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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.
Agentic coding platform (Cosmos) that runs software-dev agents at org scale, using a codebase context engine to cut token cost.
High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
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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
- ✦Context Engine for codebase understanding
- ✦Agents across the full SDLC
- ✦Model routing / bring-your-own-keys
- ✦Automated code review and test coverage
- ✦CLI, MCP and native tool integrations
- ✦Enterprise security (SOC 2, ISO 42001, SSO)
- ✦Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
- ✦Advanced metadata filtering applied during search traversal
- ✦Multivector support for multimodal retrieval
- ✦Reranking with score boosting and late-interaction models (ColBERT, MMR)
- ✦Flexible deployment: cloud, hybrid, private, or edge
- ✦Rust-based engine optimized for low-latency, high-scale search
- →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
- →Automating PR code review
- →Raising test coverage
- →Incident investigation and remediation
- →Large-scale migrations and onboarding
- →Building retrieval-augmented generation (RAG) pipelines
- →Powering AI recommendation and semantic search systems
- →Enterprises needing on-prem or hybrid deployment for compliance
- →AI agent platforms needing fast contextual retrieval at scale