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Inbenta Uses AI To Create Meaningful Chatbot Conversations

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Manage episode 302378011 series 2965865
Content provided by Hans van Dam. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Hans van Dam or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Online marketing, eCommerce, and call centre managers face some pretty tough challenges these days.

Tasked with transforming emails and calls into web traffic, this task can take a long time. And if a company’s website is lacking, visitors will leave in the middle of a transaction!

This sounds like a no-win situation, but Jordi Torras, CEO and Founder at Inbenta, has a solid solution for senior execs in this predicament: Inbenta.

Jordi and host Hans van Dam explore how Inbenta helps companies automate conversations by chatbots.

Initially, Jordi tackled the big problem of frequently asked questions (FAQs) on websites. Most companies have them, but users aren't fans, mostly because even with a search engine the results are all the answers containing every word in the question, and that can be a pretty long list!

Back in the 2010s, Jordi and his team developed successful technology to match user questions with answers. When the age of conversational AI arose, the FAQ-search engine problem remained the same.

Jordi’s early work placed him and his team in the perfect position to create solutions. Inbenta can match user questions with intent, and there’s no need for any training of company staff. In fact, with customer data Inbenta can go live within 24 hours.

Ibenta understands the meaning of what someone says, and that can be mapped to intent. Inbenta is built on a linguistic model, rather than a statistical one.

Inbenta has the linguistic model that requires no training and it extracts potential intent. It then uses machine learning for disambiguation, based on user behavioral patterns.

Jordi Torras on LinkedIn.

Hans van Dam on LinkedIn.

  continue reading

12 episodes

Artwork
iconShare
 
Manage episode 302378011 series 2965865
Content provided by Hans van Dam. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Hans van Dam or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Online marketing, eCommerce, and call centre managers face some pretty tough challenges these days.

Tasked with transforming emails and calls into web traffic, this task can take a long time. And if a company’s website is lacking, visitors will leave in the middle of a transaction!

This sounds like a no-win situation, but Jordi Torras, CEO and Founder at Inbenta, has a solid solution for senior execs in this predicament: Inbenta.

Jordi and host Hans van Dam explore how Inbenta helps companies automate conversations by chatbots.

Initially, Jordi tackled the big problem of frequently asked questions (FAQs) on websites. Most companies have them, but users aren't fans, mostly because even with a search engine the results are all the answers containing every word in the question, and that can be a pretty long list!

Back in the 2010s, Jordi and his team developed successful technology to match user questions with answers. When the age of conversational AI arose, the FAQ-search engine problem remained the same.

Jordi’s early work placed him and his team in the perfect position to create solutions. Inbenta can match user questions with intent, and there’s no need for any training of company staff. In fact, with customer data Inbenta can go live within 24 hours.

Ibenta understands the meaning of what someone says, and that can be mapped to intent. Inbenta is built on a linguistic model, rather than a statistical one.

Inbenta has the linguistic model that requires no training and it extracts potential intent. It then uses machine learning for disambiguation, based on user behavioral patterns.

Jordi Torras on LinkedIn.

Hans van Dam on LinkedIn.

  continue reading

12 episodes

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