IVP is a leading financial services firm specializing in investment data analytics, risk management, and regulatory reporting. The company is focused on leveraging cutting-edge technology to enhance data accessibility and decision-making across its teams.
The problem: fragmented data, long wait times, and overreliance on engineering
IVP customers’ teams faced a significant bottleneck in interpreting the available data. They relied on technical experts to run queries and extract insights from petabytes of archive and streaming data. This dependency slowed down strategic decision-making and introduced operational inefficiencies across client organizations, a common challenge in AI in hedge fund operations. With critical data sources like MySQL and Snowflake operating in silos, there was no unified access layer to retrieve insights efficiently.
Non-technical users lacked a self-service interface, limiting their ability to independently explore or query data. Moreover, without transparency into how SQL responses were generated, trust and explainability were compromised.
The solution: a Q&A system powered by AI for hedge funds
GoML worked with IVP to implement a secure, AI-powered Q&A system that integrated enterprise databases with a natural language chat interface. Built with role-based access and Microsoft Teams integration, the platform made enterprise data easily accessible across business and technical teams, redefining the application of AI in hedge fund software.
Seamless access to live enterprise data
GoML securely connected IVP’s data warehouse (MySQL + Snowflake) with an AI backend powered by Claude 3.5 Sonnet, enabling fast, structured responses to natural language questions.
- Real-time AI-driven SQL generation
- Data table outputs, explanations, and visualizations
- Tailored responses for different user roles
- Context-aware answers for different personas
From descriptive summaries for leadership to SQL and graphs for analysts, the system adapts based on user roles, empowering teams with smarter, faster decision-making through AI for hedge funds.
Context-aware answers for different personas
From descriptive summaries for leadership to SQL and graphs for analysts, the system adapts based on user roles.
- Executive users see visual summaries and business metrics
- Data teams get SQL output and enriched schema info
- Chat UI built for seamless UX
Built for daily workflows and secure collaboration
The system integrates with Microsoft Teams, providing persistent chat history, secure access, and adaptive card-based visualizations.
- Chat history stored in AWS MemoryDB
- Bot built with Azure Bot Services for Teams integration
- Access controlled using role-based filters and metadata permissions
The impact: faster access, smarter collaboration, happier teams
The AI-powered Q&A system radically improved IVP customers’ ability to extract insights across functions, reducing dependency on technical teams and enabling faster, more confident decision-making, a model example of how AI in hedge funds can deliver real results.
Importantly, decision-making executives were no longer dependent on analysts to interpret complex data. With speed came alpha.
- 90% improved user adoption through intuitive chat-based access
- 70% increase in team collaboration via Microsoft Teams integration
- 85% reduction in turnaround time for data requests
Lessons for hedge fund software teams building Gen AI tools
Common pitfalls to avoid:
- Building a generic chatbot without deep data integration
- Ignoring the need for role-based access and explainability
- Failing to embed AI into existing collaboration workflows
Advice for data-driven enterprises:
- Start with one high-impact domain (e.g. investment reporting or regulatory queries)
- Use metadata enrichment to drive role-based insights
- Choose AI partners that understand both your data architecture and business workflows
Get rapid, AI-driven financial insights to accelerate your team's decision-making.
Let GoML help you build a secure, scalable, and intelligent Q&A system, just like we did for IVP.
