Mandala is an AI-powered data insights platform that uses graph machine learning and GraphRAG technology to let business users query complex enterprise data through natural language. The platform automatically translates questions into code-backed queries and model actions, enabling advanced analytics like link prediction, node ranking, and pattern recognition without requiring data science expertise. It provides a conversational interface that maps real-world context around data to deliver actionable insights.
Funding
Funding not disclosed
Founders
Product
Problem
Advanced graph machine learning and data science capabilities have traditionally been restricted to specialized internal research teams and academic institutions with deep technical expertise. Business users who need to extract insights from complex enterprise data are often locked out of these powerful analytical tools, forcing them to rely on manual analysis or simplified dashboards that fail to capture the full context and relationships within their data.
Solution
Mandala democratizes graph data science by providing an AI-powered platform that lets users interact with their enterprise data through natural language questions. The system's GraphRAG technology automatically converts user queries into code-backed queries and model actions, enabling complex operations like link prediction, node ranking, and pattern recognition through simple conversational prompts. Mandala's architecture builds a contextual map around data that mirrors human reasoning, allowing the system to understand relationships and deliver insights that traditional analytics tools miss. The platform handles the technical complexity behind the scenes, so users can ask sophisticated questions and receive meaningful answers without needing a PhD in data science.
Target Audience
Business intelligence teams, product managers, and enterprise decision-makers who need advanced data insights but lack specialized data science expertise, as well as organizations seeking to make graph machine learning accessible across their workforce.
Features
- GraphRAG technology that translates natural language questions into executable code-backed queries and model actions
- Link prediction capabilities that analyze historical data relationships to forecast product performance and market outcomes
- Node ranking and centrality algorithms that identify the most impactful factors, such as demographics or market trends, shaping business outcomes
- Pattern recognition engine that surfaces trends and behaviors driving success, from product features to market strategies
- Conversational interface that builds a contextual map of real-world relationships around enterprise data for human-like reasoning