Loyalty Intelligence Platform Backend

Built the backend for a loyalty intelligence platform that indexes data across 400+ airline and rewards programs and serves fast queries for transfer-partner lookups and point valuations.

ClientTravel Technology Startup

Duration3 months

400+

Programs Indexed

65+

Countries Covered

Low ms

API Response Time

What We Delivered

High-performance backend indexing 400+ loyalty programs across 65+ countries

Near-instant API for transfer-partner lookups and point valuations

Automated data pipelines for continuous program updates

Pre-calculated query results for the most frequent lookups

Problem

Pulling together loyalty program data from 400+ airlines and rewards programs across 65+ countries means dealing with wildly inconsistent formats and constant updates. Users expect instant comparisons and valuations, so the system needs to be fast.

Solution

We built a backend that collects and standardizes data from hundreds of loyalty program sources into one unified system. It serves transfer partner lookups, point valuations, and program comparisons near-instantly. Common queries are pre-calculated and cached so the most frequent lookups return immediately.

Technology Used

Java 25RedisKubernetesGrafana

Impact

400+ airline and loyalty programs across 65+ countries available in one place

Near-instant response times for transfer partner and valuation queries

Users can compare point values across hundreds of programs side by side

Automated pipelines that deploy updates to production in minutes

Reduced API response time by 95% compared to the previous solution

Architecture Highlights

Common queries are pre-calculated so the most popular lookups return instantly

Frequently accessed data is cached in memory for speed

One clean data model that handles 400+ programs despite wildly different source formats

Built to handle heavy read traffic without slowing down

Lessons Learned

When speed is what makes the product valuable, performance work is a feature, not an afterthought

When every data source looks different, you need flexible ingestion with strict rules on what comes out the other side

Pre-calculating results uses more storage but makes the user experience dramatically faster

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