Building an Intelligence Engine

Summary

Morningstar chose Weaviate's vector database to build its highly accurate and scalable Intelligence Engine Platform, offering users reliable, AI-driven financial research tools and transparent, dynamic document search capabilities.

Challenge

Over the last 40 years, Morningstar amassed an extensive collection of proprietary financial data. Morningstar has sought to further empower investors that rely on its data by developing an advanced research assistant AI application. According to Benjamin Barrett, Morningstar’s Head of Technology, Research Products, building a chatbot on that data “looks like magic, but when you start peeling back the layers of the onion, you have to ask, is it actually accurate? Is it pulling the latest, greatest, most relevant data? Are our answers robust and complete?”

As his engineering team worked with Morningstar’s Quantitative Research team to build their Intelligence Engine Platform, they had to ensure an incredibly high level of accuracy in order to maintain their users’ trust. “We want to have one single source of truth. Our whole mission is to empower investor success. And the way to do that is to give them reliable, trustworthy financial data.”

Why Weaviate

In early 2023, Morningstar saw early success in experiments with LLMs and snippets of their own data. They quickly realized the potential of using AI to harness decades of longform research content and real-time data with RAG, and started their search for the right vector database.

Why Morningstar chose Weaviate:

• Ease-of-use: Weaviate’s open-source database was quick and easy for Morningstar to spin up locally in a Docker container and start experimenting.
• Data privacy and security: Flexible deployment options and multi-tenant architecture allowed for strict data privacy and security compliance.
• Flexibility and scalability: Weaviate supported a variety of use cases–from search engines to tailored AI applications–and was able to handle large and diverse data sets.
• Support: Weaviate offered great support from local development all the way to production. 

Results

With Weaviate, Morningstar was able to build their Intelligence Engine Platform, a product which solves a challenge facing today’s financial services firms: how to easily create and customize AI applications built on a foundation of trusted financial data and research. The Intelligence Engine also powers a variety of workflows, APIs, and chat interfaces across Morningstar’s own product ecosystem – including Mo, it’s investment research-assistant chatbot – for both internal and external users.