Data Graphs: Knowledge Graph Platform
Data Graphs helps organizations manage complex datasets by transforming raw information into a structured knowledge graph. It is designed for businesses needing to integrate diverse data sources for AI-driven insights.
At a glance
- Category
- Browse Data & Analytics tools
- Best for
- Small Businesses, Mid-Market Companies, Enterprise Companies, Data Stewards, Operations Managers
- Pricing
- Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- Knowledge Hub, Reference Data Management, Multi-Modal Asset Management, Digital Labeling, Sports Analytics and Engagement
- Official website
- Visit data graphs official website
Data Graphs is a knowledge graph platform designed to organize scattered data into a connected system. It uses a high-performance database and integrated large language models (LLMs) to help users find connections in their data without writing code.
The tool is designed for users across sectors such as media, sports, healthcare, and AgriTech. It helps teams move from raw data to insights through semantic search and natural language querying.
Buyers should note that while the platform offers a no-code UI for modeling and ingestion, the initial architectural setup of data models may require a strategic approach. Organizations should confirm if the API and webhook options align with their existing software stack.
Key Features
No-code intuitive UI
Supports designing data models and defining relationships between concepts without writing code.
Natural language queries
Supports asking questions in plain English to retrieve answers contextualized within business data.
Semantic search
Combines graph metadata and full-text search to provide context-aware results.
RAG model integration
Supports Retrieval Augmented Graph (RAG) to provide structured context to LLMs.
Graph query engine
A native database engine designed for the retrieval of complex, interconnected data.
Scalable APIs and webhooks
Provides developer-friendly integration points to connect the knowledge graph to other business applications.
Use Cases
Knowledge Hub
Centralizing content, metadata, and insights to support team collaboration and decision-making.
Reference Data Management
Managing and publishing industry-standard and company-specific reference data in a unified base.
Multi-Modal Asset Management
Organizing various types of digital assets and content using AI-powered connections.
Digital Labeling
Creating machine-readable labeling ecosystems, such as those used for agricultural product compliance.
Sports Analytics and Engagement
Unifying fan interactions, player stats, and media assets to support personalized fan experiences.
Best For
- Small Businesses
- Mid-Market Companies
- Enterprise Companies
- Data Stewards
- Operations Managers
Pricing
Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
What is Data Graphs used for?
- It is used to transform raw, scattered data into a structured knowledge graph, enabling businesses to use AI and natural language queries to find insights.
Does Data Graphs require coding knowledge?
- The platform features a no-code intuitive UI for designing data models and importing data.
Which industries use Data Graphs?
- It is designed for several sectors, including Media & Entertainment, Sports, Healthcare, Pharmaceuticals, and AgriTech.
Source category: Data & Analytics
Source subcategory: AI Search
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Categories
Software Type
How AI is used
Data Graphs is an AI-powered knowledge graph platform that helps businesses connect diverse data sources. It supports workflows like RAG integration and natural language querying to help users find insights without coding.
Pros & Cons
Pros
- No-code interface for data modeling and ingestion.
- Supports natural language queries for non-technical users.
- Designed for query performance with complex data.
- Includes integrated LLMs grounded in company data.
Cons
- Initial setup may involve a learning curve for data modeling.
- Pricing information is not clearly available from the provided evidence.
- Cloud or hardware requirements for deployment are not specified.
