Hoping to attract more enterprise teams to its ecosystem, Adobe launched a new model customization service called Adobe AI Foundry, which would create bespoke versions of its flagship AI model, Firefly.
Adobe AI Foundry will work with enterprise customers to rearchitect and retrain Firefly models specific to the client. AI Foundry version models are different from custom Firefly models in that Foundry models understand multiple concepts compared to custom models with only a single concept. These models will also be multimodal, offering a wider use case than custom Firefly models, which can only ingest and respond with images.
Adobe AI Foundry models, with Firefly at its base, will know a company’s brand tone, image and video style, products and services and all its IP. The models will generate content based on this information for any use case the company wants.
Hannah Elsakr, vice president, GenAI New Business Ventures at Adobe, told VentureBeat that the idea to set up AI Foundry came because enterprise customers wanted more sophisticated custom versions of Firefly. But with how complex the needs of enterprises are, Adobe will be doing the rearchitecting rather than handing the reins over to customers.
“We will retrain our own Firefly commercially safe models with the enterprise IP. We keep that IP separate. We never take that back into the base model, and the enterprise itself owns that output,” Elsakr said.
Adobe will deploy the Foundry version of Firefly through its API solution, Firefly Services.
Elsakr likened AI Foundry to an advisory service, since Adobe will have teams working directly with enterprise customers to retrain the model.
Deep tuning
Elsakr refers to Foundry as a deep tuning method because it goes further than simply fine-tuning a model.
“The way we think about it, maybe more layman's terms, is that we're surgically reopening the Firefly-based models,” Elsakr said. “So you get the benefit of all the world's knowledge from our image model or a video model. We're going back in time and are bringing in the IP from the enterprise, like a brand. It could be footage from a shot style, whatever they have a license to contribute. We then retrain. We call this continuous pre-training, where we overweigh the model to dial some things differently. So we're literally retraining our base model, and that's why we call it deep tuning instead of fine-tuning.”
Part of the training pipeline involves Adobe’s embedded teams working with the company to identify the data they would need. Then the data is securely transferred and ingested before being tagged. It is fed to the base model, and then Adobe begins a pre-training model run.
Elsakr maintains the Foundry versions of Firefly will not be small or distilled models. Often, the additional data from companies expands the parameters of Firefly.
Two early customers of Adobe AI Foundry are Home Depot and Walt Disney Imagineering, the research and development arm of Disney for its theme parks.
“We are always exploring innovative ways to enhance our customer experience and streamline our creative workflows. Adobe’s AI Foundry represents an exciting step forward in embracing cutting-edge technologies to deepen customer engagement and deliver impactful content across our digital channels,” said Molly Battin, senior vice president and chief marketing officer at The Home Depot.
More customization
Enterprises often turn to fine-tuning and model customization to bring large language models with their vast external knowledge closer to their company’s needs. Fine-tuning also enables enterprise users to utilize models only in the context of their organization’s data, so the model doesn’t respond with text wholly unrelated to the business.
Most organizations, however, do the fine-tuning themselves. They connect to the model’s API and begin retraining it to answer based on their ground truth or their preferences. Several methods for fine-tuning exist, including some that can be done with just a prompt. Other model providers also try to make it easier for their customers to fine-tune models, such as OpenAI with its o4-mini reasoning model.
Elsakr said she expects some companies will have three versions of Firefly: the Foundry version for most projects, a custom Firefly for specific single-concept use cases, and the base Firefly because some teams want a model less encumbered by corporate knowledge.
