Longtime Seattle food photographer Scott Pitts spent 25 years capturing commercial imagery for major brands, and now he’s using that quarter-century archive to train Pallat. The new AI-powered production system is designed to eliminate costly reshoots while keeping real studio craft at the center of generative creative tech.

The platform combines fine-tuned open-weight models with Pitts’ extensive archive, allowing art directors to modify existing campaign assets — like swapping a topping, adjusting lighting, or changing a backdrop — in minutes through software rather than starting from scratch back on set.

Pitts, a non-technical founder operating Pallat out of his Seattle photo studio, SP Studio, leads a nimble five-person team and believes domain experience is key to competing with generic AI platforms.

“We are close to the problem, and we’re looking at it from a photographic eye,” he said. “We’re making sure those outputs look photoreal, that they’re not going to get labeled as AI slop.”

To show how the tech works in practice, Pitts points to a recent shoot for a national steakhouse client. After completing a complex setup for a burger — carefully layering the bun, patty, sauce, and greens — the brand asked if they had shot a version with tomatoes. Rather than calling back the food stylist and rebuilding the set, Pitts dropped the final image into Pallat, prompting it to add two tomato slices with subtle condensation, natural translucency, and accurate drop shadows cast onto the cheese below.

In another instance, a commercial seafood brand prepared packaging imagery for a buyer presentation, only for the client to ask to see the fish presented on a white plate instead. Pallat to the rescue.

Commercial photographers have long tweaked images using tools like Photoshop, but Pitts sees AI as the natural next step for advertising workflows — distinct from news photography, where image manipulation remains out of bounds. Where Photoshop requires painstaking manual editing to adjust a scene, Pallat handles complex lighting, translucency, and material physics in minutes based on a simple prompt.

The startup recently signed its first enterprise customer and is currently working directly with brands as a hands-on production partner while building toward full software access.

Pitts sees the technology not as a threat to his craft, but as a natural progression. He started his career shooting four-by-five film, then transitioned to digital and video. AI is another progression.

“My hope is that me building Pallat is sort of this bridge between tech and creative,” Pitts said. “Craft is still important. Judgment and taste are still probably some of the most important things.”

Continue reading for Pitts’ answers to our Startup Spotlight questionnaire.

In 50 words or less, give us your startup’s elevator pitch.

Pallat is a photographer-led AI production system built for food and beverage brands, born from a working photo studio. It combines licensed photography with generative workflows to help brands scale photo-centric content while maintaining the creative control expected from commercial photography.

What problem are you obsessed with solving?

I’ve spent 25 years watching brands solve the same problem: invest in a shoot, then ultimately need more usable imagery than the initial shoot was designed to deliver. Generic generative tools can create images, but weren’t built around the quality, control and production standards food and beverage brands require.

I’m obsessed with using AI to close the gap. Pallat gives brands a way to extend photography they’ve already invested in and create new production-ready imagery grounded in a licensed dataset and the standards of a traditional photoshoot.

What surprised you after talking to customers?

Because we’re so close to the problem we’re solving, their need for a solution and high bar for quality didn’t surprise me. 

What did was how much generated imagery disrupted their existing workflows. There is no obvious owner, no review path and no shared vocabulary for feedback and approvals. Brands are asking us to help establish new workflows, and that has turned out to be almost as important as building the tech itself.

How has AI changed the way you build your company?

AI is a big part of why a five-person team can build something like this. Our tech stack is built on open-weight models that we fine-tune using proprietary training data, while foundation models support planning and a handful of day-to-day operations.

Not to oversimplify it, but in many ways my role at Pallat parallels production. I built a team of experts, defined the problem we’re solving and established the criteria for the output. A growing part of my work is getting those standards out of my head and structuring evals so they hold when I’m not in the room.

What’s one thing people misunderstand about your startup?

That Pallat is trying to replace photography. It’s far from it.

Practical photos are important inputs, and our studio continues to create net-new ones to expand the system. Visual trends are always evolving, so datasets powering creative tech cannot be static. The future of production is hybrid: practical photography and generative imaging working together, with each deployed where it creates the most value. 

What’s the toughest decision you’ve made in the past year?

Resisting the urge to broaden Pallat before we establish product-market fit. The goal isn’t to automate every step as quickly as possible. It’s to understand which problems in the workflow are best solved through software.

What’s the one piece of advice you give to other entrepreneurs?

I truly believe some of the most interesting AI companies will come out of service businesses where the founder knows the industry exceptionally well — where the friction lives, which shortcuts a client will notice, and what excellence looks like in their vertical.

I spent a long time assuming my 25 years in photography was the past and AI was the future, and I had that backwards. The years on set that sharpened my taste and judgment, our dataset and the client relationships are the true compounding assets.

We’ll know our company has made it when…

When an art director at a food or beverage brand drafts a shot list dividing it into two columns: “Capture as Practical Photography” and “Generate in Pallat.”

When that becomes a normal way of planning, Pallat will have done what we set out to do.

When Starbucks scrapped an AI-powered inventory counting tool back in May, just nine months after revealing the new system, it landed as a surprise to those tracking the coffee giant’s high-tech ambitions. A new report from Fast Company tells the inside story of how the national rollout disintegrated — and why the Redmond, Wash.-based startup behind it was left “blindsided.”

Known as “Automated Counting,” the tool was built in partnership with NomadGo to scan backroom storage shelves using iPad Pros equipped with computer vision, spatial computing, and augmented reality. It was designed to automatically tally coffee bags, milk, syrups, and other key supplies.

The idea was to turn an hour-long manual chore into a 10-to-12-minute job so baristas could focus on making drinks and connecting with customers.

The technology was deployed rapidly across all 11,300 company-operated Starbucks locations in North America. But almost immediately, real-world store environments triggered rampant glitches, according to Fast Company.

Baristas reported camera errors — such as shiny refrigerator reflections doubling milk counts or the app misidentifying syrups and trash cans — while stores with spotty Wi-Fi frequently had their counting progress wiped out entirely mid-scan.

According to Fast Company, the technical breakdowns stemmed from both software limitations and outdated infrastructure. While NomadGo’s computer vision achieved 99% accuracy in controlled tests, CEO David Greschler noted that computer vision inherently struggles when inventory changes — requiring up to six weeks of retraining for seasonal holiday cups or limited-time packaging that NomadGo developers sometimes only learned about once items hit store shelves.

Compounding the problem, people involved in building the tool pointed to Starbucks’ backend network, which relies on a legacy IBM AS/400 system dating back to the 1990s, making it difficult for cutting-edge AI to process real-time store data reliably.

When Starbucks notified NomadGo on April 3 that it was pulling the plug, the startup was reportedly blindsided. Greschler called the decision “a complete surprise,” telling Fast Company that “there’s nothing you can do when leadership and strategy change.”

Within days of losing its centerpiece enterprise client, NomadGo was forced to lay off a large chunk of its 30-person workforce, according to the report, including the technical team that managed the Starbucks integration. Six weeks later, on May 18, Starbucks formally notified baristas that Automated Counting was retired, instructing them to rip the QR tracking codes off backroom shelves and return to manual tallies.

A Starbucks spokesperson provided GeekWire with this statement on Monday:

“Human connection is at the core of our business, which is why we have invested $500 million to put more partners (employees) in our coffeehouses. We use technology to support human connection, not to replace it. This tool was designed to simplify a routine task and give partners more time with their customers. When it fell short, we listened to feedback and changed course. That is what innovation looks like at Starbucks: listening, learning, and adapting.”

GeekWire also contacted NomadGo, and we’ll update this story when we hear back.

Despite retiring Automated Counting, Starbucks has pushed forward with other AI initiatives across its business. The coffee giant is building an AI-powered ordering companion inside its mobile app to translate cravings into custom recipes, while testing a ChatGPT integration that suggests drinks based on a customer’s mood or outfit.

For store staff, the company continues to rely on Green Dot Assist, a generative AI virtual assistant built to help baristas quickly look up recipes, standards, and store operating procedures.