The problem
Buy-side analysts need to screen, model, and generate thesis across filings, prints, and alt-data — at a velocity rules-based screens can't match.
FINANCE · CASE STUDY
2024–26Quasar Markets — Institutional AI Research
AI-powered research workbench fusing quantitative market signals with LLM-driven analysis for the buy side.
Summary
Quasar Markets is a finance engagement running 2024–26. AI-powered research workbench fusing quantitative market signals with LLM-driven analysis for the buy side. Production research workflow across multiple buy-side desks.
Facts
- Client
- Quasar Markets
- Sector
- Finance
- Dates
- 2024–26
- Key result
- Multi-source signal fusion for the buy side
- Site
- quasarmarkets.com
- Engagement
- Discovery sprint → build alongside → hand-off
The architecture
Streaming pipelines ingest filings, prints, and alt-data into a unified research substrate. LLM layer fuses signals with quantitative models inside an analyst workbench.
- 01Ingeststreaming pipelines pull filings, prints, and alt-data into one research substrate.
- 02Signal layerquantitative models score the substrate continuously rather than on a screen refresh.
- 03LLM layerfuses those signals into thesis-level analysis, each claim traceable to its source document.
- 04Workbenchanalysts screen, model, and draft in one surface instead of three.
The results
Production research workflow across multiple buy-side desks.
- Signal fusion
- Multi-sourceFilings, prints, and alt-data in a single substrate
- Ingest to workbench
- StreamingResearch updates as documents land, not on a nightly batch
- In production
- Multiple desksRunning as the research workflow across buy-side desks
“Analysts did not need another screen. They needed the screen, the model, and the draft to sit on the same substrate.”
Written by Binary AI Labs · Reviewed