Quivr
Description
🖼️ Tool Name:
Quivr
🔖 Tool Category:
AI-powered “second brain” / knowledge-assistant platform; it falls under the category of tools for integrations, retrieval & augmented generation (RAG) where you connect documents/data to an AI interface and build assistant workflows.
✏️ What does this tool offer?
Quivr is a platform (both open-source and enterprise) that helps users ingest various file types and data sources, connect them with language models, and build interactive “brains” (AI assistants) that can answer questions, summarise, search and generate outputs based on the connected knowledge.
Key features include:
Support for any LLM: OpenAI, Anthropic, Mistral, local models, etc.
Ingestion of any file type (PDF, TXT, Markdown, DOCX) or data source (APIs, databases) and building knowledge-graphs or RAG workflows.
Customisable RAG workflow: retrieval → generation, configurable pipelines, ability to add internet search or tools.
Used as a “second brain” for personal knowledge management or by companies to build chat assistants connected to enterprise data.
⭐ What does the tool actually deliver based on user experience?
Enables users to query their own documents/data as if having a conversational agent over their files — e.g., “Ask your brain!” interface.
Supports building custom assistants (“Brains”) that reflect your own workflows/data, reducing context-switching and fragmentation.
Useful both for individuals (knowledge-workers, creators) and teams/organizations who need to surface insights from large/unstructured internal data.
Because of open-source and self-host capabilities, it allows flexibility, privacy and customization.
🤖 Does it include automation?
Yes — Quivr offers several automated/AI-driven workflows:
Automated ingestion and parsing of documents into vector stores / knowledge graphs.
Automated retrieval and generation: once setup, the “Brain” can answer queries, generate summaries, or respond to conversational prompts.
Integration automation: connecting to APIs/databases, enabling dynamic data access via LLM interface.
💰 Pricing Model:
Quivr appears to have both open-source self-hosted versions and enterprise SaaS/paid versions. The SaaS pricing includes tiers for customer support automation (though the core open-source RAG engine is free). For example, the pricing page lists plans like “Starter” for ~$1,200/year etc.
🆓 Free Plan Details:
The core Quivr engine is open-source and free to self-host.
For SaaS version, there is a “Starter” plan with fixed monthly/yearly cost and limited AI credits etc.
💳 Paid Plan Details:
The “Pro” and “Enterprise” tiers include higher volumes of AI credits, API integrations, knowledge-base support, on-premises deployment, etc.
🧭 Access Method:
Open-source: GitHub repository and documentation (QuivrHQ/quivr).
SaaS/web: Visit the official site and select plan or free trial.
After deployment: set up “Brain” by feeding files/data and start interacting via chat UI.
🔗 Experience Link: