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title: How Amplitude ships product improvements in 1–2 days...
description: Amplitude's product agent Wave, roughly 80% powered by OpenAI models, helps teams ship product improvements in an estimated 1-2 days instead of a month. Wave...
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# How Amplitude ships product improvements in 1–2 days with OpenAI

**[OpenAI](https://daily.dev/sources/openai)** · 6 min read · 1 upvotes · 0 comments

## Summary

Amplitude's product agent Wave, roughly 80% powered by OpenAI models, helps teams ship product improvements in an estimated 1-2 days instead of a month. Wave combs through analytics, session replays, customer feedback, and code to find opportunities, then hands technical plans to coding agents like Codex. On Amplitude's own homepage, personalizing suggestion cards drove a 160% lift in card-driven sends after one day. Amplitude uses GPT-6.1 Sol for deep investigations, GPT-6 Luna for cheap frequent checks, and GPT-Image-2.5 Flare for mockups; GPT-6.1 Sol scored 4.06/5 on its Opportunity Investigator evaluation versus 3.62, 3.52, and 2.94 for alternatives.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://openai.com/index/amplitude>

## Questions this post answers

### How long does it take Amplitude's Wave agent to ship a product improvement using OpenAI models?

Amplitude estimates Wave can help teams ship a product improvement in 1 to 2 days, down from roughly a month. Wave identifies opportunities from session replays, analytics, and code, builds a technical plan, and hands it to coding agents such as Codex. Among customer-launched experiments, one in four went live the same day the opportunity was surfaced, and two in three within a week.

_daily.dev tracks agentic workflow case studies like this for teams measuring real shipping speed gains._

### How does GPT-6.1 Sol compare to other models for AI agent code investigation tasks?

GPT-6.1 Sol scored 4.06 out of 5 on Amplitude's Opportunity Investigator evaluation, ahead of three alternative models that scored 3.62, 3.52, and 2.94. It led on all five investigation criteria measured (corroboration, false positive avoidance, incorporating evaluator feedback, recovering from tool failures, and evidence alignment) and had the fastest median investigation time at 54 minutes.

_developers comparing reasoning models for agent tasks follow benchmarks like this on daily.dev._

### What OpenAI models power Amplitude's Wave product agent and what does each one do?

Wave is roughly 80% powered by OpenAI models: GPT-6.1 Sol handles the opportunity manager and deep investigations into code and data, GPT-6 Luna handles frequent low-cost tasks like scanning code and running small evaluation checks, and GPT-Image-2.5 Flare generates product mockups using screenshots from session replays as visual references.

_teams architecting multi-model agent pipelines track real deployments like this one on daily.dev._

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#openai](https://daily.dev/tags/openai), [#openai-codex](https://daily.dev/tags/openai-codex)

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