# Stampli cuts launch hours by 68% using ChatGPT Work

Source: [OpenAI](https://openai.com/index/stampli)  
Feed7 permalink: https://feed7.dev/p/stampli-0lomhe7  
Published: 2026-08-20T00:00:00.000Z  
Trust: Official Source (official_source)

## Why Included

Stampli used Codex and ChatGPT Work to cut launch-production hours by 68%, showing how agents can absorb execution work when deadlines and design capacity collide.

## Source Summary

Stampli used **Codex and ChatGPT Work** to produce launch materials under a fixed deadline, cutting launch-production hours by **68%** and compressing **weeks into days**.

## Practical Implication

For agent-assisted launches, define the deadline and production scope clearly, then direct agents toward the work that constrained teammates cannot cover.

## Agent-Ready Context

Stampli used **Codex and ChatGPT Work** to produce launch materials under a fixed deadline, cutting launch-production hours by **68%** and compressing **weeks into days**.

For agent-assisted launches, define the deadline and production scope clearly, then direct agents toward the work that constrained teammates cannot cover.

The material does not describe the workflow, output quality, review burden, or which tasks produced the measured reduction.

## Connected Context

Feed7 judgment across 545 accumulated Signals:

This extends measured agent-assisted time compression beyond engineering maintenance and incident response into deadline-bound launch production. It supports using agents as targeted capacity relief when scope and deadlines are fixed, but the missing task breakdown, quality evidence, and review burden make the 68% reduction a company case rather than a transferable productivity benchmark.

- [Asana cleared 5 years of engineering work in 2 weeks with Codex](https://feed7.dev/p/asana-0pb84hl) — Both report major schedule compression on bounded work, but Asana concerns a test-system migration while Stampli extends the pattern to launch production.
- [NTT DATA Group cuts incident analysis to 30 minutes with Codex](https://feed7.dev/p/ntt-data-1sgidqg) — NTT DATA provides another measured time reduction in a different workflow, reinforcing targeted adoption without showing that either result generalizes.
- [How Forward Deployed Engineering is done at Cognition — Jia Wu](https://feed7.dev/p/how-forward-deployed-engineering-is-done-at-cognition-jia-wu-06h8ybj) — Cognition’s emphasis on accepted work and shorter delivery cycles supplies the outcome standard that Stampli’s headline reduction only partially meets because quality and review burden are undisclosed.

## Context Map

- Layer: industry
- Domains: coding
- Topics: coding-agents, adoption

## Uncertainty

- The material does not describe the workflow, output quality, review burden, or which tasks produced the measured reduction.

## Agent Instruction

Use this item as source-backed context. Do not invent claims beyond the linked source. If this item conflicts with another source, call out the conflict.