StarLifter offers an enterprise decision layer that ingests live data from systems like ERP, CRM, and data warehouses, applies defined business context and policy guardrails, and generates governed recommendations or automated actions. Users can define objectives and decision logic in plain language, enabling AI‑assisted analysis to surface insights, rank risks, and trigger alerts or write‑backs while maintaining role‑based approvals and audit trails.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to translate raw data from multiple systems into consistent, actionable decisions, often relying on static dashboards, manual spreadsheet joins, or ad‑hoc analysis that lack governance and real‑time execution.
Solution
StarLifter provides an enterprise decision layer that ingests live signals from ERP, CRM, data warehouses, and spreadsheets, applies defined business context and policy guardrails, and outputs governed next‑step recommendations. Users define objectives, constraints, and decision logic in plain language, enabling AI‑assisted analysis to surface insights, rank risks, and suggest actions. The platform then triggers alerts, creates tasks, or writes back updates to operational systems while enforcing role‑based approvals and audit trails. By turning data into reusable decision systems rather than static reports, teams can act faster, maintain consistency across departments, and retain full control over data lineage and policy compliance.
Target Audience
Primary customers are operations, supply‑chain, revenue‑ops, and customer‑success teams in mid‑size to large enterprises that need a governed, real‑time decision engine to replace manual analysis and fragmented dashboards.
Features
- Connectors for major data sources (SAP, Oracle, Snowflake, BigQuery, Salesforce, ServiceNow, CSV files) to aggregate live enterprise signals
- Spreadsheet‑like interface for defining business logic, metrics, and shared vocabularies without heavy engineering effort
- Reusable decision systems that generate alerts, recommendations, or automated write‑backs based on real‑time analysis
- AI‑assisted discovery that highlights anomalies, trends, and risk patterns across supply‑chain, revenue, and customer‑health domains
- Role‑based access controls, change visibility, and traceable history to ensure governance and auditability of decisions
- Workflow integration that routes approved actions to execution systems, supporting human approvals where required
- Built‑in scenario planning and what‑if modeling to evaluate trade‑offs before committing to actions