Summary: Stampli used ChatGPT Work and Codex to take Deep Finance from prototype demo to public go-to-market launch and first release in about six weeks. Stampli estimates the modeled workload at 243 active role-hours without Codex versus about 77 hours with it, a rough saving of 166 hours and 68% less production time. The company still requires full human review for customer-facing work, and the estimate is not a controlled experiment.
Six Weeks from Prototype to Launch
Stampli sells an intelligent platform for procure-to-pay, and the product it recently took to market, Deep Finance™, converts procurement data and accounts payable records into executive spend intelligence aimed at CFOs and business leaders. The launch ran on a stopwatch. Prototype demo, then the public go-to-market (GTM) launch and the first shipped release, all inside about six weeks—a sharp break from the months, or quarters, a launch of this kind normally eats up.
Speed came from running everything at once with OpenAI’s ChatGPT Work and Codex: product development and positioning, design and communications, enablement and operations. Outside contractors and design resources were already committed elsewhere, so Codex became the route from evolving product decisions to review-ready assets. Those assets included launch emails, a seven-part blog series, a webinar with its supporting deck, paid and social creative, a release on PR Newswire, the Deep Finance web page, and the sales enablement material. Codex even took part in the hero animation for the launch, doing roughly 90% of the polish before a contractor handled its opening scene and the final format.
The numbers came with evidence behind them. Stampli’s estimate puts the same launch without Codex at 243 modeled active role-hours; with Codex the same work came in at about 77 hours. That means roughly 166 hours saved and production that ran 3.16x faster—a 68% cut in the time the work consumed. Everything customer-facing still gets human review in full and a final sign-off.
The Same System, Running Every Day
The machinery behind Deep Finance was not assembled for the occasion—it is what the product marketing team at Stampli already works in day to day. Keeping product materials current used to be manual: sit with product managers, read the Jira tickets, review GitHub, digest the meeting notes, then turn all of it into help center articles, one-pagers, presentations, and similar assets.
Now a GPT-powered system does that gathering, drawing on meeting notes and the product systems, and those materials stay fresh as a result, so nobody has to dig for product knowledge by hand.
Content volume moved along the same line. Agents wired into the company’s single source of truth turn out material for the company website and its social channels. In the words of Melad Zahedi, Director of Product Marketing, “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.”
Daily work is what this was designed for, launches aside. Ad-hoc assistance has given way to a workflow with structure, agents sitting inside the team’s operations.
Meetings, Metrics, and a Second Brain
ChatGPT Work is also how people walk into a decision holding the business context it needs. Zahedi calls these GPT-driven automations his “second brain” to sort through a calendar of 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 says.
Take one executive meeting where the question turned to numbers kept in HubSpot and in other systems. Someone in the room asked Codex to pull and analyze the data, live. Zahedi recalls the moment: “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.”
What matters there is not an hour saved on a report. It is insight showing up at the moment somebody wants it, rather than sitting through another analysis cycle.
Caveats and Trade-offs
Impressive numbers deserve caveats. The savings are estimates built from modeled active role-hours rather than a controlled experiment: nobody ever measured the comparison, and the 243-hour baseline only approximates the work without Codex.
Review and sign-off stayed non-negotiable, too. Nothing customer-facing shipped without a human reading it in full—judgment was not replaced; drafting and iterating just got faster. A contractor was still required for the hero animation’s finishing touches: its opening scene, plus the final format. That marks a limit AI still hits when creative work needs a high degree of polish.
A third dependency is a source of truth that somebody keeps well maintained. Agents wired into those systems produce useful content only when the data they read is accurate and current, and messy underlying systems show up in the output.
One more: a single company’s case study may not generalize. Stampli’s own workflow, the skills its team had, and its appetite for adopting AI tools all shaped the outcome.
The Lesson: Redesign the Workflow, Not the Task
Anyone building products or learning AI tools can take a plain lesson from Stampli: making one task faster is the small version of this story; reshaping a whole workflow is the large one. Stampli pulled product context and decisions, meeting notes and messaging guidelines, into one shared system, and a complex launch that spans functions became something it can repeat and scale.
Tools are not the crux. The appetite to ask which steps are worth automating—and which calls still belong to a person—is. With ChatGPT Work and Codex, Stampli’s team kept product knowledge fresh, surfaced business insights, and got ideas to market sooner; the payoff was a launch delivered in six weeks instead of the months, or quarters, the work would otherwise have demanded.
Zahedi’s advice runs: “Being curious and just asking, ‘What can I do?’ and trying everything first through ChatGPT will unlock a lot of latent capacity that you didn’t realize was there in your organization—and in yourself.” For a team weighing AI adoption, curiosity plus trying the tools on real problems is where that latent capacity starts.