Multi-modal search
Search across text, images, PDFs, and structured content in one system so teams do not need separate tooling for each format.
Duckyとは？
Ducky is a fully managed AI search and retrieval platform for developers building AI-powered products. The site positions it as RAG infrastructure that helps teams add search, context, and answer generation without assembling a search stack themselves.
According to the source, Ducky handles the retrieval pipeline end to end: it supports multi-modal search across text, images, PDFs, and structured data, automatically chunks and reranks content, and exposes APIs for product integration. The emphasis is on getting search into production quickly while leaving the underlying infrastructure to Ducky.
Duckyでできること
Automated chunking and reranking
Documents are automatically split and prepared for retrieval, and results are reranked in multiple stages to surface more relevant matches first.
Advanced metadata filtering
Filter results by date, category, tags, or other attributes to support more precise search experiences and application-specific queries.
Managed search infrastructure
The platform is designed as a fully managed service, reducing the operational work of building and maintaining search infrastructure.
Developer-focused integration
Developers can use documented APIs together with Python and TypeScript SDK support to integrate search into product workflows.
RAG and agent support
For AI agents, Ducky can return source-attributed answers and reduce the amount of context sent into downstream LLM calls.
利用シーン
“Embed search in a SaaS product”
Add semantic or keyword-style retrieval to a product without building indexing, chunking, ranking, and infrastructure layers from scratch.
“Power internal content libraries”
Search across mixed content like quotes, survey results, graphics, and video assets, as shown in the UserEvidence example.
“Support RAG and agent answers”
Feed retrieval results into LLM workflows so agents can answer with source attribution and less irrelevant context.
“Build conversational search demos”
Turn documentation, legal pages, or codebases into searchable conversational experiences, similar to the gallery demos.
“Refine searches with metadata”
Apply filters and metadata to help users narrow search by date, category, tag, or similar attributes.
よくある質問
What is Ducky?
Ducky is positioned as a fully managed AI search and retrieval platform for developers building AI-powered product features. The source also describes it as RAG infrastructure for adding search and context to LLM workflows.
How is Ducky implemented?
The source says Ducky is ready to use with zero setup required and that developers can deploy AI search within minutes. It also highlights intuitive APIs, documentation, and Python and TypeScript SDK support.
What kinds of content can Ducky search?
Ducky supports multi-modal search across text, images, PDFs, and other structured content. The UserEvidence case study also shows it being used to search quotes, survey data, graphics, and video content.
Is there a free trial or starter option?
Yes. The pricing page says Ducky is free to try, and the homepage describes a trial with 100k index tokens and 100k retrieval tokens. It also lists a paid launch tier with monthly allowances and overage pricing.
Who is Ducky for?
The source shows Ducky being used for AI search, retrieval, RAG workflows, and conversational demos such as searching legal documents or codebases. It is aimed at developer teams that need search infrastructure rather than a consumer search app.
クイック情報
- Category
- Developer Tool
- Primary use
- AI search and RAG infrastructure
- Platform
- Fully managed service
- Developer interfaces
- APIs, docs, Python SDK, TypeScript SDK
- Search inputs
- Text, images, PDFs, structured data
- Pricing signal
- Free to try, with a paid launch tier
Ducky のトラフィック分析
トラフィックデータは参考情報としてご利用ください。
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