How a travel & hospitality startup cut data reporting time from hours to seconds
Adapt TeamAt a Glance
Customer: Wander
Industry: Hospitality & Travel Technology
The Challenge: A growing backlog of data requests was creating a bottleneck, forcing data engineers to spend hours on ad-hoc queries and delaying critical business insights.
The Solution: Adapt, an AI-powered data platform that integrates directly into Slack to answer natural language questions instantly.
The Result: Data inquiries are now resolved in 15 seconds rather than hours, freeing up engineering time and democratizing data access for business and operations teams.
Background: Rapidly Growing Hospitality Startup
Wander is rebuilding the hospitality industry by combining the quality of a luxury hotel with the privacy of a vacation home. The high growth startup raised a $50M Series B investment, and differentiates itself with "WanderOS," a proprietary operating system that controls the traveler experience, from bookings to smart home automation.
As a travel company deeply rooted in technology, data is the lifeblood of Wander’s operations. However, as the company scaled, the volume of questions about that data began to outpace the engineering team’s ability to respond quickly.
The challenge: data request bottleneck
Every team at Wander requires reliable, real-time data to help inform decisions. Before adopting Adapt, internal requests were sent to Wander's data science team who would then do an analysis and send back an answer.
"Essentially we had a channel in Slack where all requests to our data team would go into." - Nathan Potter, CTO of Wander.
The workflow was manual and reactive. An operations manager would post a question, such as "What is the occupancy rate for the Hudson Valley property next month?" or "How is revenue trending for Q3?"
"Our data team would triage and handle all of those accordingly," Potter says. "They would have to go in and either update the model or query the model, get the data, and return it back."
As Wander’s product and user base grew rapidly, the need for data intelligence accelerated, and this process became a bottleneck.
This process was inefficient for two reasons. First, it took hours for business users to get simple answers, slowing down decision-making. Second, highly skilled data engineers were spending valuable time on repetitive tasks, such as writing SQL queries and formatting tables, rather than building long-term data infrastructure.
Potter estimates that for every request, an engineer might spend "an hour or two looking for various data and putting together tables and charts." Wander needed a way to break this cycle and make their data accessible without the manual overhead.
The solution: an AI sata analyst in Slack
Wander turned to Adapt, an AI platform that connects to a company’s data sources and allows users to pose questions using natural language. Crucially for Wander, Adapt lives where the team already works: Slack.
The implementation was seamless. Instead of forcing non-technical staff to learn a complex Business Intelligence tool or write their own SQL, they simply changed who they were asking.
"Now what's happening is we just tag Adapt in that Slack thread," Potter says.
Once tagged, Adapt acts as an autonomous agent. It parses the natural language question, understands the context of Wander's specific business metrics (like "reservations," "revenue," or "ops issues"), and queries the underlying data models directly.
"It answers the questions directly for us. Adapt essentially looks at all of our data, pulls that in, and creates beautiful charts and really intelligent responses."
The Impact: Intelligence in Seconds, Not Hours
The most immediate impact of deploying Adapt was the dramatic reduction in time-to-insight.
"Instead of our data engineers having to go and spend like an hour or two... Adapt can do that automatically for us right in the Slack thread. And it takes like 10 or 15 seconds, which is amazing."
This shift from hours to seconds has fundamentally changed the rhythm of the business. When a question arises during a meeting or a strategy session, the team no longer has to park the discussion and wait for a report. They can tag Adapt and get the data immediately, keeping the momentum going.
Democratizing data access
Beyond speed, Adapt has successfully democratized data access across Wander. Previously, data was the domain of the engineering team, but now, every authorized employee can make data driven decisions.
Potter has observed adoption spreading rapidly beyond the technical teams to business users and operations staff.
"Now what we're starting to see is business users using it all across our Slack," Potter explains.
Staff members are asking complex, domain-specific questions without needing technical syntax. Potter cites several examples of the types of queries now being handled automatically:
- "How many reservations do we have upcoming for the next two months?"
- "What is the revenue growth for this property?"
- "What are the most common issues that our ops team is handling?"
Adapt provides contextual answers in rich formats such as charts, trend lines, and slides. Furthermore, the interaction doesn't stop at a single answer.
"You can continue diving into it further to have it answer even more fine-grained requests after that," Potter adds. Adapts "conversational" ability allows users to explore data as a team, drilling down into anomalies or asking follow-up questions just as they would with a human analyst.
Bridging the gap
For Wander, Adapt has broken the silo between their rich data warehouse and the people who need to use it. By automating the last mile of analytics, including fetching and formatting data, Adapt has freed the engineering team to focus on building data infra that powers the future of travel, while empowering the rest of the company to make faster, data-driven decisions.