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I built a marketing operating system with long-term memory

Hacker News

I built a marketing operating system with long-term memory

Hey HN – Petr here. I spent 10+ years heading marketing at various companies, from startups to publicly traded, managing €100M+ budgets and teams up to 200 people. The same problem kept recurring: marketing teams lose institutional knowledge when people leave, brand guidelines get ignored under deadline pressure, strategy looks great on paper but stays in the bin, every tool is a separate silo, scalability and time to market is a challenge. What I learned: solo founders and small teams have the exact same problem, just compressed. You're the CMO, the copywriter, the ads person, and the brand police – all at once. There's no time to document what works, no one to enforce consistency, and context gets lost between Tuesday and Thursday. Then the revenue call comes and you bombard customers with 15 emails a week and are surprised when churn goes up. So I built The AI CMO as a "marketing operating system" rather than another execution tool. The core idea: * Brand guardrails enforced automatically – your voice, forbidden words, approval rules are baked in * Long-term memory – corrections stick, past campaign learnings compound * Orchestration – it decides which tools/channels to use based on your playbooks (built-in or your existing stack) * Your data stays yours – connect your own data warehouse or use ours; pipe everything back to your infrastructure Works whether you're a one-person shop or a 50-person marketing team – the system scales with you instead of requiring you to scale first. What's missing? What would make this actually useful for you?

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, context · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, 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
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, recurring · Missing: arr, mrr, profit
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
23%23% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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