Hi HN,
We’ve been working on a problem we kept seeing in enterprise GenAI rollouts:<p>As tools like GPT, Claude, and Gemini get embedded into dashboards, support tools, and business systems, most organizations have no control or context over what the AI sees, says, or shares.<p>That creates serious risks:
- Hallucinated answers
- Prompt injection attacks
- Data and PII exposure
- Industry compliance violations (e.g. HIPAA, SOC2, GDPR)<p>So we built Dapto , an enterprise-grade trust layer designed for companies that want to deploy GenAI safely, at scale, and with full governance.<p>See it in action - <a href="https://youtu.be/dxFb7Q12gcw" rel="nofollow">https://youtu.be/dxFb7Q12gcw</a><p>Here’s how it works:
- Validates prompts before they hit the LLM - catching jailbreaks, injections, and
policy violations
- Checks AI responses before they reach the user - to prevent hallucinations or
unauthorized content
- Auto-generates real-time metadata context from the input prompt
- Then re-verifies the AI’s response against enterprise data before it’s shown
- Detects and masks sensitive data (PII, financials, health info) as needed
- Keeps full logs, audit trails, and risk scoring, without changing your model or
app<p>But here's what makes it different:<p>We use a multi-agent architecture with:
- Vertical-specific AI agents (Finance, Healthcare, Legal, etc.) that understand
the unique compliance and domain context of your industry
- Horizontal supporting agents that handle metadata, hallucination detection,
policy enforcement, and data verification<p>You can build your own AI agents inside Dapto, with all safety and governance layers baked in.<p>It works out of the box with OpenAI, Claude, Gemini, Ollama, LangChain, and self-hosted models.<p>We’d love feedback, especially from folks building with LLMs in regulated or complex domains.<p>What are you using today for guardrails? Would this plug-in approach fit into your stack?<p>Thanks for reading.
www.dapto.ai