Implied

Every fund has a chatbot. Most investors tell us their process doesn't feel much faster. The time AI saves on search gets spent stitching everything else together across an already fragmented stack. The mistake is treating AI as one more tool. We believe it's the first real opportunity to rethink the research process from scratch, and we've spent the last two and a half years building exactly that: one system for the entire workflow, run by an agent that knows your names, learns from live information, and works ahead of you. There's a lot to build, and a race to build it. The first funds to get there lock in a multi-year lead. So what does it take? Not to deploy a chatbot, but to actually build an AI-native research platform? We just published our first post on exactly that. Link in the reply.

How long does it take you to update a model during earnings?

AI can sound like a great analyst without knowing how to invest. It can build models, forecast KPIs, and write convincing investment theses. But how much of that translates into actual investment judgment? Take a seemingly simple task: given the actual earnings results, will the stock go up or down? We removed the need to forecast the numbers by giving seven AI models the actual earnings results and asking whether the stock would go up or down. Across 26 earnings cases, the models achieved accuracy rates ranging from 49% to 69%. A simple strategy of always predicting “up” achieved 54%. Even with the numbers in hand, the models struggled to interpret what they meant for the stock. Our view is that what’s missing is the context and nuance behind investment judgment: which metrics matter for a particular company, what’s already priced in, and how the same results can trigger different reactions depending on the market. We’re expanding this initial study this earnings season. We have a few ideas for closing that gap, and we’re eager to put them to the test.

AI modeling doesn't need a better agent. It needs a better spreadsheet. Most tools ask an agent to write code that populates or edits Excel. As the workbook grows, so does everything the agent has to keep straight: numbers, cell locations, formulas, sources, and company-specific instructions. A longer prompt can't fix the wrong infrastructure. So we built an AI-native spreadsheet from scratch. In Implied Sheet, AI lives in the formulas. Instead of remembering and reproducing every number, the agent builds source-linked formulas that stay inside the model. Analysts can inspect citations, edit the model directly, and export it to Excel with formulas intact. This foundation opens up a much broader set of workflows. A few examples: agents can update models in parallel, incorporate new guidance into forecasts as an earnings call unfolds, and compare commentary with consensus across your coverage. Here's a first look at building a model from scratch and updating it after earnings. More soon.

A new 200-page 10-Q comes in. You ask: “What changed vs. last quarter?” Does the agent run out of context? If it breaks the filings into smaller pieces, does it lose context across them? Even if everything fits, how do you know it caught every important change, and how do you verify it? Giving an AI agent SEC filings is easy. Making a common investment workflow accurate, complete, and auditable takes a lot more than that. Implied built custom tools to align filings by section and process text, tables, and images separately. We also built custom visuals where every citation opens directly into a redline view, so investors can see the takeaway and the underlying change together. Building AI for investors is not just about asking an agent to read raw documents. It means building a system with high-quality processed data, custom tooling, and the right visual design so the analysis is complete and accurate, and every result is auditable.

A convincing AI answer isn’t proof that the research was done thoroughly. Implied handles this by using our proprietary AI-native spreadsheet as an intermediate research step for complex, multi-step processes. It 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!

Ever feel like you’re already behind before the market even opens? What if you could wake up knowing what changed, arrive with the research already underway, and follow your curiosity all the way to conviction? See what a day on Implied looks like.

We’re in the thick of earnings season. It doesn’t have to be chaotic. See what earnings can look like with Implied.