From the first question on a company to the decision maker's answer.
Six steps, one graph. Read the reasoning behind each step, then watch it work.
Try GraphCallThe typical workload of finding data that would take days for your intern or entry-level analyst to gather, based on a list of questions and previous case examples.
AI-Graphs are supercharged structures that look like mind-maps. They organize due diligence questions and analysis in logical steps, and they can also take decisions.
They are extremely easy to customize. Use the ones preloaded in your account, create new ones, or ask for a consulting service and we build it for you.
The ground work is done, but you need additional information. That is why there are always follow-up questions.
You ask them inside the same graph, which can be edited fully automatically or semi-automatically. Select any node and ask. The graph expands with new, structured intelligence.
AI can help in several parts of the process. The standardized ground work is typically done by interns and entry-level analysts. The follow-up questions are typically asked by more experienced analysts. A crucial part is the brainstorm.
The brainstorm is the most difficult part for a buy-side analyst. It is like solving a geometry problem by drawing an additional point that lets you demonstrate what you need to demonstrate. It is called triangulation.
It means reframing the data, such as revenues per car or per watt, or finding triangulation points for potential revenue increases. These put things in perspective far better than moving from 3% growth to 4%. If your triangulation shows that revenues can be multiple times higher, that can be far more coherent than a 4% growth that “seems relatively high”.
LLM chats with tools like Google AI are fantastic for finding triangulation data, but you need to join that data to your investigation case.
In one click, GraphCall creates a general summary and organizes the topics discussed in sequence, so you don't have to scroll indefinitely. Each part is referenced, so one click reopens the chat at the exact position of the source.
No more infinite scrolls and unconnected chats. Each chat is now neatly organized and can be branched seamlessly into other chats.
There are always pieces of data “on the go”: a page you browse, a chart you receive on WhatsApp, an interesting video from a key macro investor. Your data used to be scattered, often floating in your head, with the risk of losing key information.
Now it goes into the same graph, ready to be part of the analysis.
Add any type of content element to the Graph in a few clicks.
Add a PDF and comment on it seamlessly in our unique augmented media format, a video and PDF hybrid.
Need to comment on sections of a PDF and add them to the Graph? GraphCall converts your PDF into an HTML page, ready for extraction. Selected text becomes a bubble in the Graph, with a reference back to its source.
Technical note: the conversion uses PDF.js.Video on play. Document on pause. Your recipient sees the actual data and documents, not pixels on pause.
GraphCall combines a comprehensive stack of AI and human data gathering and analysis with an unbelievably faster way to report your findings and discuss them.
Old hands of analysis remember how they would copy and paste pieces from the internet into a document as the source, then write a summary in a hurry because markets go fast.
GraphCall goes far beyond that.
The analysis has been received. Now the decision makers want to opine, have particular questions on some data they received, and need clarifications on specific data points.
With GraphCall reply, they don't need to schedule a meeting. They don't need to write a long email explaining the context of what they want clarified, like:
Can you please, on page 4, tell us what this item in the expenses means?
In the second part of the chart I saw that the trend inverted. Would you mind explaining why?
Those questions exist because of missing context. A video box layered on top of the live PDF or the data bubble gives it back: the decision maker points at the item and asks, in the same graph.
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