Main features
no code solution
specialized b2c
agent-based support processes
prices
gupshup charges a standard rate of 0.0001 usd per spain phone numbers message. But beyond this price, potential customers should contact your sales team for pricing.
However, the company offers a free trial so you can try out its platform at no cost.
Deploy your chatbot next month
Building the best chatbots and AI agents is what we do best.
The future of the industry is AI, and a well-integrated and personalized chatbot is an AI solution that is easy to scale across your business processes.
Conversations based on artificial intelligence are increasingly popular. And rightly so. The best chatbot is one that saves you time and money while improving the end-user experience.
Whether you want to improve customer interaction or your employee experience, we're here to help you create the best chatbot possible. The possibilities are endless with botpress.
Start building today. It's free.
Why we got rid of intent classifiers
botpress does not use intent classifiers. It's on purpose. Here's why.
Jean-bernard perron
intention classifiers" on black and blue background.
One of the most common questions we get asked by potential users and customers is, “where are your intent classifiers?”
We don't have. And yes, it's on purpose.
Botpress uses llms to identify user intent. because? It is much better for both the creators and users of an AI agent.
We strongly believe in this position, so I would like to take a few minutes to explain our lack of intent classifiers.
Tldr: It's easier to build, more accurate, and easier to maintain.
The old days (before dellm)
(If you are familiar with what intent classifiers are and what they do, feel free to skip this section).
An intent classifier is a tool that classifies user input into predefined intents based on training data.
Developers have to curate and tag countless examples for each possible intent, hoping that the system can match user input to these examples.
For example, with an e-commerce chatbot, developers could define an intent like “trackorder.” Your example sentences might include: “where is my package?” "track my order" and "can you check the delivery status for me?".
Essentially, they are training the AI agent to recognize the user's intent by giving it examples. And yes, they have to enter them all by hand.
Fortunately, the need to perform this manual assignment of possible statements to an intention has practically disappeared as llms has advanced.
But many conversational AI platforms continue to use them. because? We'll talk about it later.
4 drawbacks of intent classifiers
It's not just that it's a longer process: intent classifiers are terrible for many reasons. Here are some:
1. Data dependency
Intent classifiers need a lot of data. They need a huge, representative data set of user examples for each intent to work accurately. Without them, they have difficulty classifying inputs correctly.
