes time. E.g. if there are 1000 variation of invoices, you need to define 1000 templates.	No templates needed. Extracts data using Deep Learning models.
It takes time to define templates. E.g. For 1000 invoice layouts, it will take approx. 500-700 person days to define all the templates.	User saves the template definition time and effort, since no templates need to be defined.
Slight change in invoice layout needs to retrain template, which is not scalable. E.g. Supplier name position changes from right to left part of invoice.	Machine Learning models handle the data extraction even if the invoice layout changes.
Need trained resources for defining or modify templates.	You don’t need specially trained template developers
Up-front license required per template developer	No license. Default business model is SaaS. Only subscription fees need to be paid (based on usage)
Setup process is tedious. For ex: Flexicapture needs to be installed along-with the pre requisites on every template developer’s PC	SaaS delivery model. Hence nothing needs to be installed locally.However, On-Premise option also available, if required.
Line items with multiple span will not get properly extracted.	Intelligent extraction of line items, irrespective of the span.
Supports multiple languages, since this relies on templates.	KlearStack supports English currently. ML models can be trained for additional languages. 2+ person-weeks of effort for each language. Multiple languages can be trained within short span of few weeks.