Training machine learning models requires vast quantities of high-quality, relevant data. For SaaS providers working in multi-location ecosystems, especially those building AI tools for business intelligence, accessing, integrating and organizing that data can be one of the biggest roadblocks to success.

One of our clients, a company focused on AI-driven analytics for multi-location businesses, ran into exactly that problem. While their engineering team had the modeling expertise, they lacked a clean, scalable pipeline to ingest both internal and external data in a consistent format. Worse, their data integration sources weren’t aligned in structure, couldn’t be integrated into their cloud warehouse, and often required significant preprocessing just to be usable.

Here’s how Local Data Exchange (LDE) helped them overcome these barriers and how our API-powered approach can accelerate AI development for other SaaS platforms as well.

The Problem: Disconnected, Unusable Data

This client came to us while building AI models for competitive analysis and local SEO forecasting across thousands of business locations. But they hit a critical bottleneck:

• No access to reliable external data sources. They needed third-party review data, location metadata, and business listings but didn’t have reliable pipelines to ingest them.
  
• Internal data lacked standardization. Even the data they already had in-house wasn’t formatted consistently enough to be used in training without time-consuming transformation.
  
• Warehouse incompatibility. Their cloud infrastructure couldn’t support direct ingestion of the raw data from various APIs and file sources. They were spending more time building ETL scripts than improving their models.
  

Simply put, they had the model logic but not the data they needed, in the right place or format, to make it work.

Our Solution: Customized APIs and Streamlined Data Delivery

LDE stepped in to simplify and accelerate their pipeline. Rather than offering a one-size-fits-all API, we approached the problem collaboratively:

We sourced the right data: Using our Listings, Reviews, and Keyword APIs, we provided access to structured data across multiple publishers and business profiles. This gave them location-specific data points like:

• Review counts, average sentiment, ratings and review velocity
  
• Active business listings and source credibility
  
• GeoGrid rankings and keyword overlap vs. competitors
  

We enabled selective ingestion: The client didn’t want to deal with unnecessary fields or bloated JSON files. Using our API customization services, we gave them the ability to:

• Pick individual data points based on training needs
  
• Test custom queries in our sandbox before committing
  
• Filter by vertical, geography, or publisher as needed
  

We standardized the format: Instead of sending raw API responses, we cleaned and transformed the data:

• Flattened JSON into CSVs and column-based formats
  
• Applied consistent naming conventions
  
• Normalized across all sources to ensure alignment
  

We delivered it where they needed it: Whether it was their Snowflake warehouse or a secure S3 bucket, we handled the last mile of data delivery—ensuring they could plug it into their model training scripts with minimal extra work.

The Impact: Faster Model Training, Better Outcomes

With these changes in place, the client was able to:

• Reduce their data engineering time by 70%.
  No more writing endless ETL scripts—our data was ready to go.
  
• Improve model accuracy.
  With structured, consistent, multi-source data feeding their training, their AI models produced better local insights and improved predictive power.
  
• Iterate faster.
  Because they could customize their data selection and refresh at any time via our APIs, they were able to test new training configurations in hours—not weeks.
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Why It Matters for SaaS SEO and Local Analytics Providers

If you’re building analytics or optimization tools for businesses with hundreds or thousands of locations, the quality and structure of your data integration will make or break your product. APIs alone are good but not enough, you also need:

• Granular control over what you pull
  
• Standardized data across sources
  
• Flexibility in how and where you receive it
  

At LDE, we combine powerful APIs with hands-on support and customization. Whether you’re training AI models, building custom dashboards, or running performance analytics, our team can help design a data delivery solution that matches your architecture and goals.

Let’s Build Your Ideal Dataset for Integration

Looking to streamline your own model training pipeline? Whether you’re just starting or scaling up, we’ll help you:

• Source verified data from the right channels
  
• Test, select, and filter what you actually need
  
• Deliver it in the format and cadence that fits your workflow
  

→ Contact us today to request a customized solution for your business.