Supercharging the Future: How Metadata-Enhanced AI Unlocks New Value for Our Customers

June 27, 2024
Mike Barrameda, Chief Architect
 min read

Key Points

Metadata is information about the data, such as its content, quality, and characteristics.

AI leverages metadata to contextualize data and generate more accurate outputs.

Ridgeline is uniquely adapted to take advantage of AI because it is a single platform with one unified data model built upon a powerful metadata layer.

In an era where financial markets move at the speed of light, and data volumes expand exponentially by the minute, the investment management industry stands to benefit massively from the latest advancements in AI. While the innovations provided by Generative AI are very impressive on their own, in order to maximize the potential benefits of this technology for your firm requires natively pairing AI tools with not only the business data captured by your software, but also data about the data - or metadata. At Ridgeline, we’ve built a front-to-back investment management platform supporting CRM, OMS, and PMS applications atop a foundation of deep contextual metadata that will take our Generative AI capabilities to the next level.

The Importance of Metadata in AI

Metadata is essentially data about data. It provides contextual details about the content, quality, condition, and other characteristics of data. Think of metadata as a summary or a label that gives context to what the data represents. For example, in the context of a digital photograph, the metadata might include information such as the date and time the photo was taken, the type of camera used, the settings of the camera at the time of capture (like aperture, shutter speed, and ISO), and possibly the location if GPS was enabled. For the investment management domain, metadata about, say, a trade order would include information about which PM raised the order, when it was approved, and who claimed it for execution on the Trading desk. Going a level deeper, additional metadata could be captured to describe the data elements themselves, such as types, descriptions, and constraints of each piece of data.

This information helps users and systems understand and manage data more effectively by making it searchable, organized, and useful for analysis and decision-making. 

Metadata acts as the DNA of information, transforming raw data into a rich, navigable, and insightful resource. When integrated with Generative AI, this enriched data enables Large Language Models (LLMs) to understand nuances, grasp context, and generate outputs that are not only more accurate but deeply tailored to specific needs and scenarios. This approach enables a level of personalization, efficiency, and effectiveness that generalized AI solutions, which often rely on broad, undifferentiated data sets, simply cannot match. 

For industries like investment management, where precision and contextual understanding are paramount, the combination of metadata and generative AI isn’t just an improvement, it’s a paradigm shift towards more intelligent, responsive, and personalized solutions. Ridgeline’s metadata-driven platform makes us uniquely capable of harnessing this potent combination, setting us apart as not just a provider of technology, but as a partner with solutions that anticipate and exceed the evolving demands of modern business.

What Sets Ridgeline Apart

Much of the power that comes from Ridgeline is that it is a single platform with one cohesive and unified data model that is built upon a powerful metadata layer. Our unified platform ensures seamless data integration and consistency from the start, as opposed to painfully attempting to conjoin disparate, and often incongruent, datasets sourced from separate systems into a data warehouse. I think we can all agree that less is more when it comes to ETL pipelines! Not only is all of your firm’s data in one system from the front-to-back office, but that data is enriched by its descriptive metadata to allow you, and Large Language Models, to have a more complete picture and draw insights you couldn’t with legacy tech’s passive data spread across multiple disparate systems. 

Not only do we collect metadata to describe our data models, but we also have metadata for describing the product itself, its features, and what actions are possible. For example, we may have additional metadata to help:

  • Enrich the reportability of a data model within our Report Builder
  • Fine-tune access controls on specific data models
  • Drive the auditing characteristics of a data model
  • Configure how an instance of data is indexed for search-based access patterns
  • Classify which fields of a model are considered Personally Identifiable Information (PII)
  • Provide a standard way to render forms for the user interface

This metadata-based interface allows us to continue to build new and more powerful features across all areas of product at a rapid pace. Ideas can quickly turn into new product features with this loosely-coupled approach to product development. However, what’s even more exciting is about how powerfully metadata can help supercharge our AI features for the benefit of our customers.

A More Powerful AI Assistant

Earlier this year, we launched Ridgeline Assistant, which is an AI-based feature that provides a conversational interface for our customers to interact with by asking questions. We’ve seen multiple variants of chatbots appear in different products and industries, often providing generalized outputs of information to help you accomplish your task. However, without a more integrated approach, these chat assistants are limited to answering questions based on the static knowledge base with which they are provided. 

This works well for support-centric use cases, but the real benefits are unlocked when these assistants can understand more about the domain they are operating in and the capabilities of the system. It’s one thing for an assistant to ingest and summarize static documentation for you. It’s another thing for it to be able to generate your performance reports (because it knows the metadata about the Ridgeline universal data model) or expedite manual and repetitive tasks (because it knows the metadata about the user interface) simply by asking it to. With a product that has so much metadata at its core, the Ridgeline Assistant can not only answer questions, but can actually accelerate completion of operational workflows and give you back your most important resource — time.

The Impact on Innovation and Efficiency

We’re really excited about how our metadata-driven architecture can push Generative AI features to new heights. We recently hosted a company-wide hackathon, focused on turning AI ideas into reality. Over 300 teammates participated across all of our offices, all equipped with the latest AI tools and technologies to see what is possible. After two days of hacking, the entire company reviewed over 50 amazing ideas leveraging Generative AI. Some of these ideas were so exciting that we had to get them into our customers’ hands sooner rather than later! For example, we’ll be rolling out a conversational interface to our Report Builder that will provide a more natural way to generate customer reports.

Looking Ahead (Responsibly)

While we like to move fast at Ridgeline, we know that any new technology comes with new risks and generative AI is certainly no different. As powerful as AI is, we recognize the potential for it to produce incorrect results or introduce bias into its decision making. It’s extremely important that we understand these qualities for each use case in order to ensure that we aren’t propagating any of those risks to the product and our customers. We’ve formed a Responsible AI Board at Ridgeline composed of cross-functional leaders who vet any ideas or potential projects before they make their way into the product.

At Ridgeline, our focus on combining AI with metadata is about more than just leveraging the latest technology — it's about delivering real value to our customers and their clients. By thoughtfully integrating metadata with AI, we aim to offer solutions that are not only innovative but also practical and responsible. This approach allows us to provide more personalized, efficient, and predictive insights, helping our customers navigate their challenges with greater ease. It's a journey towards creating technology that serves real needs, ensuring that our advancements contribute positively to the broader community. As we move forward, our goal remains clear: to use AI and metadata in ways that are both strategic and mindful, unlocking new possibilities for our customers in a responsible manner.

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