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How a Multi-Billion-Dollar Hedge Fund Uses AI Agents to Enhance Client Relations and Sharpen Decision-Making

How a Multi-Billion-Dollar Hedge Fund Uses AI Agents to Enhance Client Relations and Sharpen Decision-Making

How a Multi-Billion-Dollar Hedge Fund Uses AI Agents to Enhance Client Relations and Sharpen Decision-Making

Discover how a multi-billion-dollar hedge fund uses secure AI agents to automate research, client coverage, and FX signal discovery, boosting speed and insight without sacrificing control.

Discover how a multi-billion-dollar hedge fund uses secure AI agents to automate research, client coverage, and FX signal discovery, boosting speed and insight without sacrificing control.

Discover how a multi-billion-dollar hedge fund uses secure AI agents to automate research, client coverage, and FX signal discovery, boosting speed and insight without sacrificing control.

Client

Multi-Billion Dollar Hedge Fund

Challenge

Billions in institutional knowledge and market data were trapped across systems, forcing analysts to rely on manual searching, reactive processes, and time-intensive research.

Solution

StackAI deployed secure, access-controlled AI agents that surface institutional insights, automate investor intelligence, and detect FX signals.

Overview 

A global hedge fund managing ~$8–10B AUM partnered with StackAI to accelerate investment research, strengthen client coverage, and improve FX signal discovery.

Before StackAI, the fund faced a familiar problem at scale. Investment analysts were losing time to manual document search, version-tracking, and repetitive drafting for investment memos. Client relationship teams lacked a consistently up-to-date dashboard of “which accounts need attention today and why,” leading to reactive outreach. Meanwhile, macro analysts were trawling FX sources manually, often missing early-stage dislocations simply because there was too much noise and not enough consistent screening capacity.

The firm’s mandate was clear: speed up the work analysts already do, without compromising compliance, identity controls, or auditability. In less than eight weeks, the firm deployed three production-grade AI agents across investment research, IR/CRM operations, and macro signal generation.

The results? A chat assistant running over decades worth of IC memos, consistent daily coverage of priority client accounts, and systematic currency-arbitrage signal detection.

  • Analysts report 65–80% less time spent combing through memos

  • Client teams recaptured 3–5 hours per week per RM

  • Macro desk now evaluates 25–40 high-quality FX signals each week with structured evidence

IC Memo Assistant: Institutional Memory on Demand

The Problem: Decades of Knowledge Locked Across Systems

The firm’s investment process relies heavily on historical IC memos, deep domain notes, and internal research archives stretching back more than a decade. Analysts frequently needed to reference prior cases, precedent risk decisions, thematic perspectives, or past market environments, but this information lived across SharePoint folders, legacy systems, and email archives.

Realistically, only senior PMs “remembered where the bodies were buried.” Newer analysts spent hours digging for materials — or worse, made decisions without full historical context.

The Solution: Role-Aware IC Research Agent

The team deployed a secure IC reference agent that surfaces institutional knowledge contextually and only when the user has access rights. Analysts authenticate through SharePoint; the agent searches only approved folders and strictly refuses to hallucinate, answering “Not in the record” when data isn’t present.

It retrieves past cases, citations, and commentary across cycles, allowing analysts to anchor decisions in historical precedent in seconds, not hours. End user connection check is enabled, meaning that users must have access to the knowledge base through their SharePoint account to receive an answer from the agent.

This agent has sped up recall of institutional knowledge, enforced grounded decision-making, and set a consistent research standard across both tenured and newer team members.

CRM Intelligence Agent: Daily Actionable IR Signals

The Problem: Manual Account Scanning & Reactive Client Engagement

Relationship managers lacked a reliable, proactive system for identifying priority accounts needing attention. Daily CRM checks were inconsistent, news monitoring was ad-hoc, and updates were pulled manually, leading to delayed engagement and potential missed opportunities.

The Solution: Automated 6AM Portfolio and News Digest

Every morning, the agent queries HubSpot, scans priority accounts, identifies stale coverage, evaluates engagement history, and enriches this with real-time news signals via automated web search. It then produces a clean briefing with:

• Accounts requiring touchpoints

• Recent account activity and CRM signals

• Relevant market/news triggers

• Suggested next steps and recommended messages

Briefings are sent automatically to the IR channel via email at 6:00 AM ET daily. This agent has resulted in consistent proactive coverage rhythm, 3–5 hours/week returned per RM, and increased activity on key accounts without additional headcount.

Currency Arbitrage Research Agent: Systematic FX Signal Engine

The Problem: FX Signal Hunting Was Manual, Constant, and Easy to Miss

The macro desk previously monitored FX markets manually, jumping between research terminals, central bank sites, macro feeds, and incoming news to spot early arbitrage signals. Analysts often described the work as “constant scanning plus intuition.” Signals were missed simply because humans can’t watch everything 24/7.

The Solution: Autonomous Morning FX Scan & Signal Table

The Currency Arbitrage Research agent now crawls curated FX news sources, central-bank communications, policy calendars, and selected market-intel pages using browser + Firecrawl tooling. It cross-references findings with a SharePoint KB of known arbitrage patterns and produces:

• Structured FX signals with relevance scores

• URLs and evidence context

• Macro commentary and regime context

• Tabular outputs analysts can query interactively

It runs daily at 6AM ET and emails a signal digest. Within the agent, analysts can chat with an interactive table. This means 25–40 credible FX signals/week surfaced with structured evidence, freeing analysts from low-value scanning and focusing time on interpretation & execution.

Currency Arbitrage Research Agent: Systematic FX Signal Engine

The Problem: FX Signal Hunting Was Manual, Constant, and Easy to Miss

The macro desk previously monitored FX markets manually, jumping between research terminals, central bank sites, macro feeds, and incoming news to spot early arbitrage signals. Analysts often described the work as “constant scanning plus intuition.” Signals were missed simply because humans can’t watch everything 24/7.

The Solution: Autonomous Morning FX Scan & Signal Table

The Currency Arbitrage Research agent now crawls curated FX news sources, central-bank communications, policy calendars, and selected market-intel pages using browser + Firecrawl tooling. It cross-references findings with a SharePoint KB of known arbitrage patterns and produces:

• Structured FX signals with relevance scores

• URLs and evidence context

• Macro commentary and regime context

• Tabular outputs analysts can query interactively

It runs daily at 6AM ET and emails a signal digest. Within the agent, analysts can chat with an interactive table. This means 25–40 credible FX signals/week surfaced with structured evidence, freeing analysts from low-value scanning and focusing time on interpretation & execution.

What's Next?

By combining structured automation with targeted reasoning, this hedge fund transformed three core research and client-coverage workflows in under eight weeks. Instead of attempting fully autonomous decision-making, the firm focused on institutional leverage, codifying historical knowledge, systematizing client engagement, and scaling early-signal discovery with tight access controls and zero-hallucination guardrails. The result is a research organization that moves faster, sees further, and operates with deeper context — not by replacing analysts, but by amplifying their judgment and freeing them from manual, repetitive work. In a market where speed and rigor compound, these agentic workflows enable the fund to stay ahead without compromising security, compliance, or investment discipline.

Want to see how StackAI can help your enterprise save thousands of hours per year? Get a demo here.