← Case studies

Case Study

Test-automation acceleration for a restaurant guest-engagement platform

Days-long manual regression cycles replaced by a self-healing automation suite that runs in minutes.

The problem

A guest-engagement technology provider to national restaurant chains, roughly 150–200 employees in the $30–40M revenue range, shipped every release on 100% manual QA. They had no automation of any kind.

At their scale, that produced a compounding set of problems:

• Regression cycles measured in days. Deployments waited on testing, not on code.
• A growing regression surface against flat QA capacity. Each release added risk the team had no extra hands to cover.
• Execution drift. Steps and coverage varied between testers and between runs, so "tested" meant something different each time.
• No test history. Zero record of who tested what, when, or with what result. Nothing to analyze and nothing to prove.
• Senior engineers doing repetitive work instead of the exploratory and edge-case testing where their judgment mattered.

What our team built

An AI-assisted end-to-end automation framework. We built it so the client's own team could extend it, rather than depend on us to maintain it:

• Playwright under the Page Object Model, in strict TypeScript, so the type system catches breakage before a run does.
• An AI DOM bridge. Agent-driven DOM analysis generates locators and heals them when the interface shifts. That is the point where hand-written suites rot.
• A three-stage CI pipeline triggered on every push, so regression runs on the change rather than on a schedule.
• Automated defect triage. Live reporting classifies each failure as a product defect or a script failure. That distinction decides whether a developer or the QA team picks it up.
• Security-first execution. All credentials AES-256 encrypted and masked in every log and report.
• Leadership visibility. Reports published to a live, secured URL giving real-time pass/fail status without asking anyone for an update.

The outcome

• Core regression from days to 3.2 minutes, fast enough to act as a release gate rather than a release tax.
• 98.5% test pass rate, with strict typing and standardized execution removing run-to-run drift.
• 60–70% faster authoring of test cases and page objects, bringing suite build time down to hours.
• Failures sorted into product defects versus script issues, cutting triage time on every red run.
• QA bandwidth reallocated from repetitive regression to exploratory and edge-case work.
• A foundation that scales by adding spec files, not by adding headcount.

The boundary of this claim

We measured the pass rate and execution time on the delivered suite at handover, against a starting point of zero automation. The improvement is large in part because the baseline was manual. Coverage grows as the team adds specs, and adding one costs little. The suite was partial at handover.

Sizing basis: tabletop/guest-engagement platforms at this scale typically serve multiple national full-service restaurant chains and run in the 150–200 employee, $30–40M revenue range.

Tools & technology

• Playwright (@playwright/test)
• TypeScript
• Page Object Model
• Playwright MCP + agents
• GitHub Copilot
• GitLab CI
• GitLab Pages
• Allure reporting
• AES-256 credential masking

Not ready to book yet? Read another case study — insurance-document automation or legacy platform modernization.