OnBrand by SlideSpeak

Product Hunt

Design context for AI agents

OnBrand gives AI agents design context through MCP. Add your brand guidelines, logos, colors, icons, imagery and slide rules, so tools like Claude, Codex and ChatGPT can create slides, infographics and design assets that actually match your brand. No more generic AI output, random layouts or off-brand visuals. OnBrand can generate your entire brand guidelines as a design.md from your existing website, existing branding guidelines or even PowerPoint templates.

Visit product View on Product HuntJun 22, 2026Design Tools Branding Artificial Intelligence

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Actual performance

107upvotes

12comments

Made the leaderboard

Traction signals

Makers1

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

18%18% predicted probability of success on Hacker News, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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