Stampli is a procure‑to‑pay platform that links procurement, accounts payable, vendor management, payments, and employee spend. Its Deep Finance™ product converts the data that flows through Stampli into executive spend intelligence for CFOs, VPs, and other business leaders. Launching Deep Finance required coordinated work across product development, positioning, design, communications, enablement, and operations on a fixed timeline, while design resources and external contractors were already assigned to other priorities.
Compressing production with GPT tools
Stampli’s marketing team used OpenAI Codex and ChatGPT Work to centralize product context, meeting notes, decisions, and messaging guidelines into a shared, machine‑assisted system. Using these tools, the team estimates it reduced the modeled active production time for the launch from about 243 hours to roughly 77 hours — a savings of approximately 166 hours, or a 3.16× faster production pace — all while retaining human review and final approval for customer‑facing content.
Deep Finance moved from an initial prototype demo to a public go‑to‑market (GTM) launch and first shipped product in about six weeks. Because design resources and outside contractors were tied to other projects, the team relied on Codex to convert evolving product decisions into review‑ready assets across a seven‑part blog series, launch emails, a webinar and supporting deck, social and paid creative, a PR Newswire release, the Deep Finance web page, and sales enablement materials. Codex also assisted in creating the launch’s hero animation: through exploration, iteration, and packaging it handled roughly 90% of the polished animation work before a contractor finished the opening scene and final format.
Day‑to‑day automation and scale
The infrastructure used for the Deep Finance launch is the same GPT‑powered system Stampli’s product marketing team uses daily. Previously, keeping product materials current required interviewing product managers, reading Jira tickets, reviewing GitHub, and processing meeting notes, then translating that information into help center articles, presentations, and one‑pagers.
Stampli automated much of this with a GPT system that gathers information from product systems and meeting notes and helps keep materials up to date. These automations also enable larger‑scale content production: agents connected to the company’s source of truth can produce material for the website and social channels.
Melad Zahedi, Director of Product Marketing, said: “it’s multiplied the output of a small team by 10x, putting out hundreds of pieces of content on a weekly basis, where it was limited to just a couple before.”
Faster decisions and meeting efficiency
ChatGPT Work also helps employees bring relevant business context into important decisions. Zahedi uses GPT‑powered automations as a “second brain” to organize information across a schedule filled with back‑to‑back meetings. “Being able to go to every meeting prepared with context, understanding what is needed from me in that meeting and how to stay efficient with my time, has been an amazing benefit,” he said.
In one executive meeting, a question came up about metrics stored across HubSpot and other systems. An employee asked Codex to retrieve and analyze the relevant data during the call. According to Zahedi: “This is something that would’ve taken our FP&A team half a day to put a report together, give us a model, and give us an answer that we felt confident in. Someone was able to do it with 20 seconds of keystrokes.”
The time saved allowed product marketing to spend less effort reconstructing context and more time advising leaders on product and company strategy. The team also became more involved in advising VPs and C‑suite leaders on corporate and product strategy.
A thought partner and learning tool
ChatGPT Work serves as a thought partner for product marketing: employees use it to deepen their knowledge, brainstorm ideas, create stakeholder personas, and test recommendations before presenting them to leadership.
Teams across product, marketing, customer success, sales, and enablement used ChatGPT Work and Codex to take the idea from prototype to launch. Zahedi estimates that with OpenAI tools the whole process took about six weeks, whereas it previously would have taken months or even quarters.
Next steps
The Deep Finance launch demonstrated to Stampli what a more connected, AI‑supported product lifecycle can look like. The company is now exploring how to apply this approach across more products, workflows, and go‑to‑market programs.
For Zahedi, the broader opportunity is expanding what employees believe they can accomplish: ChatGPT Work and Codex have helped his team learn new disciplines, build its own systems, and spend more time on decisions that move the company forward.
Key numbers and facts:
- Deep Finance’s GTM and first shipped product were completed in about six weeks.
- Modeled active production hours without Codex: ~243 hours; with Codex: ~77 hours — ~166 hours saved (~3.16× faster).
- Codex handled roughly 90% of the preparatory animation work.
- The marketing team reports roughly a 10× increase in output; the team now produces “hundreds” of content pieces weekly versus only a few previously.
