Why this problem matters

Within the Investment Hub ecosystem, data is technically centralized — but in practice, research workflows remain fragmented.

Investment specialists:

• switch between multiple systems
• manually compare products
• struggle to respond during live client conversations

As a result, time is spent gathering data instead of making confident decisions., limiting both efficiency and advisory quality.

Key Insight

The real problem is not access to data, but the lack of a structured decision-making flow.

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This reframed the opportunity:

• Not another search tool
• Not a generic AI assistant

But a decision support system embedded in advisory workflows

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Design Challenge

We reframed the problem into three core challenges:

1️⃣ From fragmented tools → unified decision flow

Users needed to move seamlessly from exploration to recommendation without switching systems.

2️⃣ From raw data → structured insights

Outputs had to be comparable, explainable, and ready for client conversations.

3️⃣ From generic AI → context-aware intelligence

Recommendations must reflect:

Currently:

• CIO house views
• compliance constraints

Designed to incorporate:

• client profile (risk, preferences)
• portfolio context (holdings, allocation)

Solution

Designing a decision system (not just chat)

Although the interface is conversational, the system was designed as a decision support layer.