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Platform to build AI customer-service agents trained on your data that answer 24/7, take real actions and hand off to humans.
Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
Free trial available
No public pricing
Free trial available
No public pricing
- ✦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
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- ✦Single API for image, video, audio, 3D and LLM models
- ✦Standardized model addressing across hosted, partner and custom uploads
- ✦Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
- ✦WebSocket and REST access with async webhook delivery
- ✦Pay-per-request billing with no infrastructure to manage
- ✦Raw serverless GPU/CPU compute for custom workloads
- ✦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
- →Automate customer support
- →Book meetings and complete tasks in chat
- →Capture and qualify leads
- →Keep answers current via auto-retrain
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals
- →Adding AI image or video generation to an app without managing infra
- →Batching multi-modal generation tasks in one API call
- →Running custom fine-tuned models via Model Upload
- →Cutting inference costs at high generation volume
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app