Nth AI offers a software platform that automates the implementation layer for enterprise AI, connecting and validating data across systems such as ERP, CRM, HRIS, and data warehouses. By replacing manual, costly integration work with versioned, auditable code, the platform enables AI agents and copilots to be deployed faster and to compound improvements over time. It targets large organizations seeking scalable AI deployment without the $500K‑$5M per‑engagement overhead.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Enterprise Software
Funding
Founders
Product
Problem
Enterprise AI projects stall because integrating diverse systems—ERP, CRM, data warehouses, and more—requires extensive manual coding and validation. This implementation layer is costly, often ranging from $500 K to $5 M per engagement, and must be rebuilt whenever data contexts change, preventing reuse and auditability.
Solution
Nth AI’s Nexus platform provides a software implementation layer that automates the connection, definition, and validation of enterprise data for AI agents. By treating the integration layer as versioned, auditable code, Nexus eliminates the need for costly hand‑crafted connectors and enables rapid deployment of AI copilots and decision tools. The platform leverages durable LLM‑driven agent orchestration to execute multi‑step data integration workflows with retries, gating, and full traceability. This approach turns a one‑off implementation effort into reusable software that compounds value across multiple AI deployments. Nexus supports a wide range of enterprise systems—including finance, HR, analytics, ITSM, and sensor data—allowing organizations to scale AI initiatives without repeatedly rebuilding the data foundation.
Target Audience
Primary customers are large enterprises and mid‑market organizations that operate complex, heterogeneous data ecosystems and seek to deploy AI agents, copilots, or automated decision tools at scale.
Features
- Declarative connectors for ERP, CRM, data warehouses, BI, HRIS, ITSM, sensor, and BMS sources
- Automated schema mapping and data model generation driven by LLM reasoning
- Durable agent orchestration engine with built‑in retries, conditional gates, and audit logs
- Version control and change tracking for integration code, enabling reuse and compliance
- Centralized dashboard for monitoring integration pipelines and validating data quality
- API layer that exposes unified, normalized data to downstream AI agents and copilots