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POC - AI Knowledge Agent for Financial Research

Objective

The purpose of this POC is to demonstrate how an AI Knowledge Agent can support financial research workflows by simplifying complex tasks, automating data extraction, and providing intelligent insights through a conversational interface.

Our aim is to showcase how the solution can:

  • Reduce time spent on repetitive research activities

  • Improve accuracy and consistency of outputs

  • Empower teams with faster decision-making tools

Scope of the POC

This POC focuses on a selected set of core workflows that are most relevant to research analysts and business users:
 

Submit New Research Request

  • Capture structured inputs and supporting files

  • Assign and track requests in real time
     

Research Workstation

  • Enter natural language prompts

  • Attach documents and extract financial metrics

  • Monitor progress with AI transparency indicators
     

Dashboard & Tracking

  • Quick view of requests submitted, in-progress, and completed

  • Access to historical outputs and research archives
     

AI-Powered Insights (e.g.)

  • Summarize earnings reports

  • Compare market share trends

  • Export deliverables in Excel, Word, PPT, or PDF

Approach

Phase 1 – Wireframes & Ideation

Initial low-fidelity wireframes were created to outline user journeys and navigation

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Phase 2 – Prototype Development

Interactive prototypes were designed to validate core workflows

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Phase 3 – UI & Experience Design

High-fidelity screens were developed with a clean, professional aesthetic

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Key Features in the POC

Conversational AI Interface:
Natural language interaction for ease of use
 

Structured Request Workflows:
For consistent input and faster analyst assignment
 

AI Transparency:
Progress indicators showing task reasoning behind the scenes
 

Multi-Format Export:
Reports downloadable in Excel, Word, PPT, and PDF
 

Quick Links & Data Sources:
One-click access to archives, external subscriptions, and templates

Expected Benefits

  • Efficiency:
    Reduced research turnaround time by automating repetitive tasks
     

  • Accuracy:
    Standardized templates and AI-assisted validation reduce errors
     

  • Scalability:
    Capable of handling large volumes of requests with minimal manual effort
     

  • User Empowerment:
    Analysts and requesters can focus on higher-value decision-making instead of manual data gathering

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Next Steps

  • Run the POC with a selected group of pilot users (Research Analysts & Requestors)

  • Capture feedback on usability, output quality, and workflow integration

  • Measure time saved vs. traditional workflows

  • Finalize roadmap for scaling to additional user groups (QA Analysts, Platform Support)

Outcome of the POC

This Proof of Concept demonstrates that the AI Knowledge Agent can function as a trusted digital teammate,
making research faster, smarter, and more reliable while ensuring compliance and transparency.

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