Genzoic
Genzoic builds the Context Graph, a living map of a company's business structure, workflows, and institutional knowledge, enabling enterprise AI to reason about operational decisions rather than just querying data tables. The platform connects entities like products, suppliers, plants, and contracts to help AI answer complex questions such as supply chain impacts or product discontinuation effects. This approach bridges the gap between raw data and the undocumented knowledge that drives real business judgment.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Software Only
Funding
Founders
Product
Problem
Most enterprise AI systems are connected directly to data warehouses like Snowflake or Databricks, allowing them to answer factual questions about historical data but not reason about business operations. The knowledge that drives real decisions—supplier dependencies, plant capacities, product formulations, undocumented workflows, and institutional rules—lives in employees' heads rather than in data tables, creating a gap between what AI can tell you and what it should be able to do.
Solution
Genzoic builds the Context Graph, a living map of a business's structure, workflows, and institutional knowledge that enables AI to reason about what to do next, not just what happened. The Context Graph goes beyond traditional knowledge graphs by encoding entity nodes for products, suppliers, plants, channels, stores, and contracts, along with their interconnections. This contextual layer routes AI to the right action at the right time, allowing it to answer complex operational questions like "Our lithium supplier just filed for bankruptcy—what products are affected?" or "If we discontinue Product Z, what happens to our channel commitments?" By capturing the undocumented rules and judgment calls that typically live in Slack conversations and employee experience, Genzoic enables AI to think like someone who actually works at the company.
Target Audience
Primary customers are enterprise organizations using AI assistants connected to data warehouses like Snowflake and Databricks, particularly those in manufacturing, retail, and supply chain-intensive industries where operational dependencies and institutional knowledge are critical to decision-making.
Features
- Context Graph technology that maps business structure, encodes workflows, and captures institutional knowledge beyond simple data tables
- Entity nodes representing products, suppliers, plants, channels, stores, and contracts with their relationships
- Reasoning capabilities that answer operational questions about supply chain impacts, product discontinuation effects, and channel commitments
- Integration with existing enterprise data infrastructure including Snowflake and Databricks
- Routing mechanism that directs AI to the right action at the right time based on business context