Delphi's research reports are legendary in the crypto space. When they publish analyses on new token mechanisms or DeFi protocols, project founders take note of the key points, 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 for institutional research, it also brings an unexpected dilemma. It is this depth and rigor that makes their analysis incredibly valuable, but at the same time it seems daunting. A typical Delphi report may cite dozens of other reports, involving technical concepts that require background knowledge, as well as assuming that the reader is familiar with the market mechanisms of the cryptocurrency industry's development dynamics.
"We have a whole set of amazing research results, but we keep hearing people complain about the difficulty in grasping this content," explained Carter Lundy, Senior Vice President of Operations at Delphi Digital. "Someone might accidentally come across a report on MEV (Maximal Extractable Value) but get lost because they don't understand the concepts behind it. As a result, we miss out on a lot of potential value."
The obvious solution seems to be an AI assistant. A tool that can explain concepts at any time, summarize lengthy analyses, and guide readers through the vast research library of Delphi. In 2023, ChatGPT swept the globe, making this path seem clear.
Failure of the first attempt
Delphi discovered that the issue was much more complex than they had imagined during their initial exploration of AI assistants. The team integrated a state-of-the-art language model into their platform and began testing, but the results were concerning. The AI would confidently misinterpret concepts and even fabricate token metrics that sounded plausible but were completely false. At times, it would even misinterpret Delphi's own published viewpoints.
"We cannot launch a product that could spread misinformation and be associated with our brand," Lundy recalled. "Our reputation is everything."
Even if they tried to use the most advanced models at the time, the economic costs would be hard to bear. Each complex query about token economics or DeFi mechanisms could cost several dollars to process. For a platform that has thousands of users every day, such costs are clearly unsustainable.
After months of setbacks, they ultimately terminated the project. The implementation of the AI assistant had to wait for more advanced technology to emerge.
Web3 native solutions
Breakthroughs come from unexpected places. While researching the intersection of AI and the cryptocurrency field for an upcoming report, the Delphi team discovered the 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 high-quality AI become economically viable at scale?"
Both parties have decided to collaborate to push the limits together. If they can successfully operate Delphi Oracle, it will prove that AI is capable of handling even the most complex content with high accuracy requirements.
Triple Innovation Method
Through collaboration with Mira and its ecological application Klok, the team has developed three innovative technologies that transform Delphi Oracle from "impossible" to "indispensable."
Intelligent Query Routing
Looking back, the first insight is actually embarrassingly simple: not every question needs to be answered with 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 high-speed router that can categorize queries instantly:
Price requests are directly redirected to market data.
Simple definition extracted from the knowledge base
Complex analytical problems are handled by complete AI models.
This routing system significantly reduces costs while also speeding up the response time to common issues.
Smart Cache
The second innovation stems from the study of user behavior. They found that many questions posed by users were just rephrased versions of the same inquiry, such as: "Summarize this report," "Explain this concept," "What are the key points?"
The system provides high-quality answers to common questions through pre-generated responses, offered in a cached form, rather than regenerating every time. The key is to know what to cache: report summaries are fixed, but queries about "latest updates" require real-time updated responses.
Verification Layer
The third innovation addresses the reliability issue. By integrating Mira's verification API, the system can check the accuracy of the answers before presenting them to the user. This gives the Delphi team confidence to let AI handle their most complex content.
The power of transformation
Within a few weeks of its launch, Delphi Oracle has become an important tool for people to access cryptocurrency research content. Nowadays, each user interacts with the Oracle at least once a day on average, and this number continues to grow.
"What surprised us the most was how it changed users' reading habits," Lundy shared. "In the past, users would give up reading when they encountered complex parts, but now they ask Oracle questions, get explanations, and continue reading instead of giving up halfway."
This impact is not limited to the level of understanding. Readers begin to discover connections between reports that they previously overlooked. They will ask Oracle to find research related to specific topics. Some users even utilize it to generate summaries for teams or investment committees.
Most importantly, the economic issues have finally been resolved. By combining intelligent routing, caching, and Mira's API, the effective cost of each query has been reduced by about 90%. The once exorbitant costs have now become sustainable, even for large-scale applications.
Beyond cost optimization
True victory does not lie in reducing costs, but in the possibilities brought by the resources saved. Delphi no longer needs to limit AI capabilities to premium subscribers, but can open 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 demands 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 expert analysts with the broader cryptocurrency community.
"We originally thought we were creating a supportive tool," Lundy recalled. "But in reality, we created a whole new way for people to interact with research content. Now some users start with Oracle, diving into specific reports based on what they've learned. This has completely changed the user's journey."
Future Blueprint
Delphi Oracle has become a model case for other platforms facing similar challenges. Whether it is 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 also keeping costs under control.
The experience here is not that every platform needs Mira's specific technical architecture, but rather to recognize 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 crucial.
Looking to the future
Today, Delphi Oracle processes thousands of queries daily, benefiting everyone from institutional investors seeking in-depth analysis to beginners trying to understand the basic concepts. This system not only explains what liquidity pools are but also synthesizes cross-chain interoperability perspectives from multiple research reports.
The Delphi team is continually expanding Oracle's capabilities, attempting features that cannot be achieved under the old cost structure. They are exploring personalized research paths, multimodal analysis combining text and charts, and even AI-generated research briefs customized for individual portfolios.
For an industry often criticized for being inaccessible, Delphi Oracle represents a significant breakthrough: it proves that AI can disseminate expert knowledge without sacrificing content depth. When you address the fundamental challenges of reliability and affordability, you are not just improving existing products but 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 in need to access expert knowledge in a way that they can understand when they need it. Delphi Oracle indicates that such a future has already arrived.
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The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
Mira Network: Making encryption research reports simple, this is how our AI does it.
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 note of the key points, 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 for institutional research, it also brings an unexpected dilemma. It is this depth and rigor that makes their analysis incredibly valuable, but at the same time it seems daunting. A typical Delphi report may cite dozens of other reports, involving technical concepts that require background knowledge, as well as assuming that the reader is familiar with the market mechanisms of the cryptocurrency industry's development dynamics.
"We have a whole set of amazing research results, but we keep hearing people complain about the difficulty in grasping this content," explained Carter Lundy, Senior Vice President of Operations at Delphi Digital. "Someone might accidentally come across a report on MEV (Maximal Extractable Value) but get lost because they don't understand the concepts behind it. As a result, we miss out on a lot of potential value."
The obvious solution seems to be an AI assistant. A tool that can explain concepts at any time, summarize lengthy analyses, and guide readers through the vast research library of Delphi. In 2023, ChatGPT swept the globe, making this path seem clear.
Failure of the first attempt
Delphi discovered that the issue was much more complex than they had imagined during their initial exploration of AI assistants. The team integrated a state-of-the-art language model into their platform and began testing, but the results were concerning. The AI would confidently misinterpret concepts and even fabricate token metrics that sounded plausible but were completely false. At times, it would even misinterpret Delphi's own published viewpoints.
"We cannot launch a product that could spread misinformation and be associated with our brand," Lundy recalled. "Our reputation is everything."
Even if they tried to use the most advanced models at the time, the economic costs would be hard to bear. Each complex query about token economics or DeFi mechanisms could cost several dollars to process. For a platform that has thousands of users every day, such costs are clearly unsustainable.
After months of setbacks, they ultimately terminated the project. The implementation of the AI assistant had to wait for more advanced technology to emerge.
Web3 native solutions
Breakthroughs come from unexpected places. While researching the intersection of AI and the cryptocurrency field for an upcoming report, the Delphi team discovered the 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 high-quality AI become economically viable at scale?"
Both parties have decided to collaborate to push the limits together. If they can successfully operate Delphi Oracle, it will prove that AI is capable of handling even the most complex content with high accuracy requirements.
Triple Innovation Method
Through collaboration with Mira and its ecological application Klok, the team has developed three innovative technologies that transform Delphi Oracle from "impossible" to "indispensable."
Intelligent Query Routing
Looking back, the first insight is actually embarrassingly simple: not every question needs to be answered with 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 high-speed router that can categorize queries instantly:
Price requests are directly redirected to market data.
Simple definition extracted from the knowledge base
Complex analytical problems are handled by complete AI models.
This routing system significantly reduces costs while also speeding up the response time to common issues.
Smart Cache
The second innovation stems from the study of user behavior. They found that many questions posed by users were just rephrased versions of the same inquiry, such as: "Summarize this report," "Explain this concept," "What are the key points?"
The system provides high-quality answers to common questions through pre-generated responses, offered in a cached form, rather than regenerating every time. The key is to know what to cache: report summaries are fixed, but queries about "latest updates" require real-time updated responses.
Verification Layer
The third innovation addresses the reliability issue. By integrating Mira's verification API, the system can check the accuracy of the answers before presenting them to the user. This gives the Delphi team confidence to let AI handle their most complex content.
The power of transformation
Within a few weeks of its launch, Delphi Oracle has become an important tool for people to access cryptocurrency research content. Nowadays, each user interacts with the Oracle at least once a day on average, and this number continues to grow.
"What surprised us the most was how it changed users' reading habits," Lundy shared. "In the past, users would give up reading when they encountered complex parts, but now they ask Oracle questions, get explanations, and continue reading instead of giving up halfway."
This impact is not limited to the level of understanding. Readers begin to discover connections between reports that they previously overlooked. They will ask Oracle to find research related to specific topics. Some users even utilize it to generate summaries for teams or investment committees.
Most importantly, the economic issues have finally been resolved. By combining intelligent routing, caching, and Mira's API, the effective cost of each query has been reduced by about 90%. The once exorbitant costs have now become sustainable, even for large-scale applications.
Beyond cost optimization
True victory does not lie in reducing costs, but in the possibilities brought by the resources saved. Delphi no longer needs to limit AI capabilities to premium subscribers, but can open 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 demands 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 expert analysts with the broader cryptocurrency community.
"We originally thought we were creating a supportive tool," Lundy recalled. "But in reality, we created a whole new way for people to interact with research content. Now some users start with Oracle, diving into specific reports based on what they've learned. This has completely changed the user's journey."
Future Blueprint
Delphi Oracle has become a model case for other platforms facing similar challenges. Whether it is 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 also keeping costs under control.
The experience here is not that every platform needs Mira's specific technical architecture, but rather to recognize 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 crucial.
Looking to the future
Today, Delphi Oracle processes thousands of queries daily, benefiting everyone from institutional investors seeking in-depth analysis to beginners trying to understand the basic concepts. This system not only explains what liquidity pools are but also synthesizes cross-chain interoperability perspectives from multiple research reports.
The Delphi team is continually expanding Oracle's capabilities, attempting features that cannot be achieved under the old cost structure. They are exploring personalized research paths, multimodal analysis combining text and charts, and even AI-generated research briefs customized for individual portfolios.
For an industry often criticized for being inaccessible, Delphi Oracle represents a significant breakthrough: it proves that AI can disseminate expert knowledge without sacrificing content depth. When you address the fundamental challenges of reliability and affordability, you are not just improving existing products but 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 in need to access expert knowledge in a way that they can understand when they need it. Delphi Oracle indicates that such a future has already arrived.