I still remember the moment I first walked into Sketch London. It wasn’t just about the food; it was about how Chef Pierre Gagnaire orchestrated each element—from the conceptual plating to the surprising mix of flavors—to create an immersive experience that went far beyond the standard restaurant setting. That sense of weaving multiple layers into one cohesive experience strikes me as incredibly relevant when thinking about how AI is transforming the creative field. In many ways, AI serves as the behind-the-scenes collaborator, tackling the heavy lifting of research and data work, while we remain the “head chefs” who shape and refine the final vision.
AI as Your Trusty Sous Chef
Every truly innovative project—whether it's in design, engineering, or product development—depends on a wealth of reliable information. It reminds me of Sketch London, where legendary chef Pierre Gagnaire transforms dining into an art form through five distinct spaces. Each room tells its own story—from the historic 18th-century architecture (once Dior's atelier) to the surprise ballet performances during Christmas—all elevated by Gagnaire's boundary-pushing cuisine. Just as his team's meticulous attention to detail creates an extraordinary experience, any creative project requires careful preparation. The challenge, of course, is that gathering all of that data can drain the time and mental energy we need for genuine creativity. In this sense, AI steps in as a sous chef, handling the bulk of the prep so we can stay focused on the artistry and strategy at hand.
Even if an AI system fetches every market statistic or technical document we request, it’s ultimately our responsibility to figure out how those details fit into the core of our project. That’s where the real craft lies: understanding what truly matters, filtering out what doesn’t, and shaping the outcome so it resonates with authentic human insight.
OpenAI’s “Deep Research” as the Prep Station
OpenAI’s Deep Research platform feels like having a dedicated prep station that efficiently combs through mountains of data and organizes it for you. In my own workflow, it has saved countless hours that would’ve been spent poring over academic papers, design trends, and old case studies. Yet, just as a sous chef can’t decide on the central flavors of a dish, Deep Research can’t determine your final direction. It lacks the cultural context and audience insight that inform how data should be interpreted. That’s why there’s a critical difference between nicely categorized facts and the spark that drives creative insight. Deep Research handles the repetitive legwork, freeing us to draw connections that might be missed if we were stuck sifting through endless documents.
Giselle Is Upgrading Your Entire “Kitchen”
Where Deep Research focuses on assembling raw materials, Giselle provides the complete professional-grade kitchen. Although Deep Research isn’t yet available as an API—and therefore can’t be integrated directly into Giselle at this point—we’re looking forward to the day when that becomes possible. By then, users might not even have to prepare their own data sources, leading to an even more seamless workflow.
In the meantime, Giselle remains an AI-driven environment that unifies tasks across product design, development, and marketing. It’s like walking into a kitchen where every tool is already in place. I’ve found Giselle’s node-based interface to be especially effective; each AI agent is represented as a node that performs a specific task—code review, documentation, or research—and linking these nodes creates a smooth workflow that spans every stage of a project. In my day-to-day development work, I appreciate how straightforward it is to see exactly which task is at which stage, so there’s no more guesswork or confusion about who’s handling what. Even the smallest to-dos that would otherwise be overlooked are right there on the interface, giving me confidence that nothing will slip through the cracks. I recently set up a text review node connected to a documentation node, and it was almost magical to watch one process feed directly into the other. It truly felt like a well-oiled machine where every part knows its exact role.
From my perspective, one of the most remarkable strengths of Giselle is how it centralizes tasks that typically exist in separate silos. One agent might look over a pull request and flag any design-related concerns, another might keep existing documentation up to date, and yet another can monitor competitor trends in the background. You can even generate product explainers that capture your brand’s unique voice, almost like having a junior writer on hand. With everything visually laid out, there’s no scramble to find documents or send repetitive messages—information flows naturally, much like watching an experienced kitchen crew work together without missing a beat.
A Five-Star Kitchen for Your Boldest Ideas
No matter how advanced AI gets, it can’t replace the innate human qualities of curiosity, empathy, and creative nuance. Instead, it should amplify our strengths by taking on the tasks that slow us down. Giselle excels at organizing information and automating mechanical steps, but it’s still our role to dream big, iterate on design, and deliver experiences with real emotional depth.
Personally, I’ve seen how teams regain valuable mental space once they adopt this setup. With fewer mundane chores and less time wasted on manual research or housekeeping tasks, there’s a fresh surge of energy for experimenting with bold ideas and crafting genuinely meaningful user experiences. I vividly recall hitting that aha moment: realizing that the real power lies in the interplay between AI’s ability to handle vast amounts of data and our ability to infuse it all with vision and empathy. If you’ve ever felt that thrill when a new idea begins to crystallize, just picture how much more vibrant it can become when AI is quietly taking care of the operational details in the background.