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Zypact

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Zypact is an evidence-backed AI buyer-question testing platform for B2B SaaS teams. Zypact helps product marketing and go-to-market teams test a specific commercial question: after we change one thing, does the same AI buyer answer meaningfully change? Instead of continuously tracking hundreds of prompts or producing a broad AI visibility score, Zypact starts with one commercially important buyer question — for example, a question a buyer might ask while comparing software vendors. How Zypact V1 works Approve one buyer question and measurement protocol. The question, Perplexity Sonar configuration, sampling parameters, limits, decision rules, and observation window are approved before execution. Capture a controlled baseline. Zypact runs the approved question on Perplexity Sonar, preserves the raw responses, sources, timestamps, protocol metadata, and relevant competitor observations. Preserve an auditable evidence trail. Teams can inspect the exact AI answer, cited sources, competitor frame, protocol version, and integrity state instead of relying only on an aggregate visibility score. Register one intervention. A single real-world change — such as new positioning, stronger product evidence, a comparison page, proof content, or another approved change — is documented with an owner, hypothesis, implementation proof, and observation window. Run a same-question retest. After the observation window, Zypact asks the same buyer question again under the approved protocol and compares the new evidence with the baseline. Document the outcome. The result is recorded as Increase observed, Decrease observed, No clear change, or Inconclusive, with limitations and confounders preserved. Who Zypact is for Zypact V1 is designed primarily for B2B SaaS Product Marketing leaders, CMOs/VPs of Marketing, Growth leaders, and founders who need evidence around positioning, competitive comparison, buyer research, category messaging, or a specific change intended to affect how an AI system represents their product. It is particularly relevant for competitive SaaS categories where buyers use AI to research or compare vendors and where the company can implement a specific positioning, evidence, or content change and later retest it. Current V1 scope Zypact V1 uses Perplexity Sonar only. It does not claim multi-engine monitoring, a market-wide visibility score, guaranteed recommendations, causal attribution, automatic revenue attribution, or continuous prompt monitoring. Zypact is built around a narrower question: “We changed one thing. Did the same buyer question meaningfully change under the same protocol?”

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