Why Generic AI Alone Falls Short On Property Data

Pranit Banthia, Founder & CEO of HitechDigital, leads Hitech i2i, an AI-powered property intelligence platform for real estate data.

The general-purpose AI models arriving every day can work for tasks ranging from regular coding to summarizing or writing text. But the same generic AI that looks good in demos fails to deliver in production workflows with property data, because the failures are not simply about model capacity. Handling property data requires jurisdiction-specific rules, traceability, validation, context and the specialized data that a generic AI model will not have.

It’s the dataset that spells the difference.

James Betker, an OpenAI engineer, had once summed up what actually defines AI models: “It’s the dataset.”

That aligns with what I have seen working with AI, ML and property data across more than a thousand U.S. counties.​

Even with the right model, you have only part of the answer. For real estate data handling, you need to build a property data-specific system around the AI. You need the rules, the validation systems and human review to make the AI effective.

Many real estate, title production and insurance firms are aware of the gap and are moving to focused solutions.

​Property data is contextual and sensitive, not simply unstructured.

There’s an entire industry that serves just this one need: retrieving, organizing, verifying and updating property data. The stakeholders range from title plants, title search and production companies to MLS, other real estate data platforms and many service providers in between.

Having worked in this ecosystem for decades, I know firsthand how sensitive it is to handle real estate data. It’s the searchers, the title abstractors, the title examiners and others who work every day in the field who know the real implications.

Besides being difficult to access, such data cannot be directly fed into the models for training.