We utilize excess human capacity for everything from ride-sharing and housing to grocery shopping and dog walking in order to make our lives more instant. And when something isn’t instant, our engagement drastically declines.
Still, it was non-existent in the hiring process–a process that has seen little to no innovation in decades. She began to focus on how to utilize excess capacity in the hiring funnel, specifically during the interview process, to increase applicant engagement for companies and, ultimately, get more people hired.
Amber founded Vette in May 2020 as an “Instant Human Engagement” platform that would connect companies with human “Vetters” to conduct screenings of their candidate pools. This solution, driven by SMS and phone communication, would allow companies to engage with applicants instantly after submitting an application and would allow applicants to interview with an actual human (“Vetter”) at any hour of any day.
She raised capital, then built out the proof of concept, working with a beta customer and proving out the need for Vette. While initially focused on the highly skilled workforce, she switched gears when she saw a more pressing need in the market of high-volume, hourly, service-focused positions. With this in mind, in 2021, Amber needed a technology partner to build out the entire Vette platform with a delightful user experience and the ability to scale. Additionally, she needed a product team to implement processes, techniques, and frameworks to make it all happen.
She approached OAK’S LAB.
In order for the platform to become a pioneer for “Instant Human Engagement,” Amber needed to ensure that the solution was functional. After building out a proof of concept that tested the core functionality of how Vette could operate, we now had to find a way to build a product that could scale to handle thousands of interviews and enterprise clients.
The goal was to make it instant, but also user-friendly. We needed to focus on building an automated messaging functionality, allowing us to engage applicants in a seamless and appealing way immediately upon application. We also needed to create a user experience that delighted both Vetters and enterprise customers. And finally, we needed to build product development processes that could allow us to understand the problems users face and that would allow us to effectively prioritize and manage a growing backlog of feature requests.
To initiate the project, we hosted Amber in Prague so that she could meet the dedicated product team that would build and redesign her product. We set up a foundation phase during which we went through a discovery process to understand what features we needed to build and why. Together with Amber we aligned on our shared goals and defined our ideal customer profile.
In order to address and define the core functionality of the platform, we leveraged Amber’s existing proof of concept alongside our business process analysis to find the solution. Once we knew we could build a functional platform, we then leveraged our goals to prioritize additional features using the MOSCOW prioritization method. Our main focus at this stage was to get a product to the market as quickly as possible in order to begin collecting valuable feedback from all users.
We wanted to design a platform that handles thousands of SMS and phone conversations on a daily basis and supports the whole process of an applicant getting vetted. So, it needed to be scalable and stable. For this reason, choosing the right infrastructure, architecture, and 3rd party tools was vital.
First, we chose Twilio to handle all the SMS and phone calls. We hosted the platform on a cloud environment that had an auto-scaling feature, so when a large load of conversations came in, the infrastructure would scale itself. For stability, we needed to design a system that implemented master nodes, which were responsible for orchestrating the whole interview process, reacting to status changes and handling the concurrent processing in the platform. We used Firebase Realtime Database on the backend and on the frontend where it was able to reflect what was happening in the interview process in real-time in the application.
In June 2022, we successfully released the first version of the product and immediately began collecting user feedback. Now that we had a functioning product and users, we needed to simultaneously collect and implement the feedback in order to optimize the user experience in the platform. We began operating on a dual-track agile methodology, which is integral to the OAK’S LAB WAY, simultaneously running discovery and delivery tracks.
This dual-track process was crucial to develop a user-friendly platform for Amber. We worked closely with Vette’s Sales and Customer Success teams to ensure that we were constantly generating feedback and understanding the value of features using the RICE Scoring Model, which allowed us to evaluate what to prioritize. As part of this discovery/delivery process, we were continuously wireframing new concepts and running user testing to gain feedback on potential functionality.
We also worked with Vette’s sales team to help define our target customer in greater detail. Now that we have more data to use, we could prioritize which features will add actual value to our target customers and bring in maximum revenue for Vette.
The Vette client base has now expanded to include major enterprise businesses, and we continue to grow the business by integrating with new ATS partners and building new features. With this growth and success, we introduced Amber to our VC partner, StageDotO.
As we continued with the dual-track process, we decided to use the advancements of generative AI to enhance the Vette platform in 2023. While Vette is first and foremost focused on “Instant Human Engagement”, we saw an opportunity to use AI to extract transcripts from the human conversations that take place between the Vetter and the applicant in order to process valuable interview data directly within the platform. We built an integration with Whisper, an OpenAI automatic speech recognition (ASR) system, that allows Vette users to easily access the transcripts of all conversations, which they then can use to more quickly and efficiently screen applicants. The Whisper AI model is trained on 680,000 hours of data, making the open-source library the ideal speech-to-text AI solution to run on our infrastructure.
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Today, Vette works with enterprise clients across the manufacturing, fast food, automotive, retail, and hospitality industries.
OAK’S LAB successfully built Amber’s “Instant Human Engagement” MVP. Applicants apply to a company job posting, and within seconds, they receive a text message inviting them to interview by phone whenever it is convenient. The applicant initiates their interview with the press of a button, and within 30 seconds, a human interviewer calls and conducts an interview with them. The product is functional. It’s user-friendly. And it’s instant.
By utilizing the gig economy to conduct phone interviews, applicants are able to interview as soon as they express interest in a job, and in return, companies are able to secure the applicants faster than their competitors resulting in higher interview-to-hire conversions.
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