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How Amplitude ships product improvements in 1–2 days with OpenAI

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How Amplitude ships product improvements in 1–2 days with OpenAI

Built primarily on OpenAI models, Amplitude’s proactive product agent, Wave, identifies what to build, helps teams ship changes with coding agents, and measures the impact.

Lower mean model cost per dispatch for GPT‑6.1 Sol in Amplitude’s Data Science Agent evaluation.

Amplitude has always helped teams see how people actually use their products. Its proactive product agent Wave helps teams turn those insights into shipped improvements. Amplitude describes Wave as roughly 80% powered by OpenAI models, including GPT‑6.1 Sol, GPT‑6 Luna, and GPT‑Image‑2.5 Flare. Wave turns product insights into implementation plans teams can ship to coding agents, including Codex, then measures the impact.

Amplitude estimates that Wave can help teams ship product improvements in 1–2 days, down from roughly a month. On Amplitude’s own homepage, Wave helped personalize suggestion cards, driving a 160% lift in card-driven sends.

Wave is Amplitude’s proactive product agent. Teams give it a product area and the goals and metrics they want to improve. Wave brings together Amplitude data, session replays, customer feedback, and code to identify and prioritize opportunities, develop solutions, and coordinate implementation with people and coding agents. It then tracks outcomes and uses those learnings to inform the next round of improvements.

Behind that workflow, Amplitude uses different OpenAI models for different tasks. GPT‑6.1 Sol supports the opportunity manager and deep investigations into code and data. GPT‑6 Luna handles frequent, lower-cost tasks such as scanning code and running small evaluation checks. GPT‑Image‑2.5 Flare generates product mockups using screenshots from session replays as visual references.



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