SeeMore Data: Driving Data Value with AI-Powered Insights

Overview

At CodeValue, we specialize in developing innovative software solutions that address our clients' unique challenges. Recently, we had the opportunity to partner with SeeMore, an end-to-end data product optimization platform that focuses on maximizing data ROI, attributing data spend, and eliminating cost spikes across various pipelines and stacks. Our task was to enhance SeeMore's ability to understand and optimize their complex data processes by generating automated descriptions, ultimately supporting their mission to drive growth through data-driven insights.

Customer Needs

SeeMore required a solution to automatically generate detailed descriptions for large SQL tables, SQL jobs, and data flow lineage from their databases. Their key goals included improving data transparency, making it easier to understand complex data transformations, and optimizing their data workflows.

Solution

To address SeeMore's needs, we developed three key capabilities:

1. SQL Table Auto-Description: We implemented a solution to automatically generate descriptions for SQL tables, even those with extensive column  transformations. This capability helps SeeMore quickly understand the structure and purpose of large datasets.
2. SQL Jobs Auto-Description: We created a feature that provides detailed descriptions of SQL jobs, regardless of their size. This helps SeeMore ensure that all SQL jobs are documented
3. Data  Flow Lineage Auto-Description: We developed a system to automatically generate descriptions of data flow lineage. This enhances SeeMore's ability to track and understand the movement of data across their systems.

To make these capabilities feasible within the constraints of LLMs, we implemented a splitting algorithm that intelligently breaks down large datasets and descriptions to fit within the LLM context window. This ensures that even the most extensive data can be processed effectively.

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‍Key Innovations

A critical aspect of our solution was the introduction of a rate limiter to manage the usage of LLMs. This mechanism not only helps SeeMore control operational costs but also ensures that their system remains efficient and responsive under varying loads.

Furthermore, we developed the capability to utilize multiple model providers, This flexibility allows SeeMore to select the most suitable model for their specific needs, ensuring optimal performance and cost-effectiveness.

Additionally, we created evaluation tests for the models to continuously assess their performance and make necessary adjustments to the code and prompts. This iterative approach ensures that the models remain accurate and aligned with SeeMore's evolving requirements.

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‍Conclusion

Our work with SeeMore showcases how advanced Gen AI integration can transform complex data environments. By enabling automated descriptions of SQL tables, jobs, and data flow lineage, we’ve helped SeeMore achieve greater transparency and efficiency in their operations. The introduction of a splitting algorithm, rate limiter, multi-model support, and continuous model evaluation further underscores our commitment to delivering tailored, cost-effective solutions that meet our clients' evolving needs.

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