In just a few weeks after its launch, Delphi Oracle has become an essential tool for accessing cryptocurrency research content.
Author: Mira
Compiled by: Deep Tide TechFlow
The Paradox of Research
Delphi's research reports are legendary in the crypto space. When they publish analyses on new token mechanisms or DeFi protocols, project founders take notes, venture capitalists (VCs) adjust their investment logic, and traders reconfigure their portfolios. Their research has a profound impact on the allocation of billions of dollars in capital within the Web3 space.
But the problem is: as the gold standard of institutional research, it also brings an unexpected dilemma. It is this depth and rigor that makes their analyses incredibly valuable, but it also makes them seem daunting. A typical Delphi report may cite dozens of other reports, involve technical concepts that require background knowledge, and assume that readers are familiar with the market mechanisms of the evolving crypto industry.
“We have an amazing body of research, but we constantly hear people complain about how difficult it is to navigate,” explained Carter Lundy, Senior Vice President of Operations at Delphi Digital. “Someone might stumble upon a report on MEV (Maximum Extractable Value) but get lost because they don’t understand the underlying concepts. We miss out on a lot of potential value because of this.”
The obvious solution seemed to be an AI assistant. A tool that could explain concepts on demand, summarize lengthy analyses, and guide readers through Delphi's vast research library. In 2023, with the global rise of ChatGPT, this path seemed clear.
The Failure of Initial Attempts
When Delphi initially explored the AI assistant, they found the problem to be far more complex than they had imagined. The team integrated a state-of-the-art language model into their platform and began testing, but the results were concerning. The AI confidently misinterpreted concepts and even fabricated token metrics that sounded plausible but were completely false. At times, it even misrepresented Delphi's own published viewpoints.
“We couldn’t launch a product that might spread misinformation and be associated with our brand,” Lundy recalled. “Our reputation is everything.”
Even when they attempted to use the most advanced models available at the time, the economic costs were prohibitive. Each complex query about token economics or DeFi mechanisms could cost several dollars to process. For a platform with thousands of users daily, such costs were clearly unsustainable.
After months of frustration, they ultimately terminated the project. The realization of an AI assistant would have to wait for more advanced technology to emerge.
Web3 Native Solutions
The breakthrough came from an unexpected place. While researching the intersection of AI and the crypto space for an upcoming report, the Delphi team discovered Mira Network. What attracted them was not just another AI API, but Mira's fresh approach to making AI more reliable and economically viable.
“Most AI companies focus on building larger models or optimizing prompts,” Lundy explained. “Mira posed a different question: How can we make AI's answers trustworthy? How can we make high-quality AI economically viable at scale?”
The two parties decided to collaborate to push the limits. If they could make Delphi Oracle work successfully, it would prove that AI could handle even the most complex content with high accuracy requirements.
Triple Innovative Approaches
Through collaboration with Mira and its ecological application Klok, the team developed three innovative technologies that transformed Delphi Oracle from “impossible” to “indispensable.”
- ### Smart Query Routing
Looking back, the first insight was embarrassingly simple: not every question needs to be answered by an AI model. When someone asks for the current price of ETH, why send that question to an expensive language model instead of directly querying a price API?
The team developed a lightning-fast router that can instantly classify queries:
Price requests are directed straight to market data
Simple definitions are extracted from the knowledge base
Complex analytical questions are handled by the full AI model
This routing system significantly reduced costs while also speeding up responses to common questions.
- ### Smart Caching
The second innovation stemmed from studying user behavior. They found that many user questions were simply rephrased, such as: “Summarize this report,” “Explain this concept,” “What are the key points?”
The system pre-generates high-quality answers to common questions and provides them in a cached form, rather than regenerating them each time. The key is knowing what to cache: report summaries are fixed, but queries about “latest developments” require real-time updated answers.
- ### Verification Layer
The third innovation addressed the reliability issue. By integrating Mira's verification API, the system can check the accuracy of answers before presenting them to users. This gave the Delphi team confidence to let AI handle their most complex content.
The Power of Transformation
In just a few weeks after its launch, Delphi Oracle has become an essential tool for accessing cryptocurrency research content. Today, the average user interacts with the Oracle at least once a day, and this number continues to grow.
“What surprised us the most was how it changed users' reading habits,” Lundy shared. “Previously, users would give up reading when they encountered complex parts, but now they ask the Oracle questions, get explanations, and continue reading instead of abandoning it halfway.”
This impact is not limited to understanding. Readers began to discover connections between reports they had previously overlooked. They would ask the Oracle to find research related to specific topics. Some users even utilized it to generate summaries for their teams or investment committees.
Most importantly, the economic issue has finally been resolved. By combining smart routing, caching, and Mira's API, the effective cost per query has been reduced by about 90%. The once exorbitant fees have now become sustainable, even for large-scale applications.
Beyond Cost Optimization
The real victory lies not in cost reduction, but in the possibilities that these saved resources bring. Delphi no longer needs to limit AI capabilities to premium subscription users; they can now open the Oracle to everyone. They no longer worry about the cost of each query but focus on how to make the product truly useful.
Today, the system can handle a range of needs from basic questions (“What is AMM?”) to complex comprehensive analyses (“How does Delphi's view on L2 scaling differ from its earlier research on sidechains?”). It has become a bridge connecting Delphi's expert analysts with the broader crypto community.
“We thought we were building an auxiliary tool,” Lundy recalled. “But in reality, we created a whole new way for people to interact with research content. Now some users start with the Oracle and dive deeper into specific reports based on what they learn. This has completely changed the user journey.”
Future Blueprint
Delphi Oracle has become a model case for other platforms facing similar challenges. Whether it’s financial research firms, technical documentation websites, or educational platforms, they all face the same dilemma: how to make complex content easy to understand without sacrificing accuracy while controlling costs.
The lesson here is not that every platform needs Mira's specific technical architecture, but rather that making AI truly useful requires thinking beyond the model itself. You need an efficient query routing system, strategies for large-scale cost management, and ways to ensure reliability when accuracy is critical.
Looking Ahead
Today, Delphi Oracle processes thousands of queries daily, benefiting everyone from institutional investors seeking in-depth analysis to newcomers trying to understand basic concepts. This system can not only explain what a liquidity pool is but also synthesize cross-chain interoperability viewpoints from multiple research reports.
The Delphi team continues to expand the Oracle's capabilities, exploring features that were not achievable under the old cost structure. They are investigating personalized research paths, multimodal analyses combining text and charts, and even AI-generated research briefs tailored to individual portfolios.
For an industry often criticized for being inaccessible, Delphi Oracle represents a significant breakthrough: it proves that AI can democratize expert knowledge without sacrificing content depth. When you solve the fundamental challenges of reliability and economics, you are not just improving an existing product; you are providing a new way for people to learn, analyze, and make decisions.
The future of AI in research is not to replace human experts but to enable everyone who needs it to access expert knowledge in a way they can understand, whenever they need it. Delphi Oracle shows that such a future has already arrived.
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