Ducky

What is Ducky?

Ducky is a comprehensive retrieval platform that simplifies the implementation of semantic search and Retrieval-Augmented Generation (RAG) systems for developers. The service handles complex infrastructure challenges including vector databases, embedding models, chunking strategies, query transformation, and reranking systems through a single managed solution. With its multi-stage retrieval pipeline, Ducky enables developers to quickly integrate accurate search capabilities into their applications without managing the underlying technical complexity. The platform offers seamless integration through a Python SDK and supports various deployment scenarios from rapid prototyping to production-scale applications.

Key Features

• Multi-Stage Retrieval Pipeline

  Complete search infrastructure handling chunking, query rewriting, hybrid search, and reranking to deliver contextually relevant results rather than just similar matches.

• Developer-First Integration

  Simple Python SDK with comprehensive documentation allowing developers to implement semantic search in seconds while Ducky manages all backend infrastructure.

• Managed Infrastructure

  Fully hosted solution eliminating the need to configure vector databases, embedding models, or scaling infrastructure, with automatic optimization for performance and accuracy.

• LLM Agent Enhancement

  Direct integration capabilities for language model agents, providing context-aware information retrieval to reduce hallucinations and improve response accuracy.

• Flexible Deployment Options

  Supports various implementation approaches from rapid experimentation to production deployment with generous free tier for builders and clear pricing structure.

Use Cases

• RAG System Development : Developers building question-answering systems or chatbots that need to retrieve relevant information from large document collections or knowledge bases.
• Enterprise Search Solutions : Companies implementing internal search capabilities across documentation, policies, or business data without investing in complex search infrastructure.
• LLM Application Enhancement : Teams adding contextual retrieval to language model applications to improve accuracy and reduce hallucinations in generated responses.
• Rapid AI Prototyping : Startups and development teams needing to quickly validate search-based features without dedicating resources to infrastructure setup and maintenance.
• Document Intelligence Systems : Organizations building systems to extract insights from large document repositories, legal databases, or technical documentation collections.

FAQs