Perplexity Computer — The Age of AI Agents Is Here

Naoma AI is an AI-powered video demo agent for B2B SaaS companies that delivers live, personalized product demonstrations directly in the browser, available 24/7 without requiring a human sales representative. The agent adapts the demo flow based on the visitor's role, industry, and specific questions — creating a tailored experience that mirrors a live sales conversation. For SaaS teams, this means qualified prospects can experience the full product value at any time, accelerating the sales cycle and reducing pressure on human demo resources. Naoma integrates with existing CRM tools, captures lead information throughout the demo, and hands off warm opportunities to sales teams with full context on each prospect interaction.

Airtable Assistant expands Airtable into a more complete AI app-building and workflow automation platform for business teams. It combines conversational app creation, AI agents, research, analysis, and no-code workflow automation so teams can turn data and operational processes into production-ready internal tools faster. The platform highlights Omni for natural-language app building and field agents for tasks such as lead enrichment, campaign content generation, and feedback triage. This makes Airtable Assistant appealing to operations, marketing, and product teams that want practical AI embedded directly into the systems they already use rather than a standalone chatbot. It is best suited for organizations looking to deploy AI inside repeatable workflows, structured data operations, and custom business applications without heavy engineering overhead.

Plurai vibe training is a method for training small language-model evaluators and guardrails around a specific agent workflow. Instead of relying only on generic frontier-model judges, teams can create lower-latency evaluators tuned to the exact behavior, tone and task boundaries their agent needs. It is useful for AI product teams building agents that require quality checks, safety gates, regression tests and production monitoring without expensive inference on every step. The approach stands out because it treats evaluation as a lightweight custom model layer, promising cheaper and faster checks for narrow use cases. It is best understood as agent reliability infrastructure rather than an end-user chatbot.