Most asset managers treat AI as just a chatbot, failing to bridge the gap between an LLM's general reasoning and the specific, high-stakes workflows of their actual day-to-day.

In this episode of The Hedgineer Podcast, Michael Watson sits down with Jhanvi Virani, COO of Hedgineer, to discuss the practical mechanics of deploying AI within hedge funds and asset managers. Jhanvi details her experience shadowing a CIO to translate their cognitive investment process into a digital skill—a structured framework that allows Claude to synthesize fragmented data from order management systems, SharePoint research, and consensus estimates into polished, institutional-grade outputs in a one-day turnaround. We move beyond simple prompting to explore the "Agentic Loop," discussing how local schedulers and the Claude Agent SDK are enabling systems to run autonomously 24/7.

The conversation also covers the technical nuances of the Claude Ecosystem, comparing developer-centric Claude Code with user-friendly Claude Cowork. Jhanvi shares her on-the-ground findings regarding the limitations of local vs. remote execution and why building a secure, server-side environment is the ultimate bottleneck for scaling AI intelligence across a firm.

• The Skill-Based Unlock: How shadowing investment professionals allows engineers to map complex and manual research workflows into automated skills that produce consistent, high-polish one-pagers.
• Claude Code vs. Cowork: A breakdown of why developers prefer terminal-based workflows for multitasking, while non-technical users leverage Cowork for scheduled tasks and visual connector management.
• Building "AI Native" Infrastructure: The 0-to-1 process of auditing fund workflows, building custom MCP (Model Context Protocol) connectors for legacy data vendors, and establishing organizational agent management frameworks.
• The Self-Healing Feedback Loop: Using usage analytics and "meta-agents" to observe behavior, evaluate performance, and automatically suggest system improvements, creating a self-sufficient AI framework.

00:00 - Introduction and the role of skills in unlocking automation

04:15 - Evolving daily workflows with Claude Code and Cowork

08:42 - UI vs. Terminal: Optimizing screen real estate and parallel sessions

14:30 - Testing the bounds: Automating expense reports and attachment limitations

17:45 - Windows vs. Linux runtimes and the "Local Scheduler" in Cowork

22:10 - The Agentic Loop: From Claude Agent SDK to OpenClaw deployments

29:40 - CIO Shadowing: Translating a day of research into a custom AI skill

36:50 - The future of autonomous analytics and observation agents

43:15 - Deliverables for becoming AI Native: Audits, MCP servers, and data warehouses

51:00 - AI Personification: Authenticity in communication and the risk of "AI slop."

64:20 - Team expansion in Bangalore and the tech-focus of South India

Guest Bio: Jhanvi Virani is the COO of Hedgineer, where she oversees the deployment of AI infrastructure and automation for institutional asset managers. She specializes in bridging the gap between technical LLM capabilities and high-level investment workflows.

Host Bio: Michael Watson is the founder of Hedgineer and host of the podcast, focusing on the intersection of data science, AI, and hedge fund technology.

Links & Subscribe

Subscribe for weekly analysis on AI and Asset Management.

youtube.com/@hedgineer

Hedgineer.io

Hosted on Acast. See acast.com/privacy for more information.

The traditional model of purchasing financial data is structurally misaligned with the requirements of modern AI development. While hedge funds have historically navigated opaque pricing and rigid, six-figure bulk contracts, the rise of Frontier Labs and agentic workflows demands a shift toward data liquidity and consumption-based procurement.

In this episode, Michael Watson is joined by Dan Entrup (Founder of Agnowledge) and Freeman Lewin (Founder of BrickRoad) to bridge the gap between institutional data strategy and the emerging ML data marketplace. The conversation explores why the "data-centric AI" movement is forcing a reimagining of the supply pipeline, moving away from "buying data to cover your tracks" toward a world where agents autonomously discover, score, and purchase granular datasets for real-time inference.

We analyze the friction within current procurement cycles—often involving over 80 emails for a single deal—and contrast this with the "vibe coding" revolution and the Anthropic "skills" ecosystem. By treating expertise as a distributable text-based asset, firms can bypass traditional SaaS moats and build opinionated, autonomous systems that scale far beyond the capacity of human analyst teams.

• The Shift to Consumption-Based Data: Moving away from bulk annual minimums to consumption models allows firms to trial, backtest, and identify ROI within minutes rather than months, effectively creating a "spot market" for information.
• Agents as the New Data Buyers: Unlike humans, agents require high-frequency access to small data subsets for accuracy. This creates a need for automated marketplaces where data "sells itself" to machines to maintain trust in agentic outputs.
• Skills as Monetizable Data: Anthropic’s Model Context Protocol (MCP) and "skills" framework represent a shift where organizational knowledge—such as specific financial modeling styles—becomes a portable, executable asset that can be distributed via marketplaces.
• The Decline of Legacy SaaS Moats: Software companies that rely on workflow inefficiencies or "proprietary" data that is actually generally available are facing significant valuation pressure as "vibe coding" allows firms to build custom, internal alternatives like CRMs overnight.
• 
  

00:00 - Introduction to Dan Entrup and Freeman Lewin. 08:45 - The bifurcation of the data industry: Hedge funds vs. Frontier AI Labs. 15:20 - Friction in data procurement: Why it takes 80+ emails to close a deal. 23:10 - Data-centric AI: Why better data now moves the needle more than algorithmic tweaks. 32:45 - Token optimization vs. Weight fine-tuning for enterprise value. 42:15 - Building the Agentic Marketplace: Why data doesn't sell itself to humans. 54:30 - The "SaaS is Dead" debate and the transition to consumption-based revenue. 79:00 - Anthropic Skills: Structuring and distributing expert knowledge at runtime. 98:30 - Vibe coding and the future of the autonomous, multi-billion dollar "small" firm.

Dan Entrup is the Founder of Agnowledge and a veteran data strategist who previously served as Head of Data Strategy for a Fortune 500 company. He specializes in expert network curation and helping firms navigate the complexities of data commerce.

Freeman Lewin is the Founder of BrickRoad, a frontier data lab building an agentic marketplace for data procurement and liquidity. His work focuses on establishing data liquidity through on-chain transaction histories and utility scoring mechanisms.

Michael Watson is the host of The Hedgineer Podcast and founder of Hedgineer, a firm building data and AI platforms for institutional asset managers.

• Subscribe for weekly analysis on AI and data infrastructure in finance.
• Learn more about Hedgineer: Hedgineer.io
• Follow on LinkedIn: https://www.linkedin.com/company/90976838

Hosted on Acast. See acast.com/privacy for more information.

Welcome back to The Hedgineer Podcast, where host Michael Watson dives into the world of AI, data, and technology within asset management, hedge funds, and financial services. In this episode, Michael sits down with Jonathan Regenstein, who leads AI within Financial Services at Snowflake.

This conversation explores the critical role of data and platform strategy in the successful enterprise deployment of AI, moving beyond purely technical wins to focus on commercial outcomes. Jonathan and Michael dissect the evolution of Snowflake from a powerful SQL engine to a unified platform for AI, and debate where the intelligence layer should reside for maximum effectiveness.

• The Power of Data Sharing: How Snowflake's seamless data sharing and Marketplace revolutionized the consumption of alternative data on the buy side, drastically simplifying security and licensing workflows.
• The AI Layer Debate: A deep dive into whether the AI runtime should live natively within the data platform (Snowflake) using tools like Cortex and Intelligence, or be orchestrated externally by hyperscalers or model providers.
• Beyond the Technical Win: The shift from technology-driven AI Proofs-of-Concept (POCs) to projects scoped by commercial outcomes—revenue generation or cost reduction.
• Evaluations are the Product: The crucial importance of robust evaluation frameworks (like those provided by TruEra/TruLens) for agentic workflows to avoid "chaos at scale," and how to involve business leaders—not just engineers—in defining what success looks like.
• The Semantic Layer's Role: The concept of the semantic model as a first-class citizen in Snowflake, acting as the translator between business language and data, driving accuracy in Text-to-SQL (Cortex Analyst), and building trust with non-technical users.
• The Future of BI: How AI is driving the complete rethinking of the Business Intelligence (BI) stack, moving beyond static dashboards to dynamic, generative BI that surfaces insights and visualizations on demand.

Jonathan Regenstein is a key leader in the AI for Financial Services division at Snowflake, driving the platform's strategy in machine learning and artificial intelligence for banks, asset managers, and insurance companies.

Follow The Hedgineer Podcast:

YouTube: (https://www.youtube.com/@hedgineer)

LinkedIn: (https://www.linkedin.com/company/90976838)

Twitter: (https://x.com/hedgineering)

Instagram: (https://www.instagram.com/hedgineer/)

Don't forget to like, subscribe, and hit the notification bell to stay updated on our latest episodes!

Hedgineer.io

Hosted on Acast. See acast.com/privacy for more information.