OUR SOLUTION
Delivering actionable insights quickly and automating data analysis with AI on MongoDB
Workleap launched a proof of concept of Workleap AI in just a few days. Because the company’s data was already on MongoDB Atlas, it was simple to connect it to the new AI-powered tools using Atlas Search and Vector Search. “When you’re innovating, you need to make decisions fast,” said Roy. “On MongoDB, we were able to jump very quickly into implementing AI and bring Workleap AI to production.”
The company utilizes hybrid search, which combines precise text search with semantic search, to generate unified results from across the company’s tools that power the AI capabilities. With this capability, Workleap AI understands the exact words in a user’s query as well as the meaning behind them. It provides vector embeddings to the data pipelines for its enhanced data retrieval of documents, calendar information, audio recordings, and more using MongoDB Atlas Vector Search. It also uses Atlas Search for fast, efficient relevance-based data retrieval. This hybrid approach means that Workleap AI can quickly retrieve all the necessary information from multiple Workleap products to provide detailed responses and analyses to users.
For example, a customer using Officevibe — Workleap’s team engagement platform for custom surveys, real-time feedback, and actionable insights — can use Workleap AI to perform deep data analysis on their team’s engagement. Instead of simply charting current team data on a graph, Workleap AI also looks at the metrics underlying the chart and the survey questions that team members answered to produce those metrics.
It can search through and analyze the responses to custom team surveys that are stored in Atlas, querying them to provide insightful data based on the qualitative and quantitative information. “With Officevibe, this is powerful because there’s a lot of information for a person to try to get a big picture,” said Roy. “In moments, Workleap AI helps you analyze all that data and act on it.” For example, when a manager asks about team morale, the system can find relevant survey responses, feedback comments, and engagement metrics across all Workleap tools — even if they don't specifically use the word “morale.”
In the past, a team’s manager had to look through all the data manually. Workleap AI uses the data in MongoDB Atlas to do a comprehensive analysis and recommends actions for managers based on the results. “With Workleap AI, the manager can just click on the report and have a full digest of what’s going on with their team, what they should change or continue to do, and they can act on those results quickly,” said Roy.
Customers also use Workleap AI to assist with performance reviews, which involve large amounts of performance and engagement data from multiple sources stored in MongoDB Atlas. “I’ve never heard anyone saying that they love doing performance reviews,” said Roy. “Workleap AI can look at everything an employee has worked on and surface data points about them.”
Overall, both small and large teams have increased their productivity by using Workleap AI. “AI is very good at removing administrative burden from a manager’s shoulders,” said Roy. “Workleap AI helps managers spend better time with their team to help them grow and focus on what’s really important,” said Roy.
OUTCOME
Helping companies improve their culture with rich data insights
With MongoDB Atlas’s support for gen AI capabilities, Workleap has found opportunities to innovate and provide a richer customer experience and help teams improve their work. “What AI is good at is helping professionals make better decisions and automate them,” said Roy. “With AI, if you have a good source of data, you can automate decision-making, which is the holy grail.”
And by using MongoDB Atlas to power its gen AI solutions, the company is positioned well for the changing landscape of technology. “We’ve built Workleap AI as a foundational piece of this company’s future,” said Roy. “We’ve been around for 19 years, and we’re looking toward the next 20.”
The company wants to stay agile and avoid rigid workflows as it plans to continue innovating and evolving. “MongoDB technology helps us learn and develop fast,” said Roy. “The goal is not to have a rigid system. The goal is to use technology to learn as quickly as we can and prepare for the future.”
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