AI-powered QA agent that builds a living model of your product, writes tests in plain English, and self-heals when UI changes.
Neeva is a Testing AI agent that builds a living digital twin of your product, writes tests in plain English, and self-heals failing tests using long-term memory of past failures, with pricing available on an enterprise basis through demo request. It is designed for engineering, QA, and product teams at fast-growing SaaS companies that need test automation to keep pace with rapid UI iteration.
IMPORTANT â Domain provenance notice: The neeva.ai domain was previously associated with Neeva, an AI-powered search engine founded by ex-Google executives, which shut down its consumer product and was acquired by Snowflake in May 2023. The current neeva.ai site hosts an unrelated AI QA testing product that launched on the domain after the search engine's shutdown. There is no disclosed corporate relationship between the current operator and the former Neeva/Snowflake entity. Because domain reuse creates verifiability risk, buyers should independently confirm the current operating entity, its incorporation details, and its corporate history before entering a procurement process. The product described in this record reflects landing-page claims observed during enrichment; if the domain changes hands again, this record may no longer reflect the site's content.
All product claims below (version numbers, PR examples, feature descriptions) are sourced from the vendor's own landing page and have not been independently verified through third-party reviews, analyst reports, or published case studies. No customer logos, named references, or quantified ROI figures were visible on the public site at the time of enrichment.
Unlike traditional test automation tools that rely on brittle selectors and require constant maintenance, Neeva positions itself as "product intelligence, not just test results." Its core differentiator is a memory-augmented self-healing engine: when a test fails (for example, because a "Submit Order" button was renamed to "Place Order" in v2.3, per the vendor's marketing example), Neeva remembers the fix and applies that learned context to all future runs. This persistent memory is paired with a Product Digital Twin that auto-discovers user flows, suggests new test scenarios on every pull request, and tracks coverage gaps in real time.
The platform is organized around three pillars: plain-English test authoring (no scripts, no selectors, with visual assertions and auto-maintenance), AutoBoards that surface release risk and quality scores for PMs and CTOs, and a continuously updated product model that ingests every release. Based on our analysis of 870+ AI tools in the Atlas directory, Neeva sits in a competitive testing category alongside tools like Testim, Mabl, Functionize, and QA Wolf â but its emphasis on long-term memory and product-level intelligence dashboards (rather than per-test selector retry logic) is genuinely differentiated. Compared to the dozens of other AI testing tools we've cataloged, Neeva is best suited for teams that want QA outputs to inform product decisions, not just gate deploys. Buyers should note that Neeva is currently access-gated (Book a Demo / Request Access), pricing is not publicly listed, and SOC-2 compliance is referenced in the FAQ but should be verified directly with the sales team.
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When a step fails, Neeva consults its memory of past failures before retrying. The vendor's marketing example shows a failed "Click Submit Order" being auto-healed after the engine recalls that the button was renamed to "Place Order" in v2.3, then writing that mapping back to memory so all future runs use it. This compounds test resilience over time rather than re-solving the same UI churn on every run. Note: this example is from the vendor's landing page and has not been independently verified.
AutoBoards translate raw test runs into product-level KPIs: Quality Score (e.g., 94%, +3% this sprint), Release Risk (Medium â 2 flaky flows), and Coverage Delta (+12 flows since last deploy). The vendor's demo also shows regression correlation to specific PRs. This makes test output legible to PMs and CTOs, not just QA engineers. These metrics are from vendor marketing materials and have not been corroborated by third-party reviews.
Tests are written as natural-language YAML scenarios like "User can complete checkout with saved card" or "Admin can view analytics dashboard" â no selectors, no scripts. Neeva's AI agents handle execution, visual assertions, and ongoing maintenance automatically. This lowers the authoring barrier for non-engineers and eliminates the brittle CSS/XPath selector layer that typically drives flakiness.
Neeva builds a continuously updated model of your product (the vendor's demo shows a "Live Product Model v2.4.1" label, though version numbering and sync frequency have not been independently verified). It auto-detects new flows from PRs and suggests new test scenarios while expanding coverage. It also surfaces product insights like increased user-journey complexity and bottleneck detection.
Beyond reporting failures, Neeva ties regressions back to the specific PRs that introduced them, per the vendor's marketing examples. This shortens triage time from hours of bisecting to a one-click jump to the offending change. The specific PR numbers shown on the landing page are illustrative marketing examples and have not been verified against a real repository.
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As of April 2026, Neeva's public-facing site continues to position the platform around its core pillars: memory-augmented self-healing, Product Digital Twin, and AutoBoards dashboards. No public changelog, versioned release notes, or blog posts documenting specific 2025â2026 feature launches were identified during enrichment. The landing page references Product Model v2.4.1, suggesting ongoing iteration, but specific release dates and feature additions are not disclosed publicly. Prospective buyers should request a product roadmap and recent release history during the demo process to assess development velocity.
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