"The hands-on approach from the Microsoft AI Co-Innovation Lab was instrumental in helping us rapidly prototype a solution that would have otherwise taken months. Their team gave us the tools, guidance, and secure environment to accelerate our development while navigating the complexities of working with large language models and sensitive hospitality data. We left the Lab with a solid framework and the foundation for our AI-powered recommendation engine, including an MVP we could continue to refine and build upon." – Stephanie Smith, CEO
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Co-Innovation Challenge

Cogwheel Analytics is an enterprise business intelligence and reporting platform in the hospitality industry, known for helping hotel clients identify marketing and revenue trends, close performance gaps, and benchmark their performance against other hotels.

Hotels often face challenges synthesizing data across platforms and hotel locations, especially at the enterprise level. This process relies on manual analysis and disjointed reporting, making it both labor intensive and error prone.

This makes it difficult for teams to quickly identify what is working and where to focus efforts, whether it is adjusting spend for an upcoming holiday weekend or evaluating the long-term impact of online travel agency partnerships on direct bookings.

Given these challenges and the incredible amount of data available, Cogwheel Analytics wanted to develop an AI-powered analysis and recommendation tool that could provide actionable insights that drive growth and build competitive advantage for their clients on top of their existing business intelligence data. Their goals were to:

  • identify trends and underperforming hotels.
  • analyze gaps using benchmarking scorecards.
  • create a recommendation engine to address future issues.
  • establish a feedback loop to understand the impact of implemented strategies.

To make this idea a reality, they partnered with the Microsoft AI Co-Innovation Labs to design a solution architecture, train the Cogwheel Analytics team on Azure tools and large language models, and come up with a working prototype of the solution.

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Cogwheel Analytics CEO Stephanie Smith at the AI Co-Innovation Lab in Redmond

In the Lab

Together in the AI Co-Innovation Lab in Redmond, we co-built the solution using Azure AI Search and Azure OpenAI Service, which gather the hotel data dynamically and analyze it for gaps and trends. The tool determines which data is relevant and then generates a report for the hotel employees to leverage for decision-making on critical issues like room rates, forecasting occupancy, critical hotel upgrades, and operational issues.

This solution both streamlined the analysis process and could ensure hotel decision makers were acting on insights using all relevant data.

What was critical for Cogwheel Analytics was the AI Co-Innovation Lab team’s knowledge and ability to set up an environment where the teams could train, test, and refine the AI model quickly and effectively. In addition, the Lab provided essential learning on Azure technologies that the Cogwheel Analytics team needed to deploy and run the tool.

In a matter of four days, the Cogwheel Analytics team had an AI solution that could find data across different sites, synthesize that information, and present recommendations and forecasts to hotel decision makers automatically, enabling them to make informed decisions and act quickly based on reliable and thorough insights.

Solution Architecture
Recommendation and Analysis Engine solution architecture

Solution Impact

The AI Co-Innovation Lab team was critical to Cogwheel Analytics’ success by providing the expertise and experience to create a truly innovative tool for hotels. In addition to the solution itself, the Lab team ensured Cogwheel Analytics was set up for success with the right upskilling on different Azure technologies and best practices for AI solutions.

The AI-powered Analysis and Recommendation Engine is showing promising results in testing. It significantly reduces time spent on analysis and surfaces actionable insights that were previously difficult for hotel clients to uncover.

These insights help hotel teams allocate resources more effectively, identify underperforming segments, and make faster, data-backed decisions that improve visibility and market performance. Cogwheel Analytics expects to roll out the Analysis and Recommendation Engine later in 2025 as part of their next product integration.

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