QRev is an open source alternative to Salesforce built from the ground up with AI agents at its core. Designed for sales teams overwhelmed by manual prospecting, email outreach, and lead research, QRev automates repetitive tasks using AI agents—freeing sales reps to focus on closing deals. It combines CRM functionality with autonomous AI workflows that can scale across entire sales organizations without human intervention.
Built with a modular architecture using TypeScript, Node.js, MongoDB, ChromaDB, and LangChain, QRev supports integration with external data sources and third-party tools. The system is composed of three core components: a React-based client interface, a Node.js app server with MongoDB persistence, and an AI server powered by LangChain for reasoning and automation. Deployment is flexible, supporting self-hosted environments with Docker or direct Node.js installation.
What You Get
- AI-Powered Sales Agent (Qai) - An autonomous AI agent that performs prospect research, analyzes LinkedIn posts and news, and drafts personalized outreach emails at scale, reducing manual work for SDRs and BDRs.
- BYOD (Bring Your Own Dataset) - Import custom prospect lists via CSV files to populate the system with your own leads, enabling tailored AI-driven campaigns without vendor lock-in.
- 24/7 Prospect Research - Qai continuously monitors and researches prospects using public data like LinkedIn, company websites, and news feeds to update lead profiles in real time.
- Personalized Email Campaigns - Automatically generates and sequences multi-email outreach campaigns tailored to individual prospect profiles, including job title, company, and industry context.
- Modern QRM (Qualified Relationship Management) - A CRM module that tracks and manages qualified leads with customizable fields, relationship history, and role-based access controls.
- Email Auto-Reply System - AI drafts email responses to incoming customer inquiries, allowing sales reps to review and send with one click, reducing response time and improving conversion rates.
Common Use Cases
- Running outbound sales campaigns at scale - A SaaS startup uses QRev to automate cold outreach to 10,000+ prospects by uploading a CSV, letting Qai research each lead and send personalized email sequences without human intervention.
- Reducing SDR burnout - A mid-sized B2B company replaces its 5-person SDR team with Qai, cutting labor costs by 70% while increasing lead response speed and email open rates through AI-personalized messaging.
- Managing high-volume lead pipelines - A sales operations team uses QRev’s QRM to track qualified leads from multiple sources, with AI agents updating lead status based on engagement and research findings.
- Integrating CRM with AI research - A sales leader connects QRev to their existing LinkedIn and CRM data to auto-populate lead profiles with real-time insights, eliminating manual data entry and improving forecast accuracy.
Under The Hood
Architecture
- Frontend components exhibit tight coupling with mock data and hardcoded rendering logic, lacking abstraction layers for data fetching or transformation
- Backend API lacks discernible service or repository layers, with minimal route implementation and no clear domain modeling
- No dependency injection or inversion of control mechanisms observed, leading to direct references to utilities and mock data
- UI components rely on ad-hoc implementations for data presentation rather than declarative, reusable patterns
Tech Stack
- Python backend using Flask and Pydantic for validation, with Beanie and ChromaDB for data persistence and vector storage
- React and TypeScript frontend with MUI, React-Redux, and Loadable for state management and code splitting
- OpenAI and LlamaIndex integrated for AI-driven content analysis, supported by custom logging and configuration modules
- Gmail API and PapaParse enable email ingestion and CSV processing, with MongoDB and ChromaDB providing scalable storage
Code Quality
- Extensive test coverage across email automation, HTML parsing, and API modeling using both unit and integration tests
- Strong type safety and schema validation via Pydantic, with comprehensive validation of request/response structures
- Clean separation of concerns with mock implementations for external dependencies, enabling reliable offline testing
- Consistent naming, modular structure, and well-isolated utility functions across both frontend and backend
- Robust error handling with custom exceptions and structured validation for malformed inputs
What Makes It Unique
- Dynamic data grid columns that adapt to campaign-specific metadata schemas without hardcoded configurations
- Rich in-table email rendering with HTML parsing and dynamic cell content, eliminating need for navigation to view details
- Theme-aware components that respond to both global theme state and active route context for cohesive visual feedback
- Loadable patterns for UI elements that reduce bundle size while preserving seamless user experience
- Unified API wrapper that standardizes error handling and response formatting across endpoints
- Synchronized client-server data models through typed interfaces that propagate campaign schemas from backend to frontend