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Zyler – AI agent for marketing data that doesn't hallucinate

Hacker News

Zyler – AI agent for marketing data that doesn't hallucinate

My co-founder Suryansh (product marketing expert) and I were burning way too many nights debugging why our startup's conversion rates were tanking. I'd spent years building adtech and martech software, but we were still juggling between Google Analytics, Google Ads, YouTube Analytics, and SEO tools – each with their own dashboard that felt like archaeological expeditions. After burning 6+ hours weekly just to connect the dots between platforms, we realized we needed something fundamentally different. So we built Zyler – an AI agent that connects to all your marketing channels (Google Analytics, Ads, SEO, YouTube) and generates unified insights through natural language, without the hallucination problem that plagues most AI analytics tools. The core problem: Marketing teams are drowning in fragmented data across multiple platforms right as Google deprecates cookies in 2025. You need one dashboard for GA, another for Google Ads, a third for SEO metrics, YouTube analytics somewhere else – and none of them talk to each other. Most AI solutions hallucinate when trying to connect these dots. What makes this technically interesting: Zero-hallucination architecture: We've focused heavily on accuracy over creativity – the AI only makes claims it can back up with your actual data across platforms Natural language to analytics translation: No more switching between 4+ dashboards to understand campaign performance Mobile-first analytics: First mobile-compatible multi-platform analytics AI agent Real-time processing: Instant insights from large datasets across GA, Ads, SEO, and YouTube without the usual loading screens Cross-platform insights: Ask "Why did my YouTube ads perform better than Google Ads last month?" and get unified analysis across all your channels The drag-and-drop interface makes anyone a "marketing data expert" in about 5 minutes. We're seeing customers reduce reporting time by 80% while getting unified insights across all their marketing channels. Early traction: Hit Product Hunt top 10, users across 6 continents, and some customers seeing 300% ROI increases. All organic growth so far – no funding raised yet, just Suryansh and me bootstrapping this. Pricing: $50/month (down from our original $99 pricing) vs the $1000+/month enterprise alternatives, which makes unified marketing analytics accessible for startups and small teams. It's still rough around the edges, and we're working on expanding to Meta Ads and LinkedIn next. I'd love feedback, especially on: What other marketing platforms should we integrate next? (Meta, LinkedIn, TikTok?) How do you currently handle cross-platform marketing attribution? Any interest in an API for custom integrations? Try it out at https://www.zyler.ai – there's a free tier to test with your marketing data. Happy to answer any technical questions about the AI architecture or product direction – Suryansh can speak to the marketing side and I can dive deep on the engineering!

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
98%98% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, google, user · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, google, users · Missing: mobile apps, ios, personal
59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
33%33% 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 · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
28%28% predicted probability of success on Acquire.com, 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.

Incorrect prediction on native model

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