CaseAI – Build custom RAG agents for WhatsApp in 2 minutes

Hacker News

Yes, absolutely. In fact, the Simulator link is more important than your homepage link. Here is why: Hacker News users hate "Marketing": If they land on a homepage that says "Sign Up" or "Book a Demo," they will leave immediately. They love "Toys": They want to click buttons and see the tech work right now. The "No-Login" Rule: By explicitly saying "No Login Needed," you signal that you respect their time. This gets you upvotes. Here is the final, clean version to copy-paste. I have made the Simulator link prominent. Title: Show HN: CaseAI – Build custom RAG agents for WhatsApp in 2 minutes Url: https://www.caseai.ai Text (Body): Hi HN, I built CaseAI ( https://www.caseai.ai ) to make it easy for non-technical businesses to deploy "smart" agents on the official WhatsApp Cloud API. The Problem: Millions of service businesses run entirely on WhatsApp but struggle with two things: They can't answer leads instantly 24/7. Standard "chatbots" are too rigid and "GPT wrappers" hallucinate too much for business use. The Solution: CaseAI is a no-code platform where you can upload your business data (PDFs, price lists), and it spins up a dedicated WhatsApp Agent using RAG. It handles the heavy lifting of: Ingestion: Chunking and embedding uploaded documents. Orchestration: Managing the WhatsApp Cloud API session window. Handoff: Detecting when a human needs to intervene and pausing the AI. Try it (No Login Required): I built a Live Simulator so you can test the RAG accuracy in the browser without needing to open WhatsApp: https://www.caseai.ai/#simulator-section The Tech Stack: Frontend: Next.js Database: Drizzle ORM Messaging: WhatsApp Cloud API (Meta) Vector Search: Used for the RAG retrieval pipeline. I’m looking for feedback on the retrieval latency and the quality of the generated answers. Thanks!

Visit product View on Hacker NewsJan 27, 2026

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

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Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, pipe, io · Missing: https docs, excited, just released

42%42% 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, users · Missing: plus, intuitive, reviews

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

TrustMRRLess likely to generate early MRR · Strong signals: answers, users · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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