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Case study · Retail & E-commerce

An OMS that survives Black Friday

Stood up an order management system that holds at 42× normal load — replaced a vendor product that had failed two peaks in a row.

A global fashion retailerGoKafkaRedisPostgresGCP
The problem

The problem

  • Vendor OMS had failed two consecutive holiday peaks, losing the retailer ~₹64Cr in deferred revenue across the two events.

  • Inventory accuracy was 71% — driving over-promise, under-deliver, and a 14-day cancellation tail.

  • 9-month deadline to the next peak. Replatform was non-negotiable.

Our approach

Our approach

  • We took the previous post-mortem and turned every red line into a SLO with an owner.

  • Built the OMS on Go services, Kafka for state changes, Postgres for the truth, Redis for the speed.

  • Chaos-tested against synthetic peak traffic 42× baseline. Found and fixed three load-shedding bugs before October.

  • Shadow-traffic for six weeks, full cutover in the off-peak window, with the old system kept warm for two months.

Results

Results

  • Held at 42× baseline orders through Black Friday weekend with no degradation.

  • Zero P1 incidents through three holiday seasons, including the year of the platform migration.

  • Infra cost down 31% versus the previous OMS at higher throughput.

They asked harder questions than our previous vendor in the first week than that vendor had asked in three years.

Director of Engineering, global fashion retailer
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