About the Client
Amansa Capital is a leading Asia-focused investment firm known for its long-term, research-driven investment approach across public and private markets. With a strong presence across key financial hubs including Singapore, India, and the United States, the firm manages capital for global institutional investors, including sovereign funds, pension funds, and endowments.  Amansa Capital is recognized for its disciplined investment philosophy, deep sectoral research, and ability to identify high-quality growth businesses across emerging and developed markets, positioning itself as a trusted partner in long-term wealth creation.
Workshop Objective
The objective of this program was to strengthen analysts’ capability to integrate AI into core investment workflows, enabling faster, more structured, and insight-driven financial decision-making.
Key focus areas included:
- Leveraging AI-assisted financial modelling for quarterly analysis, scenario planning, and forecasting
- Integrating AI agents with financial intelligence platforms such as CapIQ and AlphaSense for enhanced research workflows
- Building AI-driven approaches for financial research, including data synthesis, signal identification, and trend analysis
- Designing hybrid architectures combining private data with public LLMs to ensure secure and scalable investment insights
- Developing an AI layer that integrates proprietary intelligence with public market data for decision-ready outputs
Workshop Summary
This 3-hour virtual workshop was delivered as a focused, application-oriented session for investment analysts, emphasizing the practical integration of AI into real-world investment workflows.  The session combined structured concept walkthroughs with live demonstrations, guided exercises, and use-case-based discussions tailored to financial research and analysis.  The learning approach was designed to bridge the gap between AI capabilities and investment decision-making, ensuring participants could directly apply the frameworks in their day-to-day analysis.
Key Highlights:
- Hands-on exercises on AI-assisted financial modelling, including forecasting and sensitivity analysis scenarios
- Live demonstrations of integrating AI agents with platforms such as CapIQ and AlphaSense for research acceleration
- Practical application of AI frameworks to convert unstructured data into structured investment insights
- Simulation of AI-driven research workflows combining proprietary datasets with public information sources
- Exposure to real-world AI adoption trends across global investment firms and their impact on analyst productivity
- Structured approach to building AI-enabled research pipelines for faster decision cycles
- Practical guidelines on governance, including risk classification, human override mechanisms, and escalation protocols
- Actionable takeaways on designing AI layers that enhance, rather than replace, analyst judgment
Workshop Details
- Mode: Virtual / Online
- Audience: Investment Analysts
- Batch Size: 12 Participants
- Duration: 3 Hours (1-Day Workshop)
- Customized Training Modules
- Certificates for all participants
Trainer (Speaker) Profile
 A seasoned digital transformation and AI expert with over 23 years of experience across enterprise technology, platform engineering, and advanced analytics.
- Extensive experience in Digital Strategy & Architecture, Artificial Intelligence, and Enterprise SaaS ecosystems
- Deep expertise in designing scalable AI solutions, including agent-based architectures and data-driven decision systems
- Has worked across industries including financial services, technology, and large-scale enterprise environments
- Proven track record in building and deploying AI-enabled platforms that integrate proprietary and public data sources
- Specializes in translating complex AI capabilities into practical, business-aligned solutions for decision-making workflows
- Known for delivering high-impact, application-focused sessions that enable organizations to operationalize AI at scale
His ability to connect AI architecture with real-world financial use cases makes him highly relevant for investment teams seeking to enhance research depth, speed, and decision accuracy through AI.