Answer-first summary49/100 GEO
Weaviate
AI-native vector database.
Your Citability Score
49/100Identity60/100
Evidence45/100
Trust15/100
Freshness50/100
Classification80/100
What to improve to rank higher
- Identity: Add: Logo, Founder / Team, Social links.
- Evidence: Add: At least one evidence link, Multiple evidence sources, Demo URL.
- Trust: Add: Contact information, Support URL, Privacy policy, Terms of service.
- Freshness: Add: Has version number, Launch date provided, Reviewed within 90 days.
- Classification: Add: At least 2 tags.
Promotion (Boost) does not change this score — it only changes ordering. This number reflects real, verifiable citability.
Frequently asked questions
- What is Weaviate?
- AI-native vector database.
- What does Weaviate do?
- Open-source vector database for AI applications with hybrid search, generative modules, and multi-modal support.
- Who is Weaviate for?
- Engineering teams building AI applications, semantic search, and retrieval-augmented generation systems that need scalable vector storage and retrieval.
- Is Weaviate verified?
- Weaviate is listed on CitableHub with a citability score of 49/100, computed from verifiable profile evidence.
Weaviate
InvitedAI-native vector database.
AI InfrastructureCH-VER-967282Listed September 10, 2026
AI-Extractable Summary
What:AI-native vector database.
For whom:Engineering teams building AI applications, semantic search, and retrieval-augmented generation systems that need scalable vector storage and retrieval
Key outcome:Improve AI search and retrieval relevance at scale with a vector database built for semantic, hybrid, and RAG-powered applications
Category:AI Infrastructure
Structured for AI systems to extract and cite.
Citability Score
49/100
60
Identity45
Evidence15
Trust50
Freshness80
Classification55
Impressions
0
Clicks
0
Likes
0
GQI Earned
Citable Outcome
Improve AI search and retrieval relevance at scale with a vector database built for semantic, hybrid, and RAG-powered applications.
About
Open-source vector database for AI applications with hybrid search, generative modules, and multi-modal support.
Target Audience: Engineering teams building AI applications, semantic search, and retrieval-augmented generation systems that need scalable vector storage and retrieval.
Not ideal for: Teams that only need a simple transactional database or a basic app backend without semantic search, vector retrieval, or AI workloads.
What makes it different
- AI-native vector database with hybrid search that combines keyword and vector retrieval in one system.
- Built-in vectorization and integration modules that reduce custom embedding pipeline complexity.
- Open-source core with managed cloud deployment options for flexible implementation.
- Rich metadata filtering and multi-tenancy support for production-grade AI retrieval workloads.
Tags & Classification
semantic searchretrieval augmented generationdocument retrievalrecommendation enginesmultimodal search
machine learning engineersdata engineersbackend developersai product teams
e-commercehealthcarefinancial servicesmedia and publishing
Platform: PlatformModel: Open Source
Links & Transparency
Cite this Project
BibTeX
@misc{citablehub_weaviate,
title = {Weaviate},
url = {https://citablehub.com/p/weaviate},
note = {Listed September 10, 2026. CitableHub ID: CH-VER-967282},
year = {2026}
}APA
Weaviate. (2026). CitableHub Software Index. https://citablehub.com/p/weaviate.
MLA
"Weaviate." CitableHub, 2026, https://citablehub.com/p/weaviate.