Case Studies | Retail

AI Integration for Precise Retailer and Transaction Recognition

Airtime Rewards
About

Airtime Rewards is a UK fintech loyalty platform used by around 3 million people, converting retail spend at 150+ partner brands, including Tesco, Greggs and Argos, into airtime credit on customers' mobile bills.

Challenge
  • Airtime Rewards' system sometimes couldn't identify the retailer behind a transaction description, so loyalty points went unallocated.
  • Left unresolved, that risked customer dissatisfaction, reduced engagement, and weaker personalised offers for retail partners.
  • The team needed accurate, automated retailer attribution rather than manual review of ambiguous transaction records.
Solution

Cloud Combinator built a large language model pipeline in Amazon Bedrock that extracts retailer names from transaction references, checks parent-company and subsidiary relationships, and cross-matches results against Airtime Rewards' live partner list.

By the numbers:

  • Transaction misallocation cut from about 0.5% to under 0.1% of transactions
  • Equivalent to under 4 mismatched transactions per new partner, down from roughly 300,000 across 78 million transactions
  • Issue-resolution time cut from 2-3 minutes to about 30 seconds, a 75% time saving

What changed:

  • AWS Lambda handled the transaction-data processing workflow end-to-end, while Amazon RDS stored matched results for further analysis by the Airtime Rewards team.
  • All processed outputs and retailer mappings were stored securely, with access restricted to authorised systems and personnel.
  • Cloud Combinator trained Airtime Rewards' project lead to operate and extend the system independently after handover.
Airtime Rewards

Cloud Combinator has enabled us to improve our customer and member experience by introducing an AI-driven transaction matching service, reducing toil for our teams and improving retention of our critical retail partners.

Airtime Rewards

Client feedback

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