Dapto – Real-Time Firewall for AI Prompts and Responses

Hacker News

Most companies are excited about GenAI, but scared to use it. Why? Because they have no control over what the AI sees, says, or shares. We kept seeing this problem in enterprise GenAI rollouts. Tools like GPT or Claude are getting embedded into dashboards, chatbots, and workflows, but there are no guardrails in place. So we built Dapto. A drop-in firewall that sits between users and the model. It intercepts every prompt and response, blocks prompt injection and hallucinations, masks PII and sensitive content, and adds full logs, metadata, and risk scoring. It works out of the box with OpenAI, Claude, Gemini, Ollama, and LangChain. No need to modify your model or application. Here’s a short demo: https://youtu.be/dxFb7Q12gcw We’re looking for feedback, early pilots, and brutal honesty. Would this fit into your stack? www.dapto.ai

Visit product View on Hacker NewsApr 17, 2025

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

91%91% 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: gemini · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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