About our AI Co-Researcher
What makes our AI Co-Researcher special?
What makes our AI Co-Researcher different is that it turns a complex intelligence process into a simplified, structured output powered with the help of AI. Most crypto research tools are little more than wrappers that generate open ended summaries through interfaces built around existing AI models. Our AI Researcher takes a different approach. It translates a complex research process into a pre structured, curated, and systematic overview and grading of any digital asset, using AI as the engine rather than as the product itself. The real value lies in the research architecture behind the output, not in AI's raw reasoning alone. The result is not a long and unfocused text, but a short, consistent, actionable, and easier to verify overview with built in benchmarking and red flag detection.
How does it work behind the scenes?
A methodology developed over years:
This system is built on a crypto specific, purpose-built research and scoring framework that has been refined over more than three years by combining DeFi and traditional market metrics and criteria. The methodology is not perfect because the crypto space is still evolving, but it is as comprehensive as possible at this stage and continues to improve as the industry matures. Anyone can propose a methodology in theory. In practice, building one for a new and fast-moving industry is far more difficult. That is part of the value this product brings.
A multi chain prompt stack, not a simple GPT wrapper:
The methodology was translated into a 70-page multi-prompt workflow engine that guides the model through a multi-step reasoning process. Instead of asking one broad question and accepting a vague answer, the system breaks research into structured stages that produce a more consistent overview and multi-level grades. Writing prompts is not difficult. What is harder to replicate is the scale of prompt engineering, testing, and retesting required to make such a system work.
Built in data cross verification:
The system includes a verification layer that cross checks key data points across sources, flags gaps and inconsistencies, and surfaces red flags. The question of how to verify AI generated information is one of the central challenges of our time. There is no perfect answer, and our system is not flawless. But we have developed an approach that draws on long standing methods of data collection and verification from the social sciences, and these methods have proven surprisingly effective in this context.
This approach grows out of real product experience, not just an abstract idea. The broader weDYOR product evolved from a Telegram channel, to a dApp, to a subscription website and now into an AI Research Factory. The result is a research experience that works better than the usual open ended chatbot style because structured and curated research tends to produce better outputs than broad, unstructured questioning.
