Company and use case

Soundsensing specializes in predictive maintenance for HVAC units in commercial real estate using AI and sensors. By offering early warnings of potential issues, Soundsensing helps property owners and managers proactively address maintenance needs. This approach not only reduces costs and downtime but also improves energy efficiency and environmental impact.

Performance problems to solve

Initially, Soundsensing faced significant challenges with their existing database infrastructure. Using PostgreSQL, they experienced slow query speeds and performance bottlenecks as their sensor data increased. Managing large volumes of time-series data with traditional methods proved inefficient, hindering their ability to provide timely insights and alerts to their clients.

Performance gains unlocked

Transitioning to Tiger Data provided Soundsensing with substantial performance improvements. Queries on historical data became up to 20 times faster, and the system could scale seamlessly to handle ten times the number of sensors without performance degradation. Additionally, Tiger Data's compression features significantly reduced storage costs, allowing Soundsensing to deliver more efficient and cost-effective solutions to their customers.