Perplexity has introduced an API service named Sonar that would allow developers and enterprises to embed the company’s generative AI search technology into their applications.
Although real-time information capabilities offer clear advantages, they also introduce additional complexities to an already intricate landscape.
“Enterprises tend to take cautious steps when exploring new technologies like enterprise research, especially when handling sensitive company data,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “Beyond concerns around data privacy, security, and compliance, any large company would first evaluate accuracy and performance. Whether Perplexity can outpace Google and OpenAI in this space will depend on the aggressiveness of Google’s and other competitors’ strategies.”
Real-time information and search is an area where Google excels. To stay competitive, Perplexity will need to explore other differentiators, such as compliance, Gogia said.
But for the tool to succeed, significant customization may be necessary, tailored to specific industries and individual companies.
Initially, and in the near term, use cases are expected to remain highly customized before gradually evolving into industry-standard templates, all while adhering to local compliance requirements, according to Gogia. “Early adopters may come from industries like banking and manufacturing with a mature data posture, which is an essential foundation for any such deployment,” Gogia said. “Architectural maturity is another key requirement for success, as it enables the embedding and native use of such a tool within the architecture.”
ComputerWorld.com
Additional comments by Greyhound Research analyst:
Perplexity’s focus on citation-backed AI responses is a high-priority topic for enterprises that have, to date, taken a wait-and-watch approach, especially given the lack of citations. Perplexity is solving precisely that problem. However, many other grey areas spanning data privacy, depth, scale, audit trail, and others must be addressed before any company can be called a leader. While a definite plus, real-time information capabilities add new complexities to an area already riddled with questions.
It is important to note that enterprises prefer to take baby steps when exploring new technologies, such as enterprise search, primarily when dealing with sensitive company data. In addition to data privacy, security, and compliance concerns, any large company would first want to assess for accuracy and performance rather than deciding solely based on price or choosing a vendor with an early mover advantage. So, whether Perplexity will be able to beat Google and OpenAI in this game will be decided based on how aggressive Google’s and other competitors’ strategies are.
It is worth noting the real-time information and search is an area that Google knows exceptionally well. If Perplexity wants to keep its edge, it must use other levers like compliance as a differentiator.
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Before we understand the use cases, it is critical to note that while enterprise users appreciate citation-backed responses, that alone isn’t enough to use any tool or capability. The entire premise of enterprise search is to combine and make sense of the layers of complex data across functions. Any such tool must aim to bring about a significant change that business intelligence reports could not: democratisation and organisation-wide use of data and insights. This can only happen if and when these tools can:
- put together and assemble data across functions
- synthesise this cross-function data most accurately
- do the above at a performance that does not break the bank, and
- embed themselves in the language and terminology (read as context) preferred by the enterprise user
In terms of actual use cases, they are broad, from the most simplistic tasks of understanding service management issues within the technology function to solving more complex functional business problems across supply chain, finance, sales, marketing, and operations. Such a tool can be valuable in answering complex questions on retail location, product, consumer type, profitability, and lots more. In theorty, the possibilities are truly endless.
Must point out that for such a tool to be successful, deep customisation is required in terms of the vertical and the company in question. To begin with, and in the foreseeable future, use cases will be highly customised and will, over the course, get templatized by the industry while keeping locally mandated compliance requirements in mind.
However, earlier adopters will come from industries like banking and manufacturing with a mature data posture, an essential bedrock for any such deployment. Architectural maturity is another bedrock required to make this a success, which allows the embedding and use of such a tool natively in architecture.
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