Use Cases

See how organizations transform their scattered, unstructured data into powerful AI-native search engines. These real-world examples show Vector Stores solving common data discovery challenges across different industries and use cases.

Make Support Documentation Instantly Searchable

Challenge: Complex product documentation, FAQs, and troubleshooting guides are buried in different systems, making it hard for users to find relevant help quickly.

Solution: Transform your scattered documentation into an AI-native search engine. Upload PDFs, wikis, video transcripts, and help articles to create a unified knowledge base that understands user questions in natural language.

Results:

Users find answers instantly with queries like "How do I reset my password?" or "Why is my payment failing?"
Dramatic reduction in support tickets and improved user satisfaction.
Support agents can also search the same system for faster resolution times.

Transform Product Catalogs into Intelligent Shopping

Challenge: Customers struggle to find products using traditional keyword search, especially when they describe what they need rather than specific product names.

Solution: Make your entire product catalog naturally searchable. Upload product descriptions, specifications, images, and customer reviews to create an AI-powered shopping experience that understands intent like "comfortable shoes for long walks" or "budget laptop for students".

Results:

Customers discover relevant products faster, increasing conversion rates.
Reduced bounce rates as shoppers find what they're looking for immediately.
Works with product images, descriptions, and even customer review content for comprehensive search.

Make Historical Support Data Actionable

Challenge: Years of support tickets, knowledge base articles, and resolution guides are locked away in different systems, forcing agents to search manually or rely on memory.

Solution: Create a unified search engine from all your support data. Historical tickets, internal guides, and product documentation become instantly searchable, allowing agents to find similar cases and proven solutions in seconds.

Results:

Agents resolve issues faster by finding relevant past cases with natural language queries.
Consistent service quality as all agents access the same knowledge base.
New agents get up to speed quickly with searchable institutional knowledge.

Turn Enterprise Data Chaos into Searchable Knowledge

Challenge: Critical business information is scattered across Google Drive, SharePoint, Slack, wikis, and email, creating information silos that slow down decision-making.

Solution: Create a company-wide search engine that understands your business context. Index documents, presentations, code repositories, meeting recordings, and project files to make institutional knowledge instantly discoverable through conversational search.

Results:

Employees find relevant information with queries like "Q3 marketing budget analysis" or "API documentation for payment service".
Cross-team collaboration improves as knowledge becomes accessible to everyone.
Onboarding accelerates as new hires can search for context and examples across all company data.

Overview

Build AI-native search engines that understand any data type. Make documents, images, code, and product catalogs instantly searchable through natural language and AI agents.

Overview

Leverage Mixedbread's Embeddings API to access state-of-the-art models that convert text into powerful vector representations.

Last updated: June 17, 2025