Hopi
Defects reaching production fell from 7% to 2%
Overview
Hopi is a data-driven customer technology company within Boyner Group. It set out to raise its quality standard in a retail environment shaped by intensive campaign cycles, without slowing its release cadence. In the work we carried out with RabbitQA, defects reaching production fell from 7% to 2% and the regression test cycle was cut in half.
- Company
- Hopi, a data-driven customer technology company within Boyner Group
- Partner network
- 300+ brands
- Industry
- E-commerce and retail
- Product used
- RabbitQA
- Award won
- The AI Awards 2026, Best AI Usage in Retail
Before and after
| Measure | Before | After |
|---|---|---|
| Defects reaching production | 7% | 2% |
| Regression test cycle | Baseline duration | 50% shorter |
| Test case creation | Limited by automation engineer capacity | Produced by a multi-agentic system |
| Requirement to test traceability | Tracked in fragments | Recorded end to end |
Industry need
In retail, the quality standard has to keep pace with the campaign calendar
In retail and e-commerce, a large share of revenue is generated during campaign periods that are announced in advance. In those periods the release calendar tightens, the volume of change rises, and the time available for verification narrows. When quality control becomes the bottleneck, two options remain: delay the release, or push an unverified change into production. Both carry a direct revenue consequence.
The cost of a defect reaching production in retail is not only technical. An interruption in the cart, payment or campaign flow converts into lost transactions and eroded customer trust within the same day. The objective at board level is therefore not to reduce the number of defects, but to hold the defect rate at a level that is independent of release speed.
Hopi defined its need in these terms: bring the rate of defects reaching production down measurably while maintaining release frequency.
Product
We moved the quality cycle back to the requirements stage
Prioritising scope by business impact
We ranked critical user flows by transaction volume and revenue exposure rather than by technical complexity. The flows carrying the highest volume during campaign periods became mandatory scope, and the remaining flows moved into risk-based assessment.
Project outcome
Quality indicators improved while release speed was preserved
Defects reaching production fell from 7% to 2%. That represents a 71% reduction in the defect rate reaching the production environment, and the level held through campaign periods.
The regression test cycle was cut in half. Pre-release verification stopped delaying the release calendar, and scope was expanded without reducing release frequency.
The improvement in user experience carried through to customer indicators. NPS rose and customer churn declined.
The work received the Best AI Usage in Retail award at The AI Awards 2026.
