Gradial, an AI startup focused on marketing operations, launched new capabilities Wednesday that let its platform develop new, on-brand creative assets at scale - moving beyond its earlier revision-only focus. The update lets marketing teams generate hundreds or thousands of audience-specific ad variations by combining approved assets already in their systems, rather than starting from scratch.
CEO and co-founder Doug Tallmadge started Gradial in early 2023 after working as an engineer at SpaceX, where he began exploring what AI could do beyond simple text generation. The company's platform connects the various systems where marketing data lives - from Jira to SharePoint to web channels and email platforms - through custom integrations. Those integrations create what Tallmadge described as a cohesive "system of record" that Gradial's AI uses to develop content that fits brand guidelines and has a track record of performing well.
From revisions to full creative development
Gradial's earlier work focused on helping brands revise existing ads for different audiences. For example, a brand might need different designs for ads in different languages. German words tend to be much longer than their English translations, said Tallmadge, so a design "that was beautiful in English looks really terrible in German." Gradial's agent can adjust the design, shrinking or relocating text so it fits without looking cramped.
Marketers can also prompt Gradial to analyze creative performance and shift budget from underperforming ads toward top performers. The agent can "continuously analyze and remix" the content deployed on each channel, said Tallmadge, so ad dollars aren't wasted on creative that isn't working.
Humans always have the option to approve new creative before it launches, and the amount of oversight is up to the brand. Tallmadge said marketers typically choose human verification for "big decisions" like publishing a new, AI-developed asset. For changes like budget reallocations based on current performance, many are comfortable with a more hands-off approach. "It's selecting from already approved content," he said, so "there's no content risk."
Assembling ads from approved parts
The new capability doesn't generate assets entirely from scratch. Instead, it works like a Mr. Potato Head - assembling a new ad from an assortment of existing assets. Gradial looks at assets already in a marketer's systems like Figma and SharePoint, then combines approved backgrounds with well-performing copy, plus approved logos and product imagery.
Consistency helps build a cohesive brand identity, Tallmadge said, so there's no need to "reinvent the wheel" with every new creative. Plus, he added, if you just ask ChatGPT to generate an image, it inherently "looks AI generated."
Gradial has been developing this capability for about three months, said Tallmadge, though it could have made a single asset a year ago. Now it can produce the roughly 1,500 assets a marketing team might need for a campaign, with different versions for channel, locality, various promo codes and more. The efficiency lets clients "scale their vision," said Tallmadge, "without scaling their dollars."
Gradial charges clients based on marketing output rather than token use, a notable difference as token costs continue to rise. For marketers working with AI for Marketing tools, the pricing model removes the anxiety of watching AI usage costs climb with each campaign iteration.
Why this matters for marketers
The practical takeaway is that brands can now produce campaign-scale creative variations without multiplying their production budget. Gradial's approach - recombining assets that already perform well - also reduces the risk that AI-generated work will look generic or off-brand. For marketing teams juggling multiple channels, languages and promo codes, the ability to generate hundreds of audience-specific variations from approved components could meaningfully cut both production time and agency costs. The human approval option for major decisions also means teams can maintain brand control while letting the system handle routine variations. For AI for Creatives workflows, this represents a shift from using AI to tweak existing work toward using it to assemble new work from proven pieces.
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