What we built: When data mapping stops being a bottleneck

In the healthcare claims ecosystem, speed and accuracy in data processing are critical. Intermediary platforms connecting payers and provider networks must transform incoming datasets into a standardized structure before claims can be quoted.

To accelerate this step, we developed the MacroMap AI Engine, an AI-powered solution that automates the mapping of source data into a standardized schema.

By transforming a manual, error-prone process into an intelligent, assisted workflow, the solution significantly reduces mapping time while improving accuracy—enabling faster response times and more efficient collaboration across the healthcare value chain.

The challenge

In the healthcare ecosystem, intermediary platforms play a key role in enabling claim quotations between payers and provider networks.

Before a request can be processed, payer data must be transformed and normalized into a required standardized schema. This includes ensuring that all necessary fields are present and properly aligned in terms of format, structure, and semantic meaning.

The challenge emerges at the very first stage of the workflow.

Today, data mapping (source → standardized schema) is largely manual. Users must review columns one by one, identify equivalent fields, and validate that each element meets the required structure.

This approach leads to:

• Slow initial processing times.

• Heavy reliance on manual validation.

• Increased risk of interpretation errors.

• Delays in delivering quotes.

In a context where both speed and accuracy are essential, this bottleneck directly impacts response times and overall efficiency.

The solution: MacroMap AI Engine

To address this challenge, the MacroMap AI Engine introduces an AI-powered approach to automatically map source data fields to a standardized schema.

The system analyzes incoming datasets and intelligently suggests field correspondences between source data and the destination structure.

What previously required multiple manual steps is now transformed into an AI-assisted workflow, where users validate and refine suggestions instead of building mappings from scratch.

The result is a faster, more reliable, and scalable process.

Results

• 50% reduction in total mapping time.

• 282% increase in mapping accuracy.

• Faster onboarding of new datasets.

• Reduced friction at the start of the workflow.