**Company:** Enterprise B2B (Hardware + Software) **‍**  
**Manual QA testers**: 2  
**Integration:** GitHub  
**Time saved**: 390h per quarter

**Before**:  
8 manual testers, blocked releases, and bugs in production.

**After**:  
2 manual testers, smooth release flow, and less bugs in production.

### Company Overview  
[**Pricer**](https://www.pricer.com/) builds and operates Electronic Shelf Labels (ESL) used in over 28,000 stores across 70+ countries. Their platform connects hardware and software in real-time, powering price updates and in-store coordination. The system is business-critical, and any issues cause large damage to operations.

### The problem before QA tech  
Pricer had a QA team of 8. Most testing done manually, with a growing suite of E2E tests. However, this did not prevent bugs from entering the production.

After a company restructuring, only 2 QA engineers remained.

Key issues:  
- Automated tests weren’t catching regressions reliably.  
- Test coverage was limited and brittle.  
- Manual QA was slowing releases down.  
- The platform’s complexity was larger than the QA team’s bandwidth.

Pricer’s QA team needed a way to test better, not just more.

### What changed with QA tech  
Chris Chalkitis (CTO) pushed for a shift toward AI-driven testing and introduced **QA.tech** into the workflow — an agentic testing platform that uses AI to generate and maintain tests.

Instead of scaling headcount, they used QA agents to scale test depth and frequency.

> **“We project that we will significantly improve quality while saving money in the next year by investing in agentic testing that allows us to test deeper and more frequently than manual testing.”**  
> – Chris Chalkitis, Chief Digital Officer

### How it works now  
Each product team at Pricer owns its test plan. QA.tech agents create, update, and run test cases automatically, integrating with GitHub to run checks on every pull request.

- GitHub + CI integration ensures that all critical paths are tested pre-merge.  
- Smoke tests run continuously to catch regressions early.  
- Agents adapt to UI/API changes and propose updates to stale tests.

> **“With QA.tech, we automate cases we previously didn’t have the resources to cover. Since implementation, we have increased our test coverage and significantly reduced deployment risks.”**  
> – Thomas, Platform Development Manager

### Results  
- **Test coverage increased** despite the team shrinking from 8 to 2.  
- **Fewer bugs** in production.  
- **Release cycles sped up**, with fewer blockers from QA.  
- Engineers spend less time setting up tests and more time on features.  
- Teams feel more confident deploying changes.

### How JOIN automated QA with AI agents  
JOIN Solutions eliminated a severe QA bottleneck by transforming week-long manual regression testing into automated test runs using QA.tech's AI agents.

### How Crystal Intelligence cut regression from weeks to hours  
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### Bizbrains automates UI testing for continuous delivery  
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### Smartlinx replaced legacy QA processes with AI agents  
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### 4C Strategies scales testing with AI automation  
4C Strategies overcame massive testing bottlenecks by using QA.tech's AI agents to read dense user manuals and autonomously test highly customized workflows.

### Airpelago – Scaling Drone Operations with Reliable End-to-End Testing  
Airpelago builds advanced drone technology for higher-definition power line inspections. With autonomous path planning and advanced imaging, their platform allows utilities to reduce costs, improve safety, and inspect power infrastructure at scale.

### How Upsales Replaced 320h of Manual Testing with Agents  
Upsales is a CRM system that helps sales teams work smarter. It handles tasks ranging from mass mailing to managing deal flows. Their platform is central to daily operations, which means even a small bug can lead to major disruptions.