WonderBuild for Launch-ready GenAI

GenAI and agentic systems move fast, with new capabilities shipping quickly as teams push to deliver value. That speed can create blind spots when behavior that appeared stable in controlled testing unexpectedly changes once real users interact with a system at scale. This leads to obvious failures and subtle behaviors, including unexpected outputs and multi step interactions that drift away from the originally intended system behavior teams planned. 

Early user interactions shape whether a GenAI product succeeds or fails, and even a few harmful, insecure, or unsafe experiences can quickly erode confidence. When systems behave unpredictably or fall outside expectations, users lose trust not just in the AI product, but in the brand behind it. Rebuilding that trust after launch is far harder than earning it upfront, making strong pre-deployment validation essential.

WonderBuild exists to close the gap between the product team’s intentions and reality ahead of launch with easy-to-use pre-deployment stress testing designed to help teams understand how GenAI models, applications, and agents behave when exposed to real-world and adversarial conditions before those systems reach users.

Your Pre-launch Testing May Not be Enough

Most AI teams already test before launch, running scripted evaluations and platform native checks, and manually reviewing samples to see if anything looks off. Those steps all matter, but they only tell part of the story.

One-time or narrowly scoped evaluations that rely on scripted prompts, limited test cases, or snapshot reviews of model output rarely capture how systems behave when inputs are ambiguous, adversarial, or culturally nuanced. They often don’t reflect how agents make decisions over longer interactions. And they often miss the ways small changes in system prompts or model versions can introduce new vulnerabilities, or revive old ones.

This can leave teams with a false sense of confidence in a system that appears ready because it passed known checks, even though it's never pushed in the ways real users or attackers will inevitably push it.

WonderBuild approaches pre-deployment testing differently, offering adversarial evaluations that simulate realistic user behavior, misuse patterns, and emerging threats. These scenarios are informed by our in-house red teaming expertise and Rabbit Hole, Alice’s adversarial intelligence engine. 

Rabbit Hole draws on years of global research and is built on billions of real world signals to power safety, security, and trust at global scale.

Atop of testing for individual, obvious errors that appear in specific prompts or single interactions — such as a clearly unsafe response, a broken tool call, or a response that violates a known rule — WonderBuild provides insight into how systems behave under pressure, including how they respond to unexpected inputs, handle multi turn conversations, and make decisions across longer agent-driven interactions.

This approach helps teams uncover vulnerabilities that would otherwise stay hidden until after launch, so that issues that affect safety, security, robustness, and expected function can be addressed while there’s still time to fix them.

Built to Gain User Trust in Modern AI Systems Faster

GenAI systems are rarely single-model, single-modality tools anymore, instead, combining text, images, audio, video, and agentic logic across different architectures. Testing needs to account for that complexity.

That’s why WonderBuild is model agnostic and supports multimodal evaluations across text, image, audio, and video inputs. Teams can assess how behavior changes across modalities and how cross-modal interactions introduce new risks.

It also supports multilingual and culturally-nuanced testing. Because behavior that looks acceptable in one language or region may surface risks in another. With WonderBuild, teams can identify those gaps early, which is especially important for products with global reach.

Policy-Aligned By Design

Pre-launch testing must go beyond finding bugs to help teams understand whether a system meets internal, industry, and regulatory standards

With WonderBuild, teams can define highly customizable, policy-aligned evaluation criteria to test specific use cases, risk tolerance, and governance requirements, including alignment with regulations and frameworks like the EU AI Act, ISO 42001, MITRE ATLAS, NIST, and OWASP.

This gives Responsible AI, Security, and Compliance teams structured evidence that testing was performed in a way that maps to real obligations. It also helps product and engineering teams understand which issues matter most and why.

Insights That Move Teams Forward

Finding issues is only useful if teams know what to do once they’re found. WonderBuild delivers clear, prioritized findings that explain what went wrong, where it happened, and how it affects system readiness. Instead of overwhelming teams with noise, it highlights the risks most likely to impact deployment timelines, user trust, or governance outcomes.

WonderBuild doesn’t operate in isolation. Insights integrate into existing development and ticketing workflows with no code overhead so that teams can move from discovery to remediation without slowing down. And, findings from pre-deployment testing can flow into WonderFence and WonderCheck to support ongoing protection and evaluation once systems go live.

That continuity matters because risks evolve alongside systems after launch. By starting with structured, adversarial testing in WonderBuild, teams create a stronger foundation for responsible, secure AI operations across the full lifecycle.

Ship With Confidence

Releasing probabilistic AI systems will always involve a degree of uncertainty. WonderBuild gives teams the breadth and depth of evaluation and insight required to understand how their systems behave before they reach users, turning assumptions into evidence and scrambles into informed decisions.

This gives product leaders confidence at launch. For engineers, it provides clarity on where to focus fixes. For security, compliance, and Responsible AI teams, it produces evidence  grounded in real-world adversarial testing, not just best intentions.

And for users, WonderBuild helps shorten the path to trust by better ensuring their day-one experience is the incredible, trustable experience you intended it to be.

‍