A $1 billion AUM hedge fund managing approximately 200 public equities ran an analyst workflow that was bleeding time. Before each corporate access call, analysts spent 30 to 60 minutes per equity manually pulling earnings transcripts, 10-Qs, internal financial models, and prior meeting notes — then synthesizing it all to generate relevant questions. With 15 analysts covering hundreds of equities, the cumulative cost was enormous. The fund had no systematic way to structure pre-call research, post-call transcript analysis, or modeling implications — leaving investment decisions slower, less consistent, and limited by analyst bandwidth.
Casper Studios designed and built a three-module AI system for a $1B AUM hedge fund covering 200 public equities. The first module automated Q&A generation for corporate access calls, pulling from public filings, earnings transcripts, and internal Excel models via SharePoint and OneNote. The second ingested post-call transcripts for sentiment analysis. The third assessed whether executive statements warranted changes to the fund's financial models.
The engagement began with a workflow audit across the 15-analyst team to identify where time was being lost. Pre-call preparation — pulling filings, reviewing transcripts, cross-referencing internal models — consumed 30–60 minutes per equity. Post-call analysis and model reassessment added further load. Casper Studios scoped a three-module system targeting each stage.
Module one was built on a RAG architecture ingesting SEC filings, earnings transcripts, and internal Excel models stored in SharePoint and OneNote. Given a company and upcoming call context, it generated structured Q&A packages for analysts.
Module two processed post-call transcripts using NLP to extract sentiment signals and flag meaningful statements by management. The output was structured for downstream consumption rather than raw text.
Module three took those signals and assessed whether they were material enough to warrant changes to the fund's existing financial models — providing a first-pass impact assessment the analyst could accept, modify, or reject.
The three modules were tested individually with a pilot cohort of analysts before full rollout. Edge cases — earnings calls with limited management commentary, companies with sparse public filings — were addressed with fallback logic and analyst override capabilities built into each module.
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