Scaling Institutional Investment Intelligence Across 1,000+ Investments and $150B+ AUM
With Polyagentic Deep Business Insights Discovery.
Step 1:
Asymmetry Audit
Working with a global investment fund managing private equity, infrastructure, and real estate investments, we identified a critical asymmetry: investment and risk teams needed to analyze hundreds of enterprise data systems, yet consolidating them into a single platform would require a massive, costly transformation. Instead, we deployed Kaboo, a team of visual AI research agents that works alongside business users to autonomously explore enterprise data where it already resides, conduct deep investigations, and deliver trusted insights through intuitive visualizations without requiring large-scale data consolidation.
The outcome: Investment professionals gained rapid, enterprise-wide insights across fragmented data sources while avoiding costly, multi-year data integration initiatives.
Step 2:
Architecture Fit
We designed Kaboo to align with the investment fund's heterogeneous enterprise data architecture, including Databricks, Snowflake, Confluent, relational and non-relational databases, and one of its most critical data assets: complex multi-tab financial modeling spreadsheets containing intricate formulas and investment assumptions. Rather than requiring data to be centralized, Kaboo was architected to intelligently discover, access, and reason across these discrete data sources, enabling seamless cross-system analysis while preserving the organization's existing data ecosystem.
The outcome: A streaming- and data-native architecture that unlocked trusted investment insights across fragmented enterprise data without requiring large-scale data migration or consolidation.
Step 3:
Instrumented Pilot
We enhanced Kaboo to support the fund's most complex financial modeling spreadsheets while integrating Databricks, relational databases, enterprise data catalogs, and web search into a unified research workflow. Using token delegation, Kaboo accessed only the data each user was authorized to view, preserving enterprise security while maintaining full data lineage. Every insight was grounded in its originating source, allowing investment professionals to perform deep investigations with complete transparency and confidence.
The outcome: A production-ready pilot that delivered trusted, source-aware investment intelligence across diverse enterprise data sources while maintaining security, governance, and explainability.
Step 4:
Expertise Extraction
We established a human-within-the-loop collaboration model where Kaboo continuously learned through conversations with investment and risk professionals. Every question, refinement, correction, and analytical direction provided by the user was captured as institutional expertise and stored in the expertise repository. This enabled the AI research agents to continuously refine their hypotheses, improve subsequent research iterations, and increasingly align with the organization's investment methodology and decision-making process.
The outcome: Business expertise became a compounding enterprise asset, enabling Kaboo to deliver increasingly accurate, context-aware, and organization-specific investment insights over time.
Step 5:
AI Compression & Funneling
We optimized Kaboo by assigning the right model to the right task. Smaller reasoning models were distilled to autonomously crawl enterprise data, perform sampling, and extract relevant information from underlying source systems, while a more powerful reasoning model was reserved for the final stage of deep insight generation and hypothesis validation. This layered approach significantly reduced cost and latency while preserving the quality of investment research.
The outcome: An efficient multi-model AI architecture that delivered high-quality investment insights with lower cost, faster response times, and scalable enterprise performance.
Step 6:
Govern and scale
We established a governance framework that continuously measured research quality, time to insight, source coverage, and user feedback for every investigation. These metrics guided ongoing improvements to Kaboo while ensuring every insight remained transparent, explainable, and traceable to its source. As confidence grew, the platform was systematically expanded across additional investment teams, asset classes, and enterprise data sources, creating a scalable AI research capability for the entire organization.
The outcome: A governed, enterprise-wide AI research platform that continuously improved insight quality while scaling trusted investment intelligence across the organization.
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