Tokenless

Unstructured data

How Bridgewater Turned 100 Million Documents Into an Edge - Sam Green & Aaron Linsky, Bridgewater

How Bridgewater Turned 100 Million Documents Into an Edge - Sam Green & Aaron Linsky, Bridgewater

Bridgewater Associates' Applied AI and product engineering leads explain how they built the Vault, a governed repository of more than 100 million unstructured documents, and why humans and agents need different things from the same search system. They cover a hand-built benchmark that lifted precision at 10 from about 60% to over 90%, the filtering and scaling choices behind it, and the shift toward proactive agents that bring investors the topics most deserving of their attention.

From Raw Documents to AI-Ready Data — Leo Platzer & Jeff Koss

From Raw Documents to AI-Ready Data — Leo Platzer & Jeff Koss

This presentation by Jeff Koss and Leo Platzer from Deasy Labs (now Collibra) tackles the critical challenge of scaling AI chatbots from pilot projects to production with large, messy unstructured datasets. They demonstrate a solution for curating and cleaning 80,000+ SharePoint documents, highlighting the importance of data quality—specifically identifying duplicates, conflicts, sensitive information, and ensuring data freshness—to significantly improve RAG chatbot performance and reduce development time.

AI on Your Lakehouse: Context Comes in Shapes, Not Queries — Zach Blumenfeld, Neo4j

AI on Your Lakehouse: Context Comes in Shapes, Not Queries — Zach Blumenfeld, Neo4j

AI agents often struggle with context in lakehouses, leading to confident but incorrect answers. This workshop by Zach Blumenfeld introduces three Neo4j graph shapes built on lakehouse data—Connections (semantic layer), Trees (document outlines), and Communities (themes)—to provide essential context, enabling agents to accurately navigate structured and unstructured data, answer complex estate-level questions, and overcome limitations of traditional Text2SQL and vector search.

Aaron Levie: Why Startups Win In The AI Era

Aaron Levie: Why Startups Win In The AI Era

Box CEO Aaron Levie discusses the paradigm shift from cloud to AI, arguing that AI will augment jobs by automating non-strategic work and create massive opportunities for startups to tackle previously unsolvable problems with unstructured data. He covers the evolution of SaaS business models, the 'build vs. buy' debate, and offers key advice for founders in the AI era.

The AI that solves the market: A new era in forecasting with natural language explainability

The AI that solves the market: A new era in forecasting with natural language explainability

LG AI Research introduces its advanced financial forecasting framework, which powers a US equities market ETF (LQAI) and a new "Master Score with Commentary" product with LSEG. The system uniquely combines structured financial data with unstructured text from news and reports, using the proprietary Exaone LLM and a multi-agent architecture to deliver explainable, accurate, and actionable market predictions across the entire US stock market.

Unlocking Unstructured Data with LLMs

Unlocking Unstructured Data with LLMs

Shreya Shankar of UC Berkeley discusses DocETL, a MapReduce-style framework that leverages LLMs to extract, analyze, and structure insights from unstructured enterprise data. The conversation covers practical architecture patterns, the role of non-determinism, strategies for model selection (including fine-tuning and multi-LLM pipelines), and the importance of user experience in this emerging field.