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How BlackRock Builds Custom Knowledge Apps at Scale — Vaibhav Page & Infant Vasanth, BlackRock
Takeaway
BlackRock scales internal AI by standardizing extraction, workflow, Q&A, and agentic patterns into a shared platform investment-ops teams reuse for each new domain app.
Summary
- BlackRock's data engineering team classifies AI apps into four buckets: document extraction, complex workflow automation, Q&A/chat, and agentic systems.
- Walkthrough of a New Issue Operations use case: ingesting prospectuses and term sheets through an LLM pipeline to set up securities (IPOs, splits) before portfolio managers can trade them.
- Built a reusable internal platform so investment operations teams can stand up domain-specific knowledge apps without re-implementing extraction, structured output, and downstream integration each time.
- Emphasis on speed-to-production for hundreds of internal tools used across investment operations, compliance, trading, and post-trade workflows.
enterprise-ragdocument-extractionplatform
Original description
Investment Operations teams are the backbone of asset and investment management firms. Their day-to-day work not only enables portfolio managers to respond swiftly to market events but also ensures that complex, unstructured data flows seamlessly across the organization. In this talk, we introduce a modular, Kubernetes-native AI framework purpose-built to scale custom Knowledge Apps across the enterprise. Designed with speed, flexibility, and compliance in mind, the framework empowers teams to launch production-grade document extraction applications in minutes instead of months, unlocking new levels of automation and efficiency for investment management workflows. We’ll also share how this framework has helped BlackRock streamline document extraction processes, generate investment signals, reduce operational overhead, and accelerate the delivery of high-impact business use cases—all while maintaining the robustness and control required in a regulated industry. 00:30 Introduction to BlackRock's AI Initiatives 01:31 Classifying AI Applications 02:22 Use Case: New Issue Operations 03:59 Challenges with Scaling AI Knowledge Apps 07:02 Architecture of BlackRock's AI Framework 08:32 Demonstration of the Sandbox 15:52 Key Takeaways from the Discussion Vaibhav Page Principal Engineer Vaibhav is a Principal Engineer at BlackRock, where he leads the development of the Data Science and AI platform powering investment research and automation across the firm. Vaibhav is also the author of Argo-Events, a CNCF-graduated project widely used for event-driven automation in cloud-native environments. Infant Vasanth Senior Director of Engineering Infant Vasanth leads the engineering team responsible for the Studio Compute Platform, BlackRock's analytics and automation platform that enables our users to conduct research & analysis, run automations and distribute research at scale. In addition, Infant is also leading the Data & AI Acceleration team focusing on efforts to enhance Aladdin Studio's AI capabilities along side the Operational AI capabilities(prospectus analyzer, operational agents etc.)