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Delphi, a two-year-old San Francisco AI startup named after the Ancient Greek oracle, was facing a thoroughly 21st-century problem: its “Digital Minds”— interactive, personalized chatbots modeled after an end-user and meant to channel their voice based on their writings, recordings, and other media — were drowning in data.
Each Delphi can draw from any number of books, social feeds, or course materials to respond in context, making each interaction feel like a direct conversation. Creators, coaches, artists and experts were already using them to share insights and engage audiences.
But each new upload of podcasts, PDFs or social posts to a Delphi added complexity to the company’s underlying systems. Keeping these AI alter egos responsive in real time without breaking the system was becoming harder by the week.
Thankfully, Dephi found a solution to its scaling woes using managed vector database darling Pinecone.
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Open source only goes so far
Delphi’s early experiments relied on open-source vector stores. Those systems quickly buckled under the company’s needs. Indexes ballooned in size, slowing searches and complicating scale.
Latency spikes during live events or sudden content uploads risked degrading the conversational flow.
Worse, Delphi’s small but growing engineering team found itself spending weeks tuning indexes and managing sharding logic instead of building product features.
Pinecone’s fully managed vector database, with SOC 2 compliance, encryption, and built-in namespace isolation, turned out to be a better path.
Each Digital Mind now has its own namespace within Pinecone. This ensures privacy and compliance, and narrows the search surface area when retrieving knowledge from its repository of user-uploaded data, improving performance.
A creator’s data can be deleted with a single API call. Retrievals consistently come back in under 100 millisecond …