
How I Trained ChatGPT to Write PML
Have you tried out the amazing ChatGPT? If you’ve interacted with it or seen screenshots of what it can do, you know there could be endless possibilities.
Have you tried out the amazing ChatGPT? If you’ve interacted with it or seen screenshots of what it can do, you know there could be endless possibilities.
The driving theme of development for Veloce CPQ is working with customers who are on CPQ apps that can’t perform what is needed. We see this often, where customers have been on other CPQ apps where requirements go unmet despite promises to meet the need in the ‘next’ development phase.
Veloce’s composable API based CPQ architecture is an agile platform that enables our customers to implement new CPQ processes and adapt to the constantly changing business requirements quickly.
The Veloce team works hard to develop solutions that simply work and require no coding for the customer’s admin team. We take care of the CPQ development, so our customers can take care of business. However, for Systems Integrators and Developers, I’m providing a glimpse of the tool our team uses behind the scenes.
When evaluating CPQ solutions, you may hear about constraint-based vs. rule-based configuration engines. This post will help make sense of the pros and cons of both. Veloce CPQ is exceptional in that it makes use of both constraint-based versus rule-based technologies. Read on to see why this is a benefit.
When it comes to managing enterprise applications, people generally love point and click administration tools. They’re easy to learn and execute. But when it comes
At Veloce we’ve met with companies who are running top tier Gartner magic quadrant CPQ solutions and who are having big trouble adding new products to their CPQ system. We’re not talking about changing the system or adding new functionality or visualizations. We are talking about basic product updates and additions. We can explain how our system works better.