Document Processing · Enterprise Knowledge Search
Stampli needed to launch complex new products and keep product knowledge current without waiting on scarce design, contractor, and operations bandwidth.
Reported impact
Stampli compressed launch production from weeks into days, multiplied the output of a small marketing team, and gave employees more time for strategy by reducing the effort needed to gather context and create polished assets.
Stampli combined Codex and ChatGPT Work to turn product marketing into a connected operating system rather than manual content tasks. For the Deep Finance launch, the team fed product context, meeting notes, decisions, and messaging guidelines into Codex so it could create review-ready assets across launch content, web pages, enablement collateral, and most of the hero animation work. The same foundation also powers everyday product-marketing operations: a GPT-driven system pulls from product systems and meeting notes to keep help-center content, presentations, one-pagers, and other materials current, while connected agents generate website and social content at higher volume than before. ChatGPT Work adds a real-time assistant layer for meeting prep, strategic thinking, and live data retrieval during executive discussions. Instead of reconstructing context, the team uses AI to stay current, surface insights, and move faster from evolving product decisions to market-ready output.
Product decisions, meeting notes, messaging guidelines, and system data are routed into Codex and Stampli's GPT-powered system. Those tools create or refresh launch assets and knowledge materials, while ChatGPT Work brings the resulting context into day-to-day meetings and executive decisions.
"3.16x Faster launch to production with Codex"
"ChatGPT Work also helps employees bring relevant business context into important decisions"
"With OpenAI tools, they compressed an estimated 243 hours of production work into about 77"
"it's multiplied the output of a small team by 10x"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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