×

VoiceOps

Jobs posted: 1






HQ
San Francisco, CA

Website









        VoiceOps


About us

VoiceOps uses AI to improve call center rep performance with world-class coaching.

Our average customer makes tens of thousands of calls per week. In a world without VoiceOps, they have literally no idea what their sales reps are doing on the phone. It's a total (and scary) black box.

By applying ML and a great UI to this problem, call center leadership has all the data they need about customer conversations at their fingertips, and can coach their reps more effectively and efficiently.

The technical problem is interesting, and gets more interesting as we grow. Our core challenge is how to take billions of audio recordings (and messy, unstructured human conversations) and make sense out of that data in a way that is: a) accurate b) cost efficient, and c) highly scalable. The corresponding product problem is how to take well-structured data and make it actionable for the end-user.

Call center recordings are one of the richest/largest untapped datasets in the world (literally, billions of calls stored in AWS buckets that no one is touching right now). We're going to be the best in the world at structuring that data and putting it to use to make businesses work better.

HQ
San Francisco, CA

Website

VoiceOps


Jobs posted: 1





        VoiceOps


About us

VoiceOps uses AI to improve call center rep performance with world-class coaching.

Our average customer makes tens of thousands of calls per week. In a world without VoiceOps, they have literally no idea what their sales reps are doing on the phone. It's a total (and scary) black box.

By applying ML and a great UI to this problem, call center leadership has all the data they need about customer conversations at their fingertips, and can coach their reps more effectively and efficiently.

The technical problem is interesting, and gets more interesting as we grow. Our core challenge is how to take billions of audio recordings (and messy, unstructured human conversations) and make sense out of that data in a way that is: a) accurate b) cost efficient, and c) highly scalable. The corresponding product problem is how to take well-structured data and make it actionable for the end-user.

Call center recordings are one of the richest/largest untapped datasets in the world (literally, billions of calls stored in AWS buckets that no one is touching right now). We're going to be the best in the world at structuring that data and putting it to use to make businesses work better.