What is AI quality engineering?

AI quality engineering is the discipline of measuring, testing and governing AI systems before and after they ship: evaluation pipelines, hallucination detection, safety benchmarks, regression testing and production monitoring so enterprise AI stays reliable.

Do you offer digital transformation consulting?

Yes. OpenCrevo is an AI transformation consultancy. We embed alongside your team for strategy, tooling and hands-on delivery, from first audit to scaled cloud-native operation, not staff augmentation alone.

What happens in an AI transformation consultation?

It's an implementation-focused working session with a senior engineer, not a sales call. We review your current AI systems, rank failure modes by severity, and hand you a build-ready transformation roadmap grounded in open-source tooling from OpenCrevo you can inspect and own, plus a scoped path through Quality Engineering, Platform Evaluation, or the Advocacy Program if you want us to implement it with you. No retainer required to receive it.

How quickly can we start?

Most engagements start within 2–3 weeks of a signed brief. A 30-minute call is enough to know whether we're a fit.

Yes. Send us yours, or we'll send ours. Mutual NDAs are turned around within a day.

What is a Playwright MCP alternative?

A Playwright MCP alternative is a governed testing control plane that still uses Playwright under the hood, but adds test orchestration, auditability, and self-hosted enterprise QA. OpenCrevo's software quality factory coordinates Playwright CLI, Playwright agents, and MCP-style browser control without leaving raw agent sessions ungoverned.

Do you support Playwright CLI and Playwright agents?

Yes. Teams keep Playwright CLI for deterministic CI execution and Playwright agents for authoring and exploration. OpenCrevo sits above both as the AI-native testing control plane: the enterprise layer for software testing modernization, test governance, and a production-ready Playwright MCP alternative.

What is an AI-native testing control plane?

It is the system that centralizes the testing lifecycle: intent, orchestration, execution, incidents, and reporting. For enterprises modernizing software testing, that means one software quality factory for Playwright CLI, Playwright agents, and self-hosted QA, not a pile of disconnected MCP servers.

What AI frameworks do you support?

We work across OpenAI, Anthropic, open-source LLMs and custom model architectures, including RAG pipelines and agentic AI stacks. Stack-agnostic by design.

What if our system isn't production-ready yet?

Good. That's what discovery is for. Most clients arrive with a quality problem; we leave week one with a written framework.

How do you measure quality?

Every engagement starts with agreed metrics: reliability rates, hallucination frequency, latency thresholds or safety scores. Live dashboards and monthly reviews included.

What's in the discovery phase?

A review of your current system, failure modes ranked by severity, and a quality roadmap. No obligation, no hard sell.

Playwright MCP Alternatives for QA Teamstest governancePlaywright agents

Playwright MCP Alternatives for QA Teams: Gates, Scope, Audit

12 September 2026 · OpenCrevo

Playwright MCP alternatives for QA teams are process, not another server. MCP gives an agent a live browser. That is fine for explore. It is a governance hole if the same agent rewrites locators and merges itself. Lock path scope, a human merge, and a one-line audit. See introduce AI into QA (/blog/introduce-ai-into-qa-without-black-box/) and the pillar on best Playwright MCP alternatives (/blog/best-playwright-mcp-alternatives/).

The failure: nobody owns the locator rewrite

An agent on Playwright MCP "fixes" a failing Pay now test by loosening the assertion to toBeVisible on the whole form. The PR auto-merges. Finance later sees double charges. The audit question — who approved the weaker assert — has no answer.

Playwright MCP alternatives for QA teams

Agent may write tests/agent/** only. No playwright.config.ts, no .github/workflows.
MCP is explore-only. The merge gate is npx playwright test.
Every agent PR gets label ai-authored and a named reviewer.
Reject status-only asserts. Same bar as testing AI generated code.

## Agent PR template
- Prompt / ticket:
- Files touched (must be tests/agent/**):
- MCP used? yes/no (session discarded: yes)
- Reviewer:
- Contract assert (business number, not HTTP 200):

# .github/CODEOWNERS
/tests/agent/  @qa-leads
/playwright.config.ts  @qa-leads
/.github/workflows/  @qa-leads

If the team needs this as a factory with auditability, use the Enterprise page or AI quality engineering services. Check your current governance gaps with the free QA maturity assessment before scoping the rollout.

Pass, fail, cleanup

Pass: An agent PR that edits workflows is blocked by CODEOWNERS. A weaker assert cannot merge without a named reviewer.
Fail: MCP is still in the required check, or ai-authored PRs auto-merge.
Cleanup: Revoke repo-wide agent write. Keep explore on a throwaway branch.

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Playwright MCP Alternatives for QA Teams: Gates, Scope, Audit

The failure: nobody owns the locator rewrite

Playwright MCP alternatives for QA teams

Pass, fail, cleanup

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