Knolli provides a unified, no-code workspace for building, deploying, and monetizing AI copilots and agents. The platform securely integrates existing data sources and supports multi-agent architecture for complex workflow automation. This enables businesses to rapidly launch custom AI solutions with enterprise-grade security and integrated monetization features.
Funding
Funding not disclosed
Founders
Product
Problem
Knowledge creators and subject matter experts often face challenges in transforming their proprietary content into scalable, interactive AI experiences. This process typically requires significant technical expertise and development resources, hindering the ability to monetize expertise effectively.
Solution
Knolli provides a no-code platform that enables individuals and organizations to build, deploy, and monetize AI-powered knowledge products. Leveraging a proprietary Retrieval-Augmented Generation (RAG) framework, the platform allows users to upload their content and configure AI copilots with pre-built templates and customizable workflows. This facilitates the creation of branded AI assistants that can be offered via subscription or pay-per-use models, generating recurring revenue streams. The platform ensures data privacy by maintaining content in isolated workspaces, preventing it from being used for training external models.
Target Audience
The platform targets knowledge creators, consultants, coaches, and businesses seeking to package and monetize their expertise through AI-driven applications.
Features
- No-code studio for rapid AI copilot development and deployment.
- Pre-built templates and workflows for common use cases such as course companions and intake bots.
- Integrated monetization tools supporting subscription and pay-per-use models.
- Retrieval-Augmented Generation (RAG) architecture for accurate, content-based AI responses.
- Full brand customization, including custom domains, UI palettes, and logos.
- Real-time analytics dashboards for tracking query volume, user engagement, and revenue metrics.
- Private content workspaces ensuring data isolation and intellectual property protection.
- Secure deployment with content never used for training public AI models.
- API for potential integration with existing workflows and data sources.