dariaevdo

VoiceOps | San Francisco, CA | Full-Time, Onsite | https://voiceops.com

VoiceOps is the #1 AI platform for analyzing enterprise voice.

The richest data source about customers — their conversations — is the least accessible. Our mission is to provide easy access to customer conversations and make voice data queryable. Our product analyzes the content of sales and support conversations and generates insights on phrases that maximize successful outcomes.

To learn more about who we are, our engineering culture, and whether this is the right place for you, read our Key Values profile: https://www.keyvalues.com/voiceops

Here are our open roles:

- Product Engineer: https://angel.co/voiceops/jobs/205241-product-engineer

- Product Designer + Front End Dev : https://angel.co/voiceops/jobs/215809-part-time-product-desi...

- Product Engineering Lead: https://angel.co/voiceops/jobs/227575-product-engineering-le...

- Machine Learning Engineer Practitioner: https://angel.co/voiceops/jobs/350140-machine-learning-engin...

- Front End Engineer: https://angel.co/voiceops/jobs/350129-front-end-engineer

Tech Stack: Rails for user facing side React Python for backend integrations/data science

VoiceOps | San Francisco, CA | Full-Time, Onsite | https://voiceops.com

VoiceOps is the #1 AI platform for analyzing enterprise voice.

The richest data source about customers — their conversations — is the least accessible. Our mission is to provide easy access to customer conversations and make voice data queryable. Our product analyzes the content of sales and support conversations and generates insights on phrases that maximize successful outcomes.

To learn more about who we are, our engineering culture, and whether this is the right place for you, read our Key Values profile: https://www.keyvalues.com/voiceops

Here are our open roles:

- Product Engineer: https://angel.co/voiceops/jobs/205241-product-engineer

- Product Engineering Lead: https://angel.co/voiceops/jobs/227575-product-engineering-le...

Tech Stack: Rails for user facing side React Python for backend integrations/data science

VoiceOps | San Francisco, CA | Full-Time, Onsite | https://voiceops.com

For sales managers that want to understand what their top-performing reps are doing that their average performing teammates are not. VoiceOps is an artificial intelligence tool that analyzes every conversation on your team. Like a sales coach, VoiceOps uses your data to give you replicable and trainable techniques that you can implement throughout your sales force.

To learn more about who we are, our engineering culture, and whether this is the right place for you, read our Key Values profile: https://www.keyvalues.com/voiceops

Here are our open roles:

- Backend Engineer: https://angel.co/voiceops/jobs/260438-backend-engineer

- Product Engineer: https://angel.co/voiceops/jobs/205241-product-engineer

- Product Engineering Lead: https://angel.co/voiceops/jobs/227575-product-engineering-le...

Tech Stack: Rails for user facing side React Python for backend integrations/data science

VoiceOps | Multiple jobs (Backend, Full-Stack) | San Francisco, CA | ONSITE, INTERNS, VISA, Full-time | https://voiceops.com/careers.html

VoiceOps is an analysis platform for enterprise voice. We plug into conferencing and call recording solutions and parse out the skills that people are using on the phone to make recommendations about how they can improve. We provide the quickest and most reliable way to judge what content and sales tactics drive the most successful results on the phone, without having to listen to a single phone call. Using the call data companies already have, we automatically identify winning behaviors and help spread those best practices to the entire team. We’re working with some of the largest sales and support teams in the US and have raised funding from Accel, Founders Fund, Lowercase, YC, and others.

Our long term vision is to own the space of business conversations by expanding into other verticals where conversations are part of the every day workflow, such as fundraising, recruiting, political campaigning, healthcare, banking, etc.

To apply choose a position on this list https://voiceops.com/careers.html and then submit your information via AngelList or directly to jobs@voiceops.com.

if I guessed correctly who you are just reached out to you :) If not, please shoot me a note daria@voiceops.com or lmk what the best way to get in touch would be.

Yes absolutely! Please shoot me a note at daria@voiceops.com and I'll send over some more info.

Our clients (for the most part) already use call recording tools like RingCentral, 8x8, or a recording function within their preferred VOIP tool.

As call data becomes more accessible it will become more valuable, and my guess is we'll see more companies adopt call recording for their inside sales teams.

Regarding legality, some of these call tools have the option of 1-sided recording, meaning they're only recording what the sales rep is saying.

The biggest difference is single call vs. trend data.

We're focused on not just looking at how to dissect a single call (chorus, gong), but how to make sense of a larger dataset of calls. That let's us understand multi-day/week trends and how to best optimize call behavior across an entire team.

As I understand it, People.ai is looking at metadata (call length, percentage of time spent talking vs. listening, etc.).

Thank you! We're keeping the particulars of that piece on the DL for now, but feel free to ping me at daria@voiceops.com if you want to talk shop offline.

thank you :)

The current process on most sales teams is managers listening to 2 or 3 call recordings per rep per week (out of hundreds) and basing feedback on that, so having data across dozens and hundreds of calls is a huge win for them. We can transcribe every agent call, but typically don't have to. It's a combination of both ML and human QA'ing when necessary.

Really good question - the way we help managers coach reps isn't giving them the data to say, "you should be making 75 percent more calls in a day." We show managers and reps what the highest performers are saying and when they're saying it, and then visualize week-over-week trends.

Sales reps are thrilled to have this data - it's helping them understand their own call behavior and adjust accordingly, which has a very tight feedback loop for teams with transactional sales (or any high velocity sales cycles).

As a sales rep, if I can see that the person at the top of the leaderboard is asking 8 probing questions per call and hardly ever highlighting product features, and I'm only asking 2 probing questions and talk extensively about features, that's extremely valuable and actionable information.

We aren't tracking rep/agent satisfaction quantitatively, but anecdotally we're seeing highly positive feedback.