Building AI for investors takes more than giving financial data to a model or agent harness.
It means preprocessing qualitative and quantitative data so agents can use it reliably, building custom tools for investment workflows, and developing a dynamic, self-updating industry and company knowledge base to help agents interpret what they find. Each requires substantial work beyond connecting a model or harness to a data source.
We've spent a lot of time building these foundations at Implied, and we're now seeing the benefits as they work together.
Take a common research task: comparing commentary from 250+ companies that presented at recent conferences against their prior earnings calls. What changed in quarter-to-date trends, forward guidance, or M&A appetite?
The hard part is ensuring the agent actually read every document, found the accompanying materials, and pulled prior commentary for comparison. A large context window doesn't solve that. You need a systematic process that lets analysts verify how the work was done, follow the supporting evidence, and identify gaps before trusting the conclusions.
Implied handles this by using our proprietary AI-native spreadsheet as an intermediate research step. It compiles current commentary, prior statements, comparisons, and sources across the tickers into a structured workbook. Analysts can check that every document has been analyzed, verify findings against the sources, and customize or extend the research.
Our chat then brings together findings from across the workbook to build a comprehensive answer to the investor’s question, with citations to the underlying sources. The workbook serves as both a research scratchpad for the agent and an auditable workspace for the investor.
This is just one example of what Implied’s AI-native foundation makes possible. We have more capabilities to show you soon. Stay tuned!