Palantir Technologies beat Wall Street revenue expectations on Monday as surging customer demand for artificial intelligence software drove third-quarter sales up 96 percent.
Revenue for the third quarter rose to $2.31 billion, surpassing analyst projections of $2.18 billion. Chief Executive Officer Alex Karp stated in a letter to shareholders that the company had essentially doubled its entire business compared with the corresponding quarter of the previous year.
The growth was driven by strong appetite for data analysis tools from both commercial enterprises and the United States government. Demand for Palantir software, which integrates and analyzes information from multiple sources, is rising as customers seek greater control over their data and artificial intelligence models to safeguard sensitive records and maintain their competitive advantage.
Raised annual guidance
Following the quarterly results, Palantir raised its full-year revenue outlook to between $8.530 billion and $8.534 billion, having previously forecast annual sales of between $8.150 billion and $8.158 billion.
For the fourth quarter, the group projected revenue between $2.649 billion and $2.653 billion. That range sits higher than the average analyst forecast of $2.46 billion, according to consensus data compiled by financial markets infrastructure provider LSEG.
Shares in the company gained 1.5 percent in volatile after-hours trading following the publication of the results.
Company background and commercial expansion
Palantir Technologies, founded in 2003 by investor Peter Thiel and current chief executive Alex Karp, is an American data analytics company based in Denver, Colorado. The firm initially built its reputation by supplying data integration platforms to United States defence and national security agencies, assisting government analysts with complex intelligence and military operations.
The software maker later expanded into the private sector, marketing enterprise software platforms to large corporations across healthcare, energy, automotive manufacturing, and finance. Its systems are designed to connect disparate databases from across an organization, enabling institutions to analyze proprietary operations and run artificial intelligence models without exposing confidential data to external networks.
