Push.ai provides the infrastructure layer for reliable, production-ready AI agents by establishing a single context layer grounded in a Business Graph™. This platform unifies entities, metrics, and relationships, enabling governed execution and trusted agentic analytics across the organization. The system delivers proactive insights and automated reviews directly into existing workflows, ensuring consistency and control over AI operations.
Funding
Funding not disclosed
WTFounders
Product
Problem
Many businesses struggle to gain a comprehensive understanding of their operations due to data silos and a lack of integration between various data sources. This fragmented view hinders effective performance tracking, informed decision-making, and proactive identification of critical business trends.
Solution
Push.ai offers an enterprise intelligence platform that leverages a Business Graph to create a unified, knowledge-graph representation of a business. By connecting to existing data warehouses, semantic layers, and unstructured data sources like documents and communication channels, Push.ai provides AI-driven analysis and actionable insights. The platform enables end-to-end visibility, allowing users to monitor performance, identify root causes, and collaborate on solutions directly within tools like Slack and Teams. Push.ai's AI algorithms proactively analyze data, identify anomalies, and deliver personalized notifications, ensuring that relevant stakeholders are informed of important changes and opportunities.
Target Audience
Push.ai targets data and business teams, executives, managers, and individual contributors who need a comprehensive view of business performance and AI-driven insights to improve decision-making and collaboration.
Features
- Business Graph: A central, customizable model that integrates metrics, goals, and ownership across the organization.
- AI-powered analysis: Leverages existing data definitions and business context to proactively identify important changes and trends.
- Integration with modern data stack: Connects to data warehouses (e.g., BigQuery, Snowflake), semantic layers (e.g., dbt, Cube, Looker), and communication tools (e.g., Slack, MS Teams).
- Collaboration features: Enables users to chat, refine analysis, and share insights directly within integrated platforms.
- Personalized notifications: Delivers analysis and alerts to relevant stakeholders via Slack, Teams, and email.
- Anomaly detection: Identifies deviations from expected performance, enabling proactive intervention.
- Query Agent: Combines access to structured and unstructured data to answer business questions.