Hi HN, We’ve been working on a problem we kept seeing in enterprise GenAI rollouts:

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.

That creates serious risks: - Hallucinated answers - Prompt injection attacks - Data and PII exposure - Industry compliance violations (e.g. HIPAA, SOC2, GDPR)

So we built Dapto , an enterprise-grade trust layer designed for companies that want to deploy GenAI safely, at scale, and with full governance.

See it in action - https://youtu.be/dxFb7Q12gcw

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

But here's what makes it different:

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

You can build your own AI agents inside Dapto, with all safety and governance layers baked in.

It works out of the box with OpenAI, Claude, Gemini, Ollama, LangChain, and self-hosted models.

We’d love feedback, especially from folks building with LLMs in regulated or complex domains.

What are you using today for guardrails? Would this plug-in approach fit into your stack?

Thanks for reading. www.dapto.ai