Entity extraction and client matching on Power Platform

An enterprise Dataverse and Power Platform implementation was struggling under large data loads: performance limits were being hit, automations were unreliable, and incoming records needed to be normalized and matched to existing clients accurately.

There was also an architecture question on the table: build a custom Azure AI Search integration, or use what the platform already provides.

Contract engineer brought in to stabilize the implementation and design the matching pipeline. I owned the technical decisions, the Power Automate flows, and the AI Builder logic.

I started with the architecture decision. After evaluating both options against the actual business needs, I recommended and justified Dataverse Search over a custom Azure AI Search build. The platform-native option covered the requirements without adding an extra service to license, deploy, and maintain.

With that settled, I built Power Automate flows and AI Builder logic to normalize incoming data, extract entities from unstructured input, and match records to existing clients at scale, then worked through the performance limits and reliability issues in the existing automations.

The implementation went from unstable to dependable under production data volumes, with accurate client matching and an architecture the client's team can maintain without specialist help.

Choosing the platform-native search avoided a custom integration the client would have paid to build and keep running.