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Ducky

Ducky: Managed AI search infrastructure with RAG support for rapid AI feature deployment.

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Introduction

Overview

Ducky is a fully managed AI search infrastructure platform designed to empower developers to build and deploy AI-powered features with unprecedented speed and ease. It abstracts away the complexities of building and maintaining search infrastructure, allowing teams to focus on creating exceptional user experiences.

What is Ducky?

Ducky provides a unified platform that handles the entire AI search pipeline, from data ingestion to retrieval. It leverages Retrieval-Augmented Generation (RAG) support to enhance the capabilities of AI models, ensuring accurate and relevant results. The service is built with a developer-first approach, offering intuitive APIs and robust documentation.

Key Benefits

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Ship Features Faster: Deploy AI search in minutes, not months, by skipping infrastructure setup and configuration.

Unified Platform: Manage the entire AI search pipeline within a single, cohesive interface.

Reduced AI Costs: Optimize token usage by up to 80% through effective context filtering.

Enhanced AI Reliability: Minimize hallucinations and improve accuracy by providing LLMs with precise, relevant context.

Main Features

Ducky offers a suite of powerful features to streamline AI development:

Multi-modal Intelligence: Seamlessly search across text, images, and PDFs, understanding content regardless of format.
Automated Data Processing: Features automated chunking and multi-stage reranking to optimize document retrieval and ensure top results.
Advanced Metadata Support: Enables precise searches with filters based on attributes like date, category, or tags.
Developer-First Experience: Provides intuitive APIs, comprehensive docs, and SDKs for Python and TypeScript, along with instant, smart defaults for immediate results.
RAG Support: Works seamlessly with LLMs to automate search-to-synthesis, providing source attribution and improving AI agent performance.

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Frequently Asked Questions

Freemium

+1

Product Information

Pricing Model

💎 Freemium

Supported Platforms

Web

Supported Languages

English

Key Features

🚀

Rapid Deployment

Deploy AI search capabilities in minutes with a fully managed infrastructure, eliminating the need for complex setup and configuration.

📚

Multi-modal Search

Search across various content types including text, images, and PDFs within a single, unified platform.

✨

Optimized Retrieval

Automated document chunking and multi-stage reranking ensure optimal retrieval and surfacing of the most relevant results.

🔍

Advanced Filtering

Utilize advanced metadata filtering to conduct highly precise searches, narrowing down results based on specific attributes.

🤖

LLM Integration & RAG

Integrates seamlessly with LLMs, automating search-to-synthesis for AI agents and reducing AI costs and hallucinations.

💻

Developer Tools

Offers intuitive APIs, comprehensive documentation, and SDKs for Python and TypeScript, catering to a developer-first experience.

Pros & Cons

Pros

Enables extremely fast deployment of AI search features.
Fully managed service reduces operational overhead for development teams.
Supports multi-modal search (text, images, PDFs).
Helps reduce AI costs and hallucinations when used with LLMs.
Developer-friendly with good documentation and SDKs.

Cons

Limited information on specific pricing tiers beyond the initial free/launch options.
While it mentions multi-modal, the primary focus appears to be on text and image search.

Use Cases

1Adding semantic search to an e-commerce platform.
2Building AI-powered knowledge bases for internal company data.
3Enabling natural language querying of large document repositories.
4Integrating RAG capabilities into customer-facing AI agents.
5Quickly prototyping and launching AI features that require search.

Who Should Use This?

👤AI Developers
👤Software Engineers
👤Product Managers
👤DevOps Engineers
👤Startup Founders

Ducky