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AI operating system for car dealerships with voice AI, a smart inbox and agents that capture leads and lift service revenue.
Prompt engineering, management, and LLM observability platform.
Open-source React/Angular SDK and platform for embedding agentic, generative-UI copilots into apps, Slack and Teams.
No-code platform for building and running AI agents that automate work across data, sales and support tasks.
Real-time web search and content-extraction API that grounds AI agents with fresh, structured data for research and RAG.
No public pricing
No public pricing
- ✦Voice AI with full customer context
- ✦AI-native Smart Inbox across channels
- ✦LiveCSI real-time satisfaction monitoring
- ✦Service Advisor Agent
- ✦Heat Case and Opportunity Agents
- ✦Mobile app
- ✦Prompt management
- ✦Prompt evaluations
- ✦LLM observability
- ✦Team collaboration
- ✦Version control for prompts
- ✦A/B testing of prompts
- ✦Prompt Registry
- ✦Historical backtests
- ✦Regression tests
- ✦Usage monitoring
- ✦React and Angular frontend SDKs
- ✦Agent-rendered generative UI
- ✦AG-UI agent-user interaction protocol
- ✦Connectors for LangChain and other frameworks
- ✦Pre-built customizable chat/sidebar components
- ✦Slack and Teams integrations
- ✦Thread and state persistence
- ✦Visual canvas to orchestrate multi-agent workflows
- ✦Prebuilt specialized agents (data, support, CRM, sales)
- ✦Access to many AI models with no vendor lock-in
- ✦Slack, Teams and email agent interaction
- ✦Recurring/scheduled tasks and triggers
- ✦Enterprise security: RBAC, VPC, audit logs, spend controls
- ✦Real-time web search API
- ✦Page content extraction and crawling
- ✦LLM-optimized structured/chunked output
- ✦Built-in PII and prompt-injection filtering
- ✦High-throughput, low-latency infrastructure
- ✦Drop-in integrations with major LLM providers
- →Answering dealership calls and messages
- →Rescuing and booking service leads
- →Resolving customer complaints (heat cases)
- →Boosting service-advisor productivity
- →Scaling customer support automation with LLMs
- →Empowering non-technical teams with prompt engineering
- →Building personalized AI interactions
- →Debugging LLM agents
- →Improving content creation processes
- →Managing and monitoring prompts with a team
- →Adding an AI assistant to a SaaS product
- →Building agents that render interactive UI
- →Deploying copilots across Slack and Teams
- →Connecting existing agents to a frontend
- →Automate data analysis and reporting
- →Triage support tickets and spot patterns
- →Keep a CRM updated and research prospects
- →Deploy AI agents across a team's tools
- →Grounding AI agents with live web data to reduce hallucination
- →Building research or retrieval-augmented generation (RAG) applications
- →Powering AI-driven search assistants
- →Enterprise-scale agents needing reliable web access