Data Preparation
Challenge
The client faced multiple challenges. First, they struggled with the management and scalability of their data, putting high-value AI use cases at risk. Inadequacies in data storage, preprocessing, and integration contributed to system stress and technical debt. Moreover, the lack of a coherent strategy for data architecture meant that their Chief Data Officers (CDOs) found it difficult to coordinate with IT leadership on optimizing compute and network costs
Solution
We focused on five key areas of their data architecture:
- Unstructured Data Stores: Implemented metadata tagging standards and mapped out all unstructured data sources to streamline data pipelines.
- Data Preprocessing: Standardized data handling techniques for both structured and unstructured data, which facilitated faster data retrieval and improved AI model training.
- Vector Databases: Utilized existing NoSQL databases to create embeddings, enabling generative AI models to access only the most relevant information.
- LLM Integrations: Provided a framework for seamless integration between Large Language Models (LLMs) and multiple data systems, using open-source solutions like LangChain.
- Prompt Engineering: Developed standards for structuring prompts that maximize the output quality from generative AI models, integrating knowledge graphs and data models for context.
Result
The client was able to not only manage but also effectively scale their data infrastructure, aligning it with high-value AI use cases. This led to a more streamlined, cost-effective system that significantly lowered technical debt and reduced compute costs. Most importantly, they unlocked the potential of generative AI, positioning themselves to capture a share of the estimated millions in economic benefits.
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