We Build AI
Quality agents,
crafted with intent.

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.

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.

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.

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.

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.

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.

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.

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

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

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

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