Ducky is a managed retrieval layer that handles the entire AI search pipeline for you, from ingestion and indexing through chunking, reranking and answer synthesis. Instead of stitching together an embedding model, a vector database, a chunker and a reranker, teams call one API surface and let Ducky manage the moving parts. The platform markets a zero-setup onboarding path: point it at your content, rely on smart defaults, and ship a working search feature the same week.
Beyond plain keyword matching, Ducky supports multi-modal content — text, images and PDFs — so the same index can answer questions across document types. It adds metadata filtering for precise narrowing (dates, categories, tags), multi-stage reranking to push the best results to the top, and agent-friendly answer generation with source attribution. Ducky also advertises context filtering that trims irrelevant passages before they reach an LLM, which the vendor says can cut token usage substantially and reduce hallucination by grounding agents in accurate context.
The product is built for developers first, with documented APIs and Python and TypeScript SDKs, a public GitHub presence and thorough docs. A clickable in-browser demo and a book-a-demo flow let teams evaluate quality before committing. Pricing is usage-based with a free trial tier and a paid launch tier, so small prototypes can start at no cost and scale into production retrieval workloads.
