Co-Intelligence | Industry 4.0
Give Our AI Analyst an Objective
Get a Proven Action Plan
Data is growing, but the number of people who can make sense of it is not. Our AI Analysts work alongside your team to turn data into decisions and decisions into profit.
Enabling industrial professionals at all levels to make smarter decisions on shared objectives: Increase On Time in Full Reduce Cost Per Order Reduce Cost Per Ton Improve Perfect Order Rate
Set an objective, like you would with A MEMBER OF YOUR TEAM:
Reduce excess inventory by 10%
Improve On-Time-In-Full (OTIF) by 5%
Increase Overall Equipment Effectiveness (OEE) by 8%
Reduce Yield Loss and Process Waste by 10%
Hear It Straight From the AI Analyst
Meet the platform that keeps your teams faster, smarter, and safer.
Meet Your New Digital Team
- decisions on objectives such as:
Increase On Time in Full Reduce Cost Per Order Reduce Cost Per Ton Improve Perfect Order Rate
How Our AI Analyst Works
- The same workflow as delegating to a human analyst
You Set the Objective
Works alongside your team, not replacing them
Al Auto-Discovers Cause and Effect
Understands WHY things happen, not just WHAT happened
Runs Billions of Simulations
Tests every option before you commit resources
Delivers Proven Actions
Recommendations backed by billions of simulated outcomes
Learns from Real Outcomes
Continuously improves recommendations using real-world feedback
Why The Industry Needs Something New
- Enterprise analytics explain drivers but don’t help you make confident decisions or track what actually worked.
No built-in domain understanding
Requires expensive data science teams
Shows correlations, not causation
You interpret dashboards
Takes weeks to test a scenario
No way to predict outcomes before acting
Includes vision, physics and chemistry-aware models
Works alongside your existing team
Auto-discovers true cause-and-effect
You set objectives, Al finds opportunities
Continuously tests scenarios across objectives
Simulates future outcomes before you act
Frequently Asked Questions
- Most tools show you what happened. ProfitOps tells you what to do next
What is Co-Intelligence?
Co-Intelligence is an always-on intelligence layer where humans and AI Analysts work together to make better decisions. Instead of replacing teams, ProfitOps augments them—combining human judgment with AI that can uncover root causes, simulate outcomes, and recommend proven actions in real time.
What is an AI Analyst?
An AI Analyst is a domain-specific intelligence designed to operate like a senior business analyst. You assign it objectives, not prompts. It understands business context, discovers cause-and-effect relationships, runs simulations, and delivers tested recommendations with predicted outcomes.
How is an AI Analyst different from an AI Agent?
AI Agents typically execute tasks or automate workflows based on predefined rules or prompts. AI Analysts focus on decision intelligence. They reason about why things happen, test multiple scenarios before action, and recommend the best path forward—while keeping humans in control of decisions.
Is ProfitOps built on GPT or a large language model?
ProfitOps does not rely on GPT for decision-making. While language models may assist with explanation or interaction, core intelligence is powered by causal AI models, domain knowledge graphs, and large-scale simulations—not text prediction.
What is the Stability Layer?
The Stability Layer is ProfitOps’ system that continuously monitors operational signals across planning, sourcing, manufacturing, fulfillment, and finance. It ensures recommendations improve flow stability, prevent unintended consequences, and protect overall business health—not just local optimizations.
How does ProfitOps find root causes, not just correlations?
ProfitOps uses causal graphs embedded with domain expertise to identify true cause-and-effect relationships. This allows the platform to explain why performance issues occur, not just highlight correlated metrics or anomalies.
Will this replace my analysts or operations team?
No. ProfitOps is designed to work alongside your existing teams. It removes manual analysis and trial-and-error, allowing human experts to focus on judgment, execution, and strategic decisions.
How does the AI learn over time?
After actions are accepted or rejected, ProfitOps measures real-world outcomes against predictions using causal attribution. Successful actions are rewarded, failed ones are penalized—continuously improving future recommendations.