Pricing
Virtuoso writes tests in plain English, heals them when the UI shifts, and runs them on an engine that is exact, not probabilistic
Turn any specification into requirements, user journeys and executable tests in minutes, with AI proposing every step and a human approving the results.
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Enterprise security and compliance
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Advanced Technology Partner
Strong Performer 2024
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Rated 4.5 out of 5 by customers on G2
Most vendors describe their AI in adjectives.
Here's ours in mechanisms, and exactly when each one is live.
Write “search for a policy, open the latest claim, expect status Approved.” Virtuoso compiles it into exact browser actions. The barrier to authoring is knowing the business, not knowing Selenium.
Your documents, Jira, and Confluence are ingested, cited, and scoped per project, so every proposal is traceable to the source it came from. We treat context as something engineered, not assumed. An agent is only as good as the context it reasons over, and we pay disproportionate attention to that.
When the application changes, AI re-identifies elements and adapts the test. Below the confidence line it fails instead of guessing, because a test that quietly papers over a real defect is worse than a broken one. Every healing decision is logged.
From that context, AI proposes structured requirements and journeys, surfaced as diffs you review and approve. Approved journeys are materialised into runnable tests. No three-week documentation project first: it starts from what you have and tells you what is missing.
The AI reasons. The Virtuoso Test Bot executes: exact, repeatable, the same result every run. AI agents driving probabilistic execution is flakiness with better marketing.
When a test needs fixing, AI proposes the repair with traceability to the offending step. A human approves. The journey regenerates and reruns.
85%
Hours
5,000+
147
AI now ships software faster than traditional QA can verify it. As change accelerates, trust becomes the bottleneck unverified tests.
Autonomous QA closes the verification gap with AI that proposes, repairs, and executes tests deterministically, while every decision remains transparent, governed, and human-approved.
Scripts
Hand-written automation that broke on every UI change. Coverage was a function of headcount.
Automation in the pipeline
Tests ran in CI/CD, fast but shallow. The pipeline moved. The understanding stayed behind.
Governed Autonomous QA
AI proposes. A deterministic engine runs. Humans approve. Every decision on the record.
Hold any platform, including ours, to four tests.
Does it own the whole loop?
Specification to requirements to journeys to runnable tests to execution to failure to repair to rerun. One continuous system, versioned at every step. Not agents stitched across a portfolio of acquired products, where the gaps between tools are where defects live.
Does AI reason while a deterministic engine executes?
AI should decide what to test and how to repair. The running itself must be exact and repeatable, and below a confidence threshold it must fail, never guess. Ask any vendor what their agent does when it is not sure. The honest answer is usually “it picks the most likely option.” Ours fails loudly and tells you why.
Is the human-AI boundary in the product, not on a slide?
Approval gates by default. Reviewable diffs for every proposal. A full audit trail of every agent action. Auto-accept that is narrow, scoped, and off by default. “Human in the loop” is a checkbox; a documented boundary is architecture.
Can you inspect the context the AI reasoned over?
Every proposal should cite the document, ticket, or run it came from. An agent is only as trustworthy as the context engineered into it. If a vendor cannot show you what their AI read before it acted, it is guessing.
The execution engine already runs in production today. We are closing the loop end-to-end by September 2026. Nothing in this loop is a black box. Every change versioned. Every rerun explainable.
Once shipped, here's what the complete loop changes for each team.
Stop running a maintenance team. Design the quality strategy while the loop handles generation, execution, and repair.
Report risk covered, not scripts counted, in language the board understands.
Tests that keep pace with sprint velocity instead of becoming the backlog.
Regressions caught in the pipeline, before review, not after production.
Context held in the platform, so new engineers inherit years of it on day one.
Acceptance criteria become verifiable gates traced to your user stories.
Know within hours whether a build behaves as intended, not after a week of manual cycles.
Every test links to the rule, policy, or requirement it satisfies.
Evidence produced as you run, not assembled under audit pressure. The AI's work is explainable, cited to its source, because “the AI did it” is not an audit answer.
UK film and television studio
A global insurer on Salesforce
Business systems, the custom applications around them, and the partner-built and ISV products on top. One platform, one loop, one plain-English syntax everywhere. Not a portfolio.
Book a walkthrough
You do not need a finished knowledge base to begin. The AI starts from what you have and tells you what is missing.
A session with your team. First tests running the same day.
More journeys, more coverage, repeatably.
Centralised, cited knowledge and governance become a shared asset.
Coverage you can see. Quality that improves every release.
The launch of Virtuoso's Touchstone introduces agentic testing with AI agents that propose requirements, generate test journeys, classify failures, suggest repairs, and rerun tests, all with human approval built into the workflow.
Now in Beta with selected customers and partners.
Be among the first to experience Virtuoso Touchstone Beta.
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