Building the future of conversation intelligence
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Chorus.ai offers sales teams an easy way to capture and summarize call notes so they can focus on really listening to customers.
GitLab’s single tool for the software development lifecycle helps Chorus.ai capture the power within sales calls.
From dream to reality
Chorus.ai was built out of the fire of three dreamers who were passionate about forming a new company. After watching the market and seeing the rise of smart home gadgets and new machine learning capabilities, the trio decided to build software around analyzing conversations. Focusing on conversations around sales, Chorus.ai enables account executives to focus on what is actually being said and respond appropriately — instead of taking notes.
They developed Chorus.ai as a conversation intelligence platform. It records, transcribes, and analyzes customer conversations to help sales teams and support teams. The platform offers transcription and AI-based analytics as well as sharable snippets to easily share conversational insights with other team members.
Creating a bold new development process
How do you design a build process from scratch? For Chorus, the founders relied on previous knowledge and experience to decide on a single development tool. They decided they didn’t want a toolchain comprised of disparate tools and plugins, so they searched for a DevOps solution to meet their needs.
With GitLab, the teams are able to focus on development instead of processes and systems. Using GitLab has also increased the collaboration between teams. “Life is just so much easier for product engineering and anyone who wants to interact with product engineering because they can just do it through GitLab,” Levy explained.
A single tool to integrate development teams
Chorus.ai selected GitLab because the team was impressed by the integration the application offers. Levy says that GitLab is the only product on the market that offered a true integration and made every process easy.
“The only other company that has anything that’s close to GitLab in our view was Atlassian, and they may offer all those tools under the Atlassian company, but they’re not integrated. So you buy Bitbucket, and it somehow ties up with this one. Then you have to deal with pricing on each one, users for each one … the whole thing, it’s just a bunch of products that are stitched together,” Levy explains.
During a recent audit for SOC2 compliance, the auditors said that Chorus had the fastest auditing process they have seen and most of that is due to the capabilities of GitLab. Levy explained that it was easy to write a script and get the information from the API to make the audit easier.
Speed, security, and Auto DevOps
Because Chorus was developed with GitLab from an early stage, they conquered common build challenges that many companies experience. They made the conscious decision to keep everyone collaborating with a single tool to avoid the use of plugins and disparate tools. Within GitLab, Chorus is able to increase development velocity by beginning sprints in GitLab as epics. Teams use issues and boards to improve their capacity planning. For releases, teams run their pipelines using GitLab CI/CD.
Chorus has also shortened its feedback loops by using the SAST and DAST capabilities within GitLab. “We started using the GitLab security dashboard a month ago. It has been very beneficial to see if there are new security issues that are coming up. It really helps the developers. We can now show them why their code and truly show them why this is an issue, and how it could be exploited,” Levy said.
The team at Chorus also credits GitLab for helping them improve their feature cycle analytics. By having test results, security reviews, performance tests, the code climate and everything in the merge requests, Chorus has been able to move quickly. Chorus is also utilizing the Auto DevOps capabilities of GitLab to deploy to their AWS S3 servers. GitLab Auto DevOps automates CI/CD configuration to simplify the execution of their development.
All information and persons involved in case study are accurate at the time of publication.