Daloopa buying intent

In almost all of my meetings with customers I get asked the same question, "how are other funds using AI today, what are their plans". I figure I'd share what I have seen as the state today and level set where folks are without revealing specific firms and their process. I will group them into a few categories: 1. Large HFs, tend to have dozens if not more investment professionals. The speed at which these firms are moving is very high. The level of understanding of orchestrating agents, using skills is extremely high at the firm level. Security is a huge concern, you rarely see coding agents running wild at the analyst level but near all of them are looking at (or have) brought in LLMs into their internal use. I have not come across a large HF that hasn't thought deeply about creating agentic artifacts. There are degrees of sophistication, some of these firms have gotten so deep that they have agents reading the outputs of other agents. The use of AI is directly impacting the # of names they can cover, and the depth and speed of which you can create information. 2. Smaller HFs, measured by analyst count. This tends to have a larger dispersion, and very often driven by the people at the firm. Our fastest moving customers tend to be a newer HF building out infrastructure today and thinking AI natively across all their workflows, and integrating agentic research into trading into risk. Almost all the time these firms have a PM who is using a coding agent at home for all sorts of side projects. Conversations range from github repos to how MCP v API provide different outputs based on the context window load of each ingestion method. 3. Long-onlys, tend to be very large Procurement and security tend to be the longest here so adoption of AI tools is slower than the above 2. The driver of use tends to mostly be focused on doing existing work or increasing the scope of coverage of work. A good example is an analyst might be tasked to cover an industry, and getting deep in 100 names historically is unrealistic, so a lot of names fall into "shadow coverage". A big driver of demand is whether shadow coverage can be brought into regular coverage. The demand to build depth in coverage at the same level of a specialized analyst at a multimanager who covers 40 names is there, and AI demand is very much aligned with achieving that objective. Most firms seem to follow the following chain of adoption: Adopted an AI chatbot (ChatGPT, Claude) > adopted MCPs and connectors > adopted skills > generating artifacts across multiple data sources > orchestrating agents to use the generated artifacts (here you start to need a coding agent) > integrating these agents into risk and monitoring agents. Each step in the above move creates a huge unlock in capabilities, the earlier steps are more increases in productivity, the latter ones are about doing things that you previously couldn't do before.

MediumHow Daloopa Cut Cash Collection Times by 50% and Reclaimed Hours of Finance Time with Invoice Butler | by Mihir | Medium Sign in Write Search Sign up Sign in # How Daloopa Cut Cash Collection Times by 50% and Reclaimed Hours of Finance Time with Invoice Butler Mihir 2 min read Jul 24, 2025 https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Fp%2F8cb950d114d3&operation=