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Testing
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Neeva

AI-powered QA agent that builds a living model of your product, writes tests in plain English, and self-heals when UI changes.

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Overview

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 — 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 — 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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Key Features

Self-Healing Engine with Long-Term Memory+

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 — Product Intelligence Dashboards+

AutoBoards translate raw test runs into product-level KPIs: Quality Score (e.g., 94%, +3% this sprint), Release Risk (Medium — 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.

Plain-English Test Authoring+

Tests are written as natural-language YAML scenarios like "User can complete checkout with saved card" or "Admin can view analytics dashboard" — 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.

Product Digital Twin+

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.

PR-Linked Regression Correlation+

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.

Pricing Plans

Enterprise

Contact Sales

  • ✓Self-healing engine with long-term memory
  • ✓Plain-English test authoring (no scripts or selectors)
  • ✓Product Digital Twin with auto-discovery of flows from PRs
  • ✓AutoBoards dashboards (Quality Score, Release Risk, Coverage Delta)
  • ✓PR-linked regression correlation
  • ✓Visual assertions and auto-maintenance
  • ✓Flaky flow detection and coverage gap tracking
  • ✓SOC-2 compliance (verify current attestation with sales)
  • ✓Dedicated onboarding and support
  • ✓Custom integrations with CI/CD and source control
See Full Pricing →Free vs Paid →Is it worth it? →

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Best Use Cases

đŸŽ¯

QA teams at fast-growing SaaS companies whose UI changes frequently and who are tired of rewriting selectors after every release — Neeva's memory-based self-healing is designed exactly for this churn

⚡

Product managers and CTOs who want a single dashboard (AutoBoards) showing Quality Score, Release Risk, and Coverage Delta across sprints, rather than parsing raw CI test logs

🔧

Engineering orgs adopting trunk-based development with frequent PRs, where Neeva's Product Digital Twin can auto-discover new flows from each PR and suggest test scenarios before they ship

🚀

Cross-functional teams where non-engineers (PMs, designers, support) need to author tests — plain-English syntax like "Admin can view analytics dashboard" lowers the barrier to entry

💡

Mature SaaS products with a long history of flaky end-to-end tests, where Neeva's correlation engine can link regressions to specific PRs and shrink root-cause time

🔄

Enterprise buyers replacing in-house Selenium/Playwright frameworks that consume disproportionate engineering hours on test maintenance rather than feature development

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Neeva doesn't handle well:

  • ⚠Pricing and tier structure are not public; teams cannot self-serve and must engage sales to evaluate the platform
  • ⚠No published support for native mobile apps, desktop apps, or APIs — the digital-twin model appears web-product centric based on the marketing examples (checkout flows, analytics dashboards)
  • ⚠Documentation of integrations, supported browsers, and CI/CD plugins is not exposed on the public landing page
  • ⚠Memory-based healing requires accumulated run history to deliver compounding value, so brand-new deployments will not see immediate benefits
  • ⚠No third-party reviews, analyst reports, named customer references, or independently verified case studies were found on the public site or major review platforms at the time of enrichment — all product claims are sourced from the vendor's own landing page
  • ⚠Domain provenance risk: the neeva.ai domain previously belonged to an unrelated company (Neeva search engine, acquired by Snowflake in 2023); the current operator is a different entity, and the URL may not persistently host this product
  • ⚠No documented free trial, freemium tier, or self-serve onboarding path — evaluation requires scheduling a sales demo

Pros & Cons

✓ Pros

  • ✓Memory-based self-healing remembers why a test failed last time and applies the fix automatically, reducing repeat maintenance work compared to selector-retry approaches used by most of the testing tools in our directory
  • ✓Plain-English test syntax (e.g., "User can complete checkout with saved card") removes the need for QA engineers to write or maintain selectors or scripts
  • ✓AutoBoards translate raw test results into product-level KPIs like Quality Score, Release Risk, and Coverage Delta — useful for PMs and CTOs, not just QA leads
  • ✓Product Digital Twin auto-detects new flows from pull requests (the vendor's landing page shows it suggesting 3 new test scenarios from a sample PR) and expands coverage without manual authoring
  • ✓Correlates regressions to specific PRs (per marketing demo examples), shortening root-cause investigation
  • ✓Positioned for fast-growing companies and trusted by multiple teams referenced on the landing page, though no named customer logos or third-party reviews are publicly visible to corroborate these claims

✗ Cons

  • ✗No public pricing — access is gated behind "Book a Demo" or "Request Access," making it impossible to evaluate cost without a sales conversation; based on category comparables, expect enterprise-tier pricing significantly above the $50–$150/month range offered by self-serve competitors
  • ✗Enterprise-only positioning likely puts it out of reach for solo developers, indie hackers, and early-stage startups with no budget for premium QA tooling; no free tier, free trial, or freemium plan is documented
  • ✗No published list of supported integrations, frameworks (Playwright, Cypress, Selenium), or CI/CD providers on the public site
  • ✗Compliance posture (SOC-2, credential storage) is only referenced as FAQ topics without published documentation or trust-center links
  • ✗Heavy reliance on the digital-twin model means teams with non-standard architectures (native mobile, complex desktop apps) may not see the same self-healing benefits as web SaaS products
  • ✗Domain provenance concern: neeva.ai previously belonged to an unrelated AI search engine acquired by Snowflake in 2023; the current QA product has no disclosed connection to the former entity, and no third-party reviews, analyst coverage, or independent case studies were found to corroborate vendor claims

Frequently Asked Questions

How is Neeva different from other AI testing tools?+

Neeva differentiates on two axes: long-term memory and product intelligence. Most AI testing tools self-heal by retrying with new selectors on each run, but Neeva persistently remembers why a previous failure occurred and what fixed it, applying that knowledge to all future runs. It also goes beyond pass/fail reporting via AutoBoards that surface a Quality Score, Release Risk, and Coverage Delta — turning test runs into product-health signals. Compared to the other testing tools in our 870+ tool directory, Neeva targets teams that want QA to inform product decisions, not just gate deploys.

What is self-healing with memory?+

Self-healing with memory means that when Neeva encounters a failing step — for example, a click on "Submit Order" that no longer exists — it first checks its long-term memory for similar past failures. If the same button was previously renamed to "Place Order" in v2.3 (per the vendor's marketing example), Neeva applies that learned mapping automatically and updates its memory so future runs use the new label. This is fundamentally different from stateless selector-retry healing, because the system gets smarter with every failure rather than re-solving the same problem each run. The result is compounding test resilience over time. Note: these examples are sourced from the vendor's landing page and have not been independently verified.

Are you SOC-2 compliant?+

Neeva lists SOC-2 compliance as an FAQ topic on its landing page, indicating they address this requirement for enterprise buyers, but full compliance documentation is not publicly published. Prospective customers should request the current SOC-2 report (Type I or Type II) and any related security questionnaires directly during the demo or procurement process. Given the enterprise positioning and fact that Neeva ingests test scenarios that may include sensitive flows, verifying compliance status before granting production access is recommended. Always confirm the latest attestation date with the Neeva sales team.

Do you store test credentials?+

Credential handling is listed as an FAQ topic on the Neeva landing page but the public-facing answer is not exposed in the scraped content. Most enterprise-grade QA platforms support encrypted secret storage with role-based access, and Neeva's enterprise positioning suggests similar capabilities. Buyers evaluating Neeva for production workflows should ask specifically about encryption at rest, secret rotation, and whether credentials are stored in their environment or Neeva's. This is especially important for teams testing flows behind authentication walls or processing payment data.

What tools does Neeva integrate with?+

Neeva references integration as an FAQ topic but does not publish a specific integration matrix on its public landing page. Based on its product model — which the vendor's demo shows auto-detecting new flows from pull requests and correlating regressions to specific PRs — Neeva likely integrates with source-control systems (GitHub and similar) for PR-driven test discovery. Prospective buyers should request an up-to-date list of supported CI/CD providers, issue trackers, and observability tools during the demo. The lack of a public integration list is a common pattern for enterprise-gated tools.

Is this the same Neeva as the former AI search engine?+

No. The original Neeva was an ad-free, AI-powered search engine founded by ex-Google executives, which shut down its consumer search product and was acquired by Snowflake in May 2023. The current neeva.ai domain hosts a separate AI-powered QA testing platform with no disclosed connection to the former search engine or Snowflake. Because the domain has changed hands, buyers should confirm the operating entity and corporate history directly with the current Neeva sales team during evaluation, and be aware that the URL may not persistently host this product.

How much does Neeva cost compared to alternatives?+

Neeva does not publish pricing, but the competitive landscape provides useful benchmarks. Self-serve AI testing tools like Reflect start around $25–$49/month for small teams, and Testim (now part of Tricentis) offers tiers starting under $100/month. Mid-market platforms like Mabl typically range from $200–$500/month depending on test volume. Fully managed services like QA Wolf start around $3,000–$5,000/month. Given Neeva's enterprise-only positioning, demo-gated access, and feature set (AutoBoards, Digital Twin, memory engine), pricing likely falls in the $500–$2,000+/month range for mid-market buyers, with custom enterprise contracts for larger orgs. Prospective buyers should request a quote during the demo and ask about annual vs. monthly billing, per-seat vs. per-test pricing, and any available pilot or proof-of-concept programs.
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What's New in 2026

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 2025–2026 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.

Alternatives to Neeva

mabl

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Functionize

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Enterprise AI test automation platform with QA agents for automated software testing.

QA Wolf

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Fully managed automated QA testing service that uses Playwright-based AI agents to write, maintain, and run end-to-end regression tests in parallel across web, iOS, and Android applications with zero-flake guarantee and CI/CD integration.

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Quick Info

Category

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Website

neeva.ai/
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