San Francisco–based startup GIGR, operating under the product name Playad, has raised $5.4 million in pre-seed funding to tackle one of digital marketing’s most stubborn problems: how slow, fragmented, and expensive ad creative production still is—even in an era of AI-everything.

The round was led by BRV Capital Management and Mirae Asset Venture Investment, with backing from a notable group of angel investors, including Bora Chung (board member at Krafton and former Bill.com executive), Jihun Yu (founder of Hyprsense, acquired by Epic Games), and Krew Capital. The funding will be used to accelerate development of Playad’s AI-driven marketing agents, which aim to help teams create, test, and improve advertising creative with far less manual effort.

From tools to workflows that actually learn

Despite an explosion of creative tools, most marketing teams still operate in a stop-start cycle: briefs move to designers, revisions bounce across teams, and performance data is reviewed after campaigns are already live. Insights arrive late, and applying them to the next creative round often feels more like guesswork than science.

GIGR’s bet is that the next leap in marketing performance won’t come from adding yet another point solution. Instead, it will come from an AI-native workflow—one that treats creative as a continuous learning system rather than a series of one-off assets.

Playad is being built as a multi-agent marketing workflow that spans the entire creative lifecycle: briefing, production, experimentation, measurement, and iteration. The idea is simple but ambitious: every campaign should generate signals that directly inform what gets built next, tightening the feedback loop between performance and creation.

Why Playad is starting with interactive ads

Playad’s initial focus is interactive advertising, particularly playable ads popular in gaming and app marketing. These formats have long been valued for their ability to drive higher conversion rates at lower cost per install, largely because they let users experience a product before committing.

More importantly, interactive ads generate richer performance signals than static formats. Taps, swipes, and in-ad choices reveal not just whether a creative worked, but how users engaged with it. That level of granularity makes iteration far more actionable.

Industry data suggests that playable ad performance hit record highs in 2025, reinforcing the growing importance of interactive formats. Yet they’ve remained difficult to scale. Historically, interactive ads have been slow to produce, expensive to maintain, and heavily dependent on specialized developers—putting them out of reach for many marketing teams.

Playad is designed to remove those constraints. By automating much of the production and iteration process, the platform aims to make interactive ads fast enough to test continuously and simple enough for marketers to own directly. Teams can rapidly create variations and A/B test them without the usual development bottlenecks.

Beyond playables: an AI-native creative platform

While interactive ads are the entry point, GIGR is positioning Playad as a broader AI-native creative system. The platform is being built to support image, video, and interactive formats within a single workflow, allowing teams to apply learnings across channels instead of treating each format as a separate effort.

“Marketing performance increasingly depends on how quickly teams can learn from creative—and act on it,” said Steve Chung, co-founder of GIGR. “We’re building AI agents that make iteration the default, so teams can apply what’s already working across the market to their next creative without sacrificing quality.”

That philosophy aligns with a broader shift in MarTech toward experimentation velocity. As paid media costs rise and targeting options narrow, creative has become one of the few remaining levers marketers can pull to improve ROAS. Faster learning cycles can translate directly into competitive advantage.

Speed is the hook; confidence is the payoff

According to GIGR, customers often adopt Playad to move faster—but stick around because of what that speed enables. Teams can ship more iterations, run more experiments, and close the loop between creative decisions and performance outcomes.

Early users have reported dramatic reductions in production costs—up to 90% in some cases—alongside measurable improvements in acquisition efficiency. Those gains aren’t just about doing more with less; they’re about reducing uncertainty in creative decision-making.

“We’re not trying to simply produce more assets,” said Jay Cho, CEO and co-founder of GIGR. “We’re building a system where every launch creates learning—and that learning directly improves the next creative decision.”

That emphasis on learning over output is notable in a market crowded with AI tools promising speed alone. If Playad can consistently translate performance signals into better creative decisions, it could point to a new category of AI-driven marketing systems—ones that don’t just automate tasks, but actively shape strategy.

The bigger picture for MarTech

Playad’s funding arrives at a moment when marketers are reassessing the role of AI in creative workflows. Generative tools have lowered the barrier to producing assets, but many teams still struggle to connect creation with outcomes. The promise of AI agents that manage iteration end-to-end—brief to performance to next build—speaks directly to that gap.

Whether GIGR can scale that vision beyond early adopters remains to be seen. But the focus on interactive formats, experimentation velocity, and measurable learning suggests the company is aiming at a real pain point, not just a flashy demo.

For now, Playad is positioning itself as a reminder that in modern marketing, creative isn’t just about inspiration—it’s about iteration, signal, and speed.

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