Limeglass: The Enabler of All Discovery Solutions
Limeglass breaks Research Discovery down into three major categories based on existing and expected future use by clients: Active Discovery, Passive Discovery, and Analytics.
Active Discovery is where the investor clients, or Salespeople on their behalf, are able to find specific Research insights for their investment processes. This can be done in many ways, the most powerful of which is through Smart Search or dedicated browsing systems like the Limeglass Portal. Searching heterogeneous, complex content like Investment Research is done very poorly across the industry at the moment, but doing it well can unlock enormous value.
We will write more about this in our next article where Limeglass CTO Simon Gregory delves into the technological challenges in this space, warns that Large Language Models are only a small part of the solution, and introduces the proprietary search solution that Limeglass has developed.
Passive Discovery is the opposite, where Research consumers are provided with tools capable of showing them or alerting them to content they did not necessarily know they needed to read.
This has long been an unmet need for the consumers of research. The producers tend to have distribution systems that make it difficult to target the correct content at the correct consumers. This is one of the main reasons that so much research languishes unread in people’s inboxes.
For example, at the moment it is easy for the Sell Side to set up distribution lists for a certain analyst’s reports, or for a certain asset class or sector, but it is extremely difficult for them to identify and distribute documents relevant to broad themes like Artificial Intelligence or the Energy Transition.
Analytics is the final piece. We will discuss later how content can only be truly discoverable when it is turned into rich data. With this rich data, Quants can analyse previously inaccessible trends buried in the text, the Sell Side can optimise complex research workflows, and the Buy Side can assess the relative value of the content it is paying for.
Ultimately, there is no limit to how this data can be used, but certain emerging use cases in this space are gaining traction: Quant teams are looking at the co-occurrence of certain ideas within the research, inferring interesting hypotheses about the informational value of Sell Side content; Sell Side management teams can compare readership data for their documents versus the rich tag data and see where they should be investing more or less heavily in their research coverage; the Sell Side is also improving complex manual workflows like the Supervisory Analyst and Compliance reviews of research; and the Buy Side can get a granular view of exactly what their providers are writing about and which providers best fit their requirements.