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    Rasa

    Rasa

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    Platform

    Enterprise platform for building, deploying, and managing conversational AI agents.

    Founded 2016San Francisco, California, USLinkedIn
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    About Rasa

    Rasa: Build Trustworthy AI Agents for Real-World Use

    Rasa is an enterprise platform for building, deploying, and managing conversational AI agents across every channel. The platform extends Large Language Models (LLMs) with structured flows, deterministic business logic, and built-in recovery patterns, giving teams full control over agent behavior and performance.

    Key Features

    • CALM: Extends the flexibility of LLMs with the reliability of deterministic logic.

    • Enterprise RAG: Retrieves information in real time to ensure answers are fresh, verifiable, and aligned with trusted enterprise data.

    • Voice Gateway: Delivers real-time voice infrastructure with built-in turn-taking, timeouts, and latency control.

    • Multilingual AI: Expands global reach with agents that adapt to different languages, tones, and contexts.

    • Omnichannel Deployment: Deploys across web, mobile, and messaging apps, with hosting options on-premises, in your own infrastructure, or managed by partners.

    • Model Context Protocol (MCP): Provides a standard way to connect AI agents with APIs as tools.

    Use Cases

    • Customer Support: Resolves routine customer issues quickly and automates internal helpdesk requests.

    • Sales Enablement: Guides buyers, qualifies leads, and hands off conversations at the right moment to drive revenue.

    • Operational Efficiency: Automates internal tasks across IT, HR, and operations to reduce manual requests.

    • Industry-Specific Automation: Handles high-volume inquiries and automates workflows securely for finance, retail, government, insurance, healthcare, telecom, and travel sectors.

    Getting Started

    Website: https://rasa.com/

    Rasa enables enterprise teams to build high-trust AI agents that perform reliably in production, keeping systems fast, flexible, and cost-efficient by using LLMs only when necessary.