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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.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
Managed context/RAG API that indexes, parses and retrieves documents, images, audio and video for developers building AI agents.
No public pricing
No public pricing
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
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦Hybrid vector, keyword and summary indexing
- ✦Native connectors to Google Drive, Notion, Slack and others
- ✦Multimodal ingestion of text, PDF, image, audio and video
- ✦Agentic OCR document parsing with structured extraction
- ✦Custom entity extraction via plain-language instructions
- ✦MCP server for permissioned agent access
- ✦Multi-tenant data partitioning
- →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
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Adding accurate retrieval/context to an AI agent or assistant
- →Building AI search over a company's Drive/Notion/Slack knowledge base
- →Extracting structured entities and tables from documents at scale