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Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Platform to build AI customer-service agents trained on your data that answer 24/7, take real actions and hand off to humans.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
AI chatbot builder that trains a support agent on your website content in minutes to deflect tickets and capture leads with cited answers.
No public pricing
Free trial available
No public pricing
No public pricing
- ✦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 agents trained on website/docs with auto-retrain
- ✦AI Actions (Stripe billing, Calendly booking, API calls)
- ✦Multi-channel embed (site, Slack, Notion and more)
- ✦Human handoff with context
- ✦Analytics on deflection and satisfaction
- ✦Multiple LLMs selectable per task
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- ✦AI-powered trading indicators
- ✦Screeners for identifying high-probability setups
- ✦Backtesters for strategy optimization
- ✦AI Backtesting Assistant for automated strategy building
- ✦Community support and educational resources
- ✦Fast setup trained directly on website content
- ✦Answers cited back to source content
- ✦Inbound lead capture with automated follow-up
- ✦Integrations with existing support/marketing stack
- ✦Self-improving responses over time
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Automate customer support
- →Book meetings and complete tasks in chat
- →Capture and qualify leads
- →Keep answers current via auto-retrain
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
- →Automating complicated price action analysis
- →Generating trading signals and detecting reversals
- →Scanning assets for specific trading criteria
- →Backtesting and optimizing trading strategies
- →Building trading strategies with AI assistance
- →SaaS companies deflecting repetitive support questions
- →Marketing teams capturing and qualifying leads via chat
- →Support teams wanting AI answers backed by verifiable sources