Close the gap between your plan and your plant.
Industrial plants run on plans: safety, reliability, margin, compliance, competency. The Imubit Platform maps the plan-to-plant gaps to recurring operating decisions, and takes them all the way to autonomous execution through learning process models.
It's powered by Deep Learning Process Control® (DLPC), our patented approach to nonlinear industrial process modeling, control, and optimization. Your own process engineers model, evaluate, and deploy — no Python, no ML expertise.
Close Your Plan-to-Plant Gaps with
Deep Learning Process Control®
Phase 1 — Map
Your gaps, traced to decisions
Every gap between plan and plant traces back to recurring operating decisions. The Imubit Platform makes that decision logic explicit — mapping objectives and constraints to the decisions, disturbances, and outcomes that drive them, across every unit and team.
Engineers define the relationships between:
▸ Operational decisions
▸ Measured outcomes
▸ Constraints and limits
▸ Strategic objectives
The result is one shared system of record: siloed teams see how decisions propagate across the organization and where tradeoffs appear, with gaps ranked by impact and recurrence.
Phase 2 — Model
The causality behind each decision
With gaps mapped, your engineers build nonlinear, dynamic models from your historical data, guided by first principles — with full visibility into every learned relationship. These models capture causality, not correlation: how decisions and disturbances actually drive outcomes under real constraints, in every operating regime.
Open Loop Execution
In open loop, engineers use the models to guide daily decisions. Teams can:
▸ Compare predicted versus observed plant behavior
▸ Evaluate model performance under disturbance
▸ Adjust constraints and assumptions as operating conditions evolve
Models are versioned and retrainable. Because your own engineers build and own them, every iteration compounds your competitive advantage.
Phase 3 — Control
Decisions executed autonomously, gaps closed
Validated models extend into closed-loop execution without changing tools or architecture. Reinforcement learning across all process regimes is captured in a deterministic controller your engineers can certify and your operators trust — steering decisions to maximize objectives within explicit constraints, with operators holding full authority.
Key characteristics include:
▸ On-premise deployment within plant networks
▸ Native integration with DCS and APC control environments
▸ Explicit constraint enforcement
▸ Operator supervision and control authority
▸ Scoped automation across selected handles or units
Closed gaps are measured continuously against the plan; models retrain as your plant changes.
100+ closed-loop AI applications deployed worldwide.
The largest install base in industry.
Built for Production Control Environments
The Imubit Platform is designed for deployment inside operating plants and built for industrial reliability. Experience gained from the industry's largest base of production closed-loop deployments informs every layer of the system — ensuring stability, controllability, and safe integration with plant operations.
Secure on-premise deployment
Model and data governance
Native historian and API integration
Version control and auditability
Cross-asset performance visibility
Inside the Imubit Platform
The Imubit Platform includes a set of modular applications supporting the full lifecycle of closing plan-to-plant gaps — from mapping to sustained autonomous execution.
DLPC Builder
Develop and retrain nonlinear dynamic models
DLPC Explorer
Analyze gain relationships and evaluate operating tradeoffs
DLPC Simulation
Test operating scenarios prior to deployment
DLPC Performance Dashboards
Monitor model accuracy and plant performance
DLPC On Prem
Enable supervised, constraint-aware automation when activated
See first-hand where your plan-to-plant gaps are today
Identify your highest-impact gaps and how to close them through mapped, modeled, and controlled operating decisions — with an Imubit assessment.