Failed Payment Recovery for Subscription Businesses
For subscription companies searching for a failed payment recovery solution to grow revenue, and retain customers.
FlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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Simplify Purchasing For Your Business
Manage what you buy and how you buy it with Order.co, so you have control over your time and money spent.
Simplify every aspect of buying for your business in Order.co. From sourcing products to scaling purchasing across locations to automating your AP and approvals workstreams, Order.co is the platform of choice for growing businesses.
A neural net module written in python. The aim of the project is to provide a large set of neural network types accessed by an API that is easy to use and powerful.
eBarter froms trades from commitments. It measures economic values without the need of a common value standard; distributes fairly the gain produced between participants and gives the preference to trades where participants have the weakest requirements.
Institute of Technology, Blanchardstown Computer Science code by the class of 2007-2011 on course BN104. In this project we are open sourcing all of our project work to the public in the hopes it can be reused, built-upon, and used in education.
SoftCo: Enterprise Invoice and P2P Automation Software
For companies that process over 20,000 invoices per year
SoftCo Accounts Payable Automation processes all PO and non-PO supplier invoices electronically from capture and matching through to invoice approval and query management. SoftCoAP delivers unparalleled touchless automation by embedding AI across matching, coding, routing, and exception handling to minimize the number of supplier invoices requiring manual intervention. The result is 89% processing savings, supported by a context-aware AI Assistant that helps users understand exceptions, answer questions, and take the right action faster.
Provide Reference Implementations of Data Structures in Python(3)
The aim of the project is to provide reference implementations of data structures along with supporting references to publications and other materials.
The only purpose is for fun and education: I like data structures.