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Delphi, a two-year-old San Francisco AI startup named after an ancient Greek oracle, The complete 21st century problem: That “digital mind”– Interactive and personalized chatbots modeled after end users are intended to guide their voices based on writing, recordings and other media – It was drove in the data.
Each Delphi can pull 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 have already used them to share insights and attract audiences.
However, new uploads of podcasts, PDFs, or social posts to Delphi each added complexity to the underlying system of the company. Staying responsive in real time without breaking these AIs has become difficult weekly.
Thankfully, Dephi found a solution to the scaling struggle using beloved pine cones in a managed vector database.
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Open source so far
Delphi’s early experiments relied on open source vector stores. These systems quickly buckled under the company’s needs. Indexes grow in size, slow searches, and use complex scales.
Latency spikes during live events or sudden content can risk breaking down the flow of the conversation.
Worse, Delphi’s small but growing engineering team has found themselves spending weeks of tuning and managing sharding logic instead of building product features.
Pinecone’s fully managed vector database with Soc 2 compliance, encryption, and built-in namespace separation turned out to be a better path.
Each digital mind has its own namespace within Pinecone. This ensures privacy and compliance, reducing search surface area and improving performance when users retrieve knowledge from the repository of data they use.
Author data can be deleted with a single API call. Searches consistently return in under 100 milliseconds at the 95th percentile. It accounts for less than 30% of Delphi’s strict 1-second end-to-end latency targets.
“With Pinecone, you don’t have to think about whether it works or not,” he said. Samuel Spelsberg, co-founder and CTO of Delphiin a recent interview. “This will allow the engineering team to focus on application performance and product capabilities rather than semantic similarity infrastructure.”
The architecture behind the scale
At the heart of Delphi’s system is the Searched Generation (RAG) pipeline. Content is ingested, cleaned and chunked. Then embed using Openai, Humanity, or Delphi’s own stack model.
These embeddings are stored in pine cones under the correct namespace. At query time, Pinecone obtains the most relevant vector in milliseconds and is then fed into large language models, a popular technique known through the AI industry, and then fed into large language models. Search Extension Generation (RAG).
This design Delphi can maintain real-time conversations without overwhelming system budgets.
As Jeffrey Zhu, Vice President of Products at PineconeThe major innovation discussed was to move from traditional node-based vector databases to an object storage-first approach.
Instead of keeping all data in memory, Pinecone dynamically loads vectors as needed, offloading idle data.
“It really matches the Delphi usage pattern,” Zhu said. “Digital minds are called in bursts rather than constantly. By separating storage and calculations, reducing costs while enabling horizontal scalability.”
Pinecone automatically tunes the algorithm according to the namespace size. A small Delphis can only store thousands of vectors. Others include millions of people derive from creators with decades of archives.
Pinecone adaptively applies the best indexing approach for each case. As Zhu said, “We don’t want our customers to have to choose an algorithm or wonder about recalls. We handle it under the hood.”
Variations between creators
Not all digital minds look the same. Some creators upload relatively small datasets, such as social media feeds, essays, or course materials.
Others get much deeper. Spelsberg described one expert who contributed to hundreds of gigabytes of scabies, spanning decades of marketing knowledge.
Despite this distribution, Pinecone’s serverless architecture allowed Delphi to expand 100 million preservation vectors Crossing Over 12,000 Namespaces Without hitting the scaling cliff.
Searches are consistent even when spikes triggered by live events or content drops. Delphi now supports it Globally per 20 seconds queriessupports simultaneous conversations in time zones with zero scaling incidents.
Towards a million digital minds
Delphi’s ambition is to host millions of digital minds. This is a goal that requires that at least 5 million namespaces be supported on a single index.
In the case of Spelsberg, that scale is not a hypothesis, it is part of the product roadmap. “We’re already moving from seed stage ideas to a system that manages 100 million vectors,” he said. “The reliability and performance we see give us confidence to scale aggressively.”
Zhu agreed, noting that Pinecone’s architecture is specifically designed to handle bursty multi-tenant workloads like Delphi. “An agent applications like this cannot be built on crack-breaking infrastructure at scale,” he said.
Why is rags important and will they be in the near future?
As the context window for large language models grows, some in the AI industry suggest that RAG can become obsolete.
Both Spelsberg and Zhu push back that idea. “Even if you have a billion context windows, rags are still important,” Spelsberg says. “You always want to surface the most relevant information, otherwise you’re wasting money, increasing latency and distracting the model.”
Zhu framed that Context Engineering – Recently, the term Pinecone has been used in its own technical blog posts.
“LLM is a powerful inference tool, but it requires constraints,” he explained. “Dumping on everything you have is inefficient and can lead to worse outcomes. Simply organizing and narrowing the context isn’t cheaper. It will improve accuracy.”
Covered by Pinecone’s own context engineering writing, search helps manage the finite attention span of the language model by curating the appropriate combination of user queries, previous messages, documents, and memory.
Without this, Windows will be full and the model will track important information. This allows applications to remain relevant and reliable over long-term conversations.
From black mirrors to enterprise grades
When Venture Beat first profiled Delphi in 2023, the company attracted $2.7 million seed funding and attention for its ability to create compelling “clones” of historical figures and celebrities.
CEO Dara Ladjevardian traced the idea into a personal attempt to reunite with her late grandfather through AI.
Today, framing is mature. Delphi emphasizes the digital mindset not as a gimmick clone or chatbot, but as a tool to expand your knowledge, education and expertise.
The company sees applications in professional development, coaching, and enterprise training. This is the domain where accuracy, privacy and responsiveness are the most important.
In that sense, our collaboration with Pinecone represents more than just a technical fit. It is part of Delphi’s efforts to shift the story from novelty to infrastructure.
Digital Mind is currently positioned as reliable, secure and enterprise-ready – Because they sit on a search system designed for both speed and trust.
What’s next for Delphi and Pinecone?
Looking forward to it, Delphi plans to expand its feature set. One future addition is “interview mode.” Here, you can ask questions to your own creator/source person to fill the knowledge gap.
This reduces the barrier to entry for people who don’t have a large archive of content. Meanwhile, Pinecone continues to improve its platform, adding features like adaptive indexing and memory-efficient filtering to support more sophisticated search workflows.
In both companies, trajectory refers to scale. Delphi envisions millions of digital minds that are active across domains and audiences. Pinecone sees it as the search layer for the next wave of agent applications where context engineering and search are essential.
“Reliability has given us the confidence to expand.” Spelsberg said. Zhu repeated his feelings: “It’s not just about managing vectors, it’s about enabling a whole new class of applications that require both speed and trust at scale.”
As Delphi continues to grow, millions of people quietly power the digital mind, a living repository of knowledge and personality, and under the hood of Pinecon.
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