The enterprise AI decision layer
StarLifter continuously monitors data across your existing business systems, using your policies, commitments, and procedures to investigate causes, assess business impact, and recommend evidence-backed actions. Your team approves the response; StarLifter helps carry it out.
Put AI to work with the systems and data you’ve already invested in.
Illustrative demonstration
An alert says a shipment is late. StarLifter brings the affected orders, feasible responses, tradeoffs, and a recommended next step your team can approve.
Decision needed
What changed
A supplier shipment has been delayed.
Business impact
Several customer orders can no longer be fulfilled on their promised dates.
What StarLifter found
Another warehouse has sufficient available inventory after covering its own commitments. A transfer can arrive in time and complies with the company’s same-country transfer policy.
Recommended action
Transfer the available stock rather than expedite replacement supply.
Scenario for illustration. Watch a supply-chain walkthrough
A supplier slips. Demand rises. Inventory sits in the wrong warehouse. Separately, they’re data points. Together, they reveal a customer commitment at risk. StarLifter connects the dots and brings your team a recommended response — while there’s still time to change the outcome.
From signal to prepared decision — with evidence and human approval
Signals from your stack, enriched with the context that matters
Evaluates options against your policies and priorities
Executes the right next step within the guardrails you set
Your ERP tells you what happened. Your BI tells you it’s a problem. Neither was built to reason and act. StarLifter was.
Begin with a concrete operational problem — like protecting customer deliveries — then apply the same approach to other recurring decisions across the business.
Detect risks across inventory, demand, and suppliers, explain what is happening, and turn them into clear, immediate actions.
Detect pipeline risk in real time, generate corrective actions, and synchronize decisions directly into your CRM and revenue systems.
Unified health scores, early warning systems, and intervention recommendations. Catch churn before it happens.
What a prepared decision sounds like
“This product will stock out in 9 days because inbound supply slipped while demand accelerated in three regions. Here are the actions most likely to prevent it.”
Illustrative decision narrative — supply chain
“This account shows elevated churn risk because usage is slowing, support dissatisfaction is rising, and renewal risk is increasing. Here are the actions most likely to prevent revenue loss.”
Illustrative decision narrative — customer health
Closing the loop from enterprise data to governed action.
Data
Systems
StarLifter Decision Control Plane
Continuously evaluates operational signals and initiates governed actions in connected systems
Enterprise Working Memory & Action Orchestration
Context
Semantics
Business Meaning
Ontology
How the Business Works
Metrics
Performance Signals
Decision
Systems of Record & Productivity
StarLifter sits between data systems and systems of record and productivity — continuously evaluating signals and orchestrating governed action with role-based controls.
Founder
Fred Luddy
Founder & CEO
StarLifter was founded by Fred Luddy, founder of ServiceNow and one of the defining architects of modern enterprise workflow software.
After helping global enterprises modernize their workflows, Fred saw a new opportunity: applying AI to the most important enterprise workflow of all — decision-making. He recognized that AI systems could reason across enterprise context in real time and at enterprise scale.
StarLifter was established with the vision to help enterprises move from fragmented, manual decision-making to a system that proactively detects what is changing, understands why it matters, and orchestrates decisions and action across the business.
Governed Intelligence
LLMs help power investigation and recommendations. A prepared decision still needs enterprise context, policy, governance, and a controlled path to action — not prompt-driven Q&A alone.
How it works across existing systems
Connect to the systems you already run. No data migration required.
Watch how StarLifter turns cross-system investigation into a recommended next step your team can approve.