Innovation often starts with frustration. Our platform provided vast amounts of news and social media data, but it was not always easy to navigate without a specific asset or location in mind. I often found myself scrolling through endless lists of articles just to get a sense of what was happening, and when I found something interesting, it was difficult to understand how it fit into the larger picture. Knowing that we had data that could be used to answer my questions, I was frustrated that I wasn’t able to get the information I was seeking.
The thesis that inspired Seerist was to combine the speed and scale of AI-processed news and social media with the contextual knowledge provided by expert-written analysis. But there was a workflow gap that prevented this vision from coming to life for me. A feed of global news wasn’t helping me discover important events and then finding the relevant analysis that could help me understand the event was difficult. My frustration led me to create a prototype of a tool that aimed to automatically surface the most reported stories from the incoming stream of news and social media and instantly connect them to our human analysis. This prototype solved my frustrations quite well, and so I pitched it as an idea we could continue to develop.
Early internal demos secured buy-in and proved valuable in client conversations, confirming the potential of the prototype. The initial prototype solved most of my personal pain points, but the feedback we received during this phase was critical in providing analyst voices to help shape the tool’s growth into something that is valuable to not just me, but our users. This stage transformed my prototype into what we now call DiscoverAI.
DiscoverAI’s main purpose is to help support the analyst-focused workflow of being able to quickly identify emerging incidents, understand the implications of the event, and then communicate their findings. DiscoverAI streamlines this process, reducing the manual lift needed to move from discovery to decision-making. The following sections illustrate how each of these steps is realized in the platform today:
1. Identify
The first step to understanding a threat is knowing it exists.
By analyzing Seerist’s collections pipeline at a macro level, DiscoverAI helps pinpoint which existing stories are gaining attention, whether reporting volume is accelerating or new storylines are emerging. Analysts receive a deduplicated, relevance-driven feed, making it simple to spot events worth investigating without sifting through repetitive or low-value content. The intention is to help analysts perform horizon scanning by identifying emerging threats before they become critical incidents.
2. Understand
After a storyline of interest is identified on DiscoverAI, we want to provide analysts with the tools to quickly understand what is going on and how it may impact their business.
A single click brings the user to a dashboard dedicated to exploring Seerist’s related content. This integration of related content brings to life Seerist’s founding vision of combining robust human analysis with the scale of AI-processed news and social media. Each data type provided by Seerist can help analysts understand unique elements to the selected event:
Near-Real Time AI Insights
- AI Situational Report: A single document that distills the EventsAI feed of related content into a concise bullet-point briefing. Each item prioritizes sources assessed by Seerist as “high-reliability” and includes citations for tracing information back to its origin. This document saves analysts’ time by reducing the need to read repetitive source material and then produce their own report.
- Recent Article Feed: A feed of the latest EventsAI related to the story is provided without the need to create any search queries. This enables the analyst to monitor the most recent information that is being published.
- Interactive Timeline: As time goes on, stories evolve and so does the reporting of them. Analysts can use the timeline visualization to see these changes at a high-level over the last 60 days and zoom into the reporting of specific time ranges. This enables them to quickly see if the current reporting is a part of a larger trend or just a one-off event.
Human-Generated Content
- Context & Analysis: Control Risks’ expert analysis provides crucial context into understanding an event, why it is important, how it may impact industries, and where the story might go next.
- Forward-Looking Insight: The Future Events section surfaces anticipated developments tied to the story so analysts can have foresight into where the story is headed.
- Verification: Seerist’s Verified Events deliver human-curated, source-confirmed updates.
By consolidating Seerist data into one view, DiscoverAI frees analysts from building queries and sifting through noise. Instead, they can focus on understanding the event and make important decisions based on what they’ve learned.
3. Communicate Findings
Once an analyst has done their research, it is essential to be able to quickly and succinctly communicate what emerging threat has been identified and why it matters.
The AI Situational Report, DiscoverAI’s newest capability, transforms our data into stakeholder-ready reporting in seconds. Designed for speed and accuracy, it compiles a clear, concise narrative from high-reliability sources identified through Seerist’s source metadata tags. Every statement includes citations, giving analysts an immediate way to verify and trace information back to its origin. With a single click, analysts can move from understanding an event to delivering well-sourced, actionable insights that are ready to be shared across teams or with decision-makers.
Last month the Head of Intelligence at Seerist, Melissa Newberg, highlighted in her blog The Tradecraft Problem with AI Tools the importance of “analyst-forward” tools built around transparency, control, and workflow integration. While much of the discussion in this post has focused on the workflow integration component, DiscoverAI expresses all of these ideas. It shows its work through traceable, reliability-tagged sourcing. It keeps analysts in the driver’s seat by letting them decide what storylines to explore, what data to emphasize, and how to shape the resulting outputs. And it integrates into the way analysts already operate, helping them to identify a threat, understand its implications, and then communicate their findings. DiscoverAI isn’t an AI tool that asks analysts to adapt to it; it’s an AI capability built to adapt to analysts, ensuring they can maintain their credibility and speed.
As we continue to evolve DiscoverAI, our focus remains the same: amplifying analyst expertise through transparent, controllable, and integrated AI.
