Data coverage and accuracy for small merchants is notoriously low. So the only way Flatpay could filter out merchants that were too small was by having reps call them.With hundreds of reps and many ramping at all times, calling merchants that should have been disqualified has been a major source of rep inefficiency.
Flatpay finds a high volume of merchants from Google Maps, but this information alone provides limited insights about each merchant. Kernel solves this challenge by enabling Flatpay to research every merchant in depth, as though a dedicated SDR had unlimited time to gather information.By feeding a fine-tuned reasoning model with structured and unstructured data sourced by Kernel agents, Flatpay is able to estimate total payments volume and pre-qualify merchants prior to reps calling them
Let’s take an example account: ACME Japanese Bar and Sushi
The relevant publicly available data is:
Full-service restaurant in both Sushi & Japanese Cuisine, Vietnamese Cuisine
Facilitates payments both physical and online
High foot traffic in an urban location
278 Google reviews
Average price point range is €15-25
Feeding this data to the model allows Flatpay to estimate TPV and qualify or disqualify the account.
