How AI Agents Saved Pricer’s QA Process

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 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.

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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

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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.

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