Playad Raises $5.4M to Build AI Marketing Agents for Ad Creative
San Francisco-based GIGR, doing business as Playad, has closed a $5.4 million pre-seed round to speed up development of AI-driven marketing agents that help teams create, test, and optimize ad creative with less manual work. The round was led by BRV Capital Management and Mirae Asset Venture Investment, with angel participation from Bora Chung (Krafton board), Jihun Yu (Hyprsense founder), and Krew Capital.
The pitch is simple: creative ops are fragmented. Briefing, production, QA, experimentation, and analysis live in separate tools and teams, so momentum dies in handoffs. Playad is building an AI-native, multi-agent workflow to connect the entire lifecycle and guide what to build next based on performance signals.
What Playad Is Building
A coordinated set of agents that span briefing, asset generation, experiment setup, measurement, and iteration. The goal is to make creative production repeatable and test-driven, instead of a one-off sprint that you analyze weeks later. Less retro, more next-step recommendations.
Why Start With Interactive Ads
Playad launched in Q3 2025 with a focus on interactive formats, especially in gaming. These units let users tap, choose, and engage before install-often driving higher conversion rates and lower cost-per-install while producing richer signals (e.g., tap maps, choice paths).
Historically, interactive ads have been slow and expensive to produce because they need specialized development. Playad's bet: package the patterns, shorten build times, and iterate based on actual interaction data, not hunches.
Product Snapshot
- Interactive-first, expanding to image and video in one system.
- Built for fast experimentation and structured A/B testing.
- Turns creative into a repeatable workflow with versioning and measurable outcomes.
Why This Matters for Marketing Teams
- Ship more creative variations without ballooning headcount or vendor costs.
- Move from "post-mortem" to continuous iteration with clear next steps.
- Use interaction data (taps, choices) to inform creative direction, not just CTR or CPI.
- Tighter loops can lift ROAS in performance-heavy categories like gaming.
What to Validate in a Pilot
- Time to first live creative and average cycle time per iteration.
- Template coverage for your genres, styles, and platforms.
- Data plumbing: event schema for interactions, export to your BI, and identity/privacy handling.
- Experiment design: guardrails, traffic allocation, and how "wins" get promoted.
- Brand safety and compliance: approvals, locks, and audit trails.
- Cost model: per-seat vs. per-output vs. performance-linked pricing.
How It Could Fit Your Stack
Map where Playad slots between creative, UA, and analytics. Check integrations for asset libraries, ad platforms, and measurement. If you rely on heavy post-processing or custom dashboards, confirm how interaction-level events flow into your existing reporting and decision frameworks.
Team and Backers
GIGR's seven-person founding team brings experience from gaming, AI, finance, and big tech, including Bagelcode, Bank of America, YouTube, and PlayStation. Investor interest signals a shift from simple content generation to agents that recommend and execute next steps based on observed performance.
Practical Next Steps
- List the top 3-5 hypotheses you haven't been able to test due to production bottlenecks.
- Run a limited-scope pilot (one title or one channel) with clear success criteria: time-to-iteration, win rate of variants, and ROAS lift.
- Standardize on a minimal event schema for interactions so learnings compound across campaigns.
Want to Upskill Your Team on AI-Driven Marketing?
If you're formalizing AI skills across your org, consider this resource for marketers: AI Certification for Marketing Specialists.
Bottom line: clearer signals and faster loops beat guesswork. If interactive formats are meaningful in your mix, this is worth a close look.
Your membership also unlocks:
