Leadership CONNECT: 03-APR-2025 (Y25W15)

By implementing a workflow-based system utilizing Claude 3.5 Sonnet alongside a programmatic workflow editor, we’ve achieved an order-of-magnitude reduction in production time. The system seamlessly synthesizes diverse inputs into structured, coherent, and high-quality investment documents for our Investment Committee.

What’s particularly noteworthy is that this represents a workflow rather than an agent approach. The process demands predictable outcomes and standardized formats—exactly the type of structured, consistent process where a workflow approach shines. An agent, with its emphasis on autonomy, would be less suitable for this task, where format adherence and predictability are paramount.

Customized Intelligence Newsletters

Another transformative implementation at 71 Capital has been our customized intelligence newsletter system. This three-stage process perfectly illustrates the evolution from basic data gathering to sophisticated AI-powered synthesis:

First, we scrape and gather data from various websites and online sources, creating a diverse pool of raw and unstructured information. Next, scheduled automated cron jobs systematically extract, structure, and store this web data in an organized database, ensuring reliable and consistent updates.

The true breakthrough comes in the final stage, where we use Large Language Models to aggregate, synthesize, and summarize this structured data into concise newsletters. This has dramatically reduced daily reading time from approximately two hours to around 10 minutes—an efficiency gain that multiplied across our entire team represents thousands of hours annually redirected toward higher-value analysis.

Again, this implementation leverages a workflow rather than an agent approach because the data sources are known and predictable, ensuring consistent outputs and format adherence. The structured automation provides the standardization and reliability our investment process demands.

Research Workbench Platform

Perhaps our most sophisticated AI implementation at 71 Capital has been our Research Workbench platform, which represents an evolution beyond traditional workflows toward what we call a “pseudo-agent” architecture. Unlike our customized newsletter system, which follows a fixed, structured process designed for standardized outputs, the Research Workbench employs a fundamentally different approach.

This pseudo-agent dynamically selects from a diverse toolkit—including a RAG Knowledge Base, internet search capabilities, and specialized analytical instruments—based on the specific query at hand. The contextual intelligence of this system allows it to navigate complex, unpredictable information requests with remarkable flexibility.

What distinguishes this approach from a pure workflow is its adaptive nature. Rather than following predetermined pathways, the system evaluates each query independently, selects optimal information retrieval strategies, and synthesizes responses tailored precisely to the analyst’s needs. This contextual awareness enables the platform to effectively handle the varied and complex questions that emerge during the investment research process.

The core architecture integrates several advanced components: a comprehensive RAG Knowledge Base containing our proprietary research and analysis, dynamic internet search capabilities that extend beyond our internal data, contextual tool selection mechanisms that identify the most appropriate analytical approach for each query, integration with OpenAI’s ChatGPT (o3-mini), and multi-user collaboration features that allow our entire investment team to leverage and contribute to the platform’s capabilities.

The impact has been transformative, significantly reducing hours of research into concise, insightful summaries that address precisely what our analysts need to know. This evolution toward more agent-like behaviors while maintaining the reliability of structured workflows represents exactly the kind of architectural innovation that defines the transition between our second and third horizons of AI implementation.

Deep Research Integration

Our most advanced AI implementation at 71 Capital is our Deep Research platform, which represents a further evolution of our Research Workbench into truly agent-based territory. This system addresses broader, more open-ended questions compared to structured tasks like Investment Memorandums, requiring less rigid frameworks and more adaptive methodologies.

The fundamental architecture follows an iterative research loop that begins with clearly defined research objectives and parameters for exploration depth and breadth. From there, the system enters a sophisticated research cycle:

1. Intelligent Query Generation: The agent autonomously formulates targeted queries aligned with research goals, adapting its approach based on emerging information.
2. Parallel Web Searches: The system concurrently searches across multiple sources to efficiently gather comprehensive information, operating across diverse data landscapes simultaneously.
3. Results Processing: More than mere collection, the system analyzes and synthesizes findings to identify actionable insights, connecting disparate information points into coherent narratives.
4. Recursive Exploration: Perhaps most impressively, the system continuously deepens research by iteratively refining and expanding queries based on insights, navigating research paths that human analysts might miss.

Throughout this process, the agent maintains contextual awareness, continuously incorporating new findings to ensure all subsequent actions remain aligned with the original research objectives. The output consists of structured, comprehensive markdown reports complete with actionable insights, references, and citations.

This approach dramatically differs from traditional research workflows in its flexibility and context-awareness. For example, when we tasked the system with researching statistical arbitrage strategies across centralized and decentralized exchanges, it autonomously generated a sophisticated 30-page analysis in just 10 minutes—work that would have required days with traditional research approaches.

The critical innovation in our Deep Research platform is its ability to handle complex, evolving contexts. The system excels at generating comprehensive analysis for technical subjects like algorithmically-driven trading strategies, beginning with well-formulated queries, proceeding through clarification dialogues, and eventually producing structured reports that include sophisticated elements like pseudocode for implementation.

As the agent ecosystem continues evolving, we’ve carefully tracked the emerging competitive landscape across leading LLM platforms. When Gemini 1.5 Pro first piloted deep research capabilities, it set high benchmarks by integrating tightly with Google’s ecosystem. However, ChatGPT quickly established dominance in complex analytical tasks that appeal to technical professionals. More recently, Perplexity has carved out a niche through rapid, interactive insights, while You.com targets enterprise-grade research demands, and xAI’s Grok has made headlines with its uniquely real-time, social data-driven approach.

These platforms differ significantly in performance characteristics—from Perplexity’s rapid 2-4 minute processing to ChatGPT’s more thorough 30-minute deep dives, from source coverage ranging from ChatGPT’s limited 25 sources to You.com’s impressive 400+ simultaneous source processing. Each platform has carved distinct specializations, whether in technical research, coding tasks, market analysis, or real-time trend monitoring.

This competitive innovation cycle is precisely why we’ve designed our systems to be platform-agnostic, allowing us to leverage each model’s strengths while mitigating their individual limitations. The result is an adaptive research capability that significantly expands our analytical horizon while maintaining the critical human judgment that defines our investment philosophy.

The Vendor Landscape: A Strategic Decision Matrix

Investment firms now navigate a complex decision landscape regarding AI implementation, choosing between AI-native startups and enhanced incumbent platforms:

AI-First Platforms

A wave of specialized vendors has emerged, including:

• Perplexity for Finance: Combining conversational interface capabilities with authenticated financial data sources.
• AlphaWatch: An “AI copilot for hedge funds” integrating external financial data with proprietary information.
• Brightwave: An AI research assistant generating financial analysis by synthesizing earnings reports and filings.

These pure-play AI platforms often introduce innovative features ahead of incumbents and cater to specific niches within the investment workflow.

Enhanced Incumbent Platforms

Traditional market data providers are actively embedding AI capabilities:

• Bloomberg: Developed BloombergGPT (a 50-billion parameter finance-trained LLM) and integrated AI-written earnings summaries.
• FactSet: Launching generative AI search and conversational APIs.
• S&P Global: Implementing AI-enhanced search and data integration across its platforms.

Incumbents benefit from trusted data and compliance frameworks while often struggling to fundamentally reimagine their architectures.

The emerging industry pattern isn’t one vendor category displacing the other but rather a complex ecosystem where both approaches address different aspects of the investment process. Many sophisticated firms maintain relationships with traditional data providers while selectively integrating specialized AI tools for specific use cases.

Strategic Implementation Roadmap

For investment organizations navigating this transformation, I recommend a phased approach that balances immediate gains with long-term architectural advantages:

Near-Term (12-18 months)

• Evaluate platforms based on their architectural roadmap toward AI-native capabilities.
• Prioritize systems that support universal connectivity standards.
• Build internal competencies in prompt engineering and AI workflow design.

Medium-Term (18-36 months)

• Implement internet-scale semantic search capabilities.
• Develop hybrid workflows combining human judgment with emerging AI capabilities.
• Restructure research processes to leverage multi-modal data integration.

Long-Term (3-5 years)

• Prepare organizational structures for autonomous AI workflows.
• Design governance frameworks for agent-based systems.
• Develop performance metrics for measuring AI-augmented analyst effectiveness.

The Fundamental Thesis: AI as Force Multiplier

The central argument that I’ve been advancing to investment leaders is that AI represents not merely an incremental improvement but a fundamental force multiplier for investment professionals. When properly implemented through AI-native architectures, these technologies promise:

1. Comprehensive market intelligence spanning the entire web rather than just subscribed databases.
2. Expansion of research coverage from dozens to hundreds of companies per analyst.
3. Enhanced competitive intelligence through continuous monitoring of market trends.
4. Accelerated investment thesis generation, reducing research cycles from weeks to hours.

This force multiplier effect—potentially increasing productivity by an order of magnitude—represents a competitive imperative for investment firms. The organizations that recognize this architectural evolution and position themselves to capture the transformative benefits of truly AI-native systems will likely establish compounding advantages, while those implementing superficial AI features risk falling behind.

The journey to fully AI-native investment operations is still unfolding, but the trajectory is clear: AI is evolving from experimental technology to the fundamental infrastructure of modern investment management. Just as previous technological revolutions created clear winners and laggards, this AI transformation in investment management will likely reward the architecturally astute and strategically bold.