Physical AI thatcloses the loop on your plant.
Imubit is Closed-Loop Physical AI for the process industries. It maps the gaps between plan and plant, models the cause and effect, and controls the key decisions in closed loop, inside the envelope your engineers set.
Imubit is the only commercial solution applying closed-loop reinforcement learning at scale in a production environment.
Named a Leader in the ARC MarketMap 2026: Autonomous Execution Agents, Continuous Process Edition
Your plan depends on recurring decisions.Different people make them every shift.
Every shift, day and week, people with different information and experience make the same decisions differently. Results change with who is on shift and what they know, and that is where the gaps between the plan and the plant come from.
See how the gaps happen
- Safety
The operating envelope, and procedures for the abnormal.
An overly conservative envelope, unexpected disturbances, and reactive, disjointed decisions.
- Reliability
Balance degradation with production and asset utilization.
Static hard constraints for asset health, decoupled from day-to-day objectives.
- Margin
Optimize the economic and operational tradeoffs.
Constraint giveaway invisible to operations, and sluggish reoptimization after upsets.
- Compliance
Integrate compliance metrics into day-to-day operations.
Environmental limits managed either reactively or too conservatively.
- Competency
Formalize complex operation into repeatable procedures.
Cyclical generational gaps, tribal knowledge, and personal preferences and opinions.
Decisions that keep up with the plant.
Imubit learns the plant from its own data and moves the key decisions every minute, across every unit, inside the envelope your engineers set. What Closed-Loop Physical AI means
Each unit runs to its own target, on its own schedule.
Map, Model and Control your gaps to plan.
Three problems every plant works around, and what changes when Imubit takes them on.
Plan-to-plant gaps, traced to recurring decisions.
Map analyzes the plan and its intent across every source and ranks the gaps by impact and recurrence. It ties each recurring decision and disturbance to measured outcomes, so every team works from one map of objectives, constraints, decisions and outcomes.
Read the transcript
Map is where Imubit starts. Every plant runs to a plan, built on economic and operational assumptions. If the plan lives in multiple places, Map pieces them together. Then Map looks at how the plant really runs, across the whole facility, and finds the gaps between the two.
It traces each gap to its causes, and to the recurring decisions behind it. It ties every objective, concern and constraint to those decisions, to the disturbances and the measured outcomes in the plant, and to the people who own each one. What looked like a monolith becomes a map you can act on: which gaps matter most, and which decisions to take on first.
- The gaps between plan and plant, across the whole facility
- The recurring decisions behind each gap
- Who owns each decision, and where to start
The cause and effect behind each gap.
Nonlinear, dynamic AI process models, built on your own history and guided by first principles, with every learned relationship visible. Deterministic causality, not correlation, links decisions, disturbances and outcomes, and your own engineers train the models.
Read the transcript
Model is the cause and effect behind the map. It shows how each decision moves each outcome, against its objective and its constraints. These relationships are nonlinear and dynamic. They change over time, and plant data is never complete, so data alone is not enough.
That is why first principles are built into every model, and nothing sits in a black box. Every relationship is visible, causal rather than correlated, and deterministic. A Deep Learning Predictor learns each unit's dynamics from years of its own data. And with DLPC Builder, your process engineers build models of their own units, without Python or a machine-learning background.
Your engineers can make their own models and create their own competitive advantage.
- Cause and effect your engineers can check
- Models that hold where the data runs out
- Models your engineers build and own
Key decisions executed, gaps closed.
AI controllers learn to steer the key decisions toward your objectives, within your constraints. Reinforcement learning across every process regime is captured in a deterministic controller your engineers certify, running inside the limits they set, and the models are retrained as the plant changes.
Read the transcript
Control is where the decisions get executed. AI controllers take the key recurring decisions behind your gaps, and steer them around the clock, minute by minute, to maximize your objectives inside your constraints, through your existing APC and DCS. Reinforcement learning trains them across every regime the unit has run, millions of simulations and thousands of simulated years, before anything reaches the plant. What ships to the plant is a deterministic controller your engineers can certify and validate.
It does not hallucinate. Given the same inputs, it gives the same move, to every person, every time. Closed loop always runs inside the envelope your engineers set. And every gap you close is measured, continuously, against the plan.
- The key recurring decisions executed, around the clock
- Deterministic controllers your engineers certify
- Always inside the envelope your engineers set
Build on a model you can control, audit and reproduce.
Your process engineers build a model of each unit from the plant's own history, and the model is deterministic: the same inputs give the same answer, every time. That makes it a base anyone on the team can build on, from a lab value predicted every minute to a closed-loop controller.
Always inside the envelope your engineers set.
Imubit works on top of your APC where you have one, or directly on your DCS, never around them, and your operators can take over at any time. If anyone offers you closed loop, ask one question: does it move the setpoint every minute, within the limits your engineers set?
Imubit recommends each move. Your operator reviews it and applies it by hand.
Thirty years on the unit.
Operators and engineers make decisions about the unit on every shift, and after decades much of that judgment sits with a few people. Each team also built its own tools, so numbers still move between teams by hand.
Now they are retiring.
It has more than doubled in twenty years, and retirement rates have surged. When they go, thirty years of judgment about the unit goes with them.
Source: McKinsey, July 2025
What they knew can stay.
Years of their decisions live in the unit's own data. The model learns from that history, so what they knew stays in the model that runs the plant.
“We have these younger, newer engineers that are coming in place of 30 year employees, who no longer have to wait 30 years to acquire that experience. With Imubit, they have an opportunity to come in and make their mark on the organization.”
Across the process industries.
Refining and gas processing
Crude, FCC and hydrotreating units, and gas plant fractionation.
See refining and gas
Chemicals and polymers
Steam crackers, polymer reactors, grade transitions and separation trains.
See chemicals
Cement
Raw mix, kiln, clinker cooler, alternative fuels and finish mill.
See cement
Mining and minerals
Grinding, flotation, leaching and smelting.
See miningWhat closing the gap is worth.
Petroleum coke calcining, in closed loopRead the Oxbow story
“Working with Imubit allows us to leverage cutting-edge AI to optimize our renewable processes while minimizing our carbon footprint.”
Close the gap between the plan and the plant, and keep it closed.
Every operations leader inherits the same gap. The plan says one thing, the plant does another, and the difference is decided shift by shift by whoever is on.
Imubit closes it one step at a time. Map shows where your plant departs from its plan and which recurring decisions cause it, Model shows what each decision does to each outcome, and Control executes the key decisions in closed loop, inside the envelope your engineers set.
Each step builds on the one before and hands your team something it can use and you can show. Day one brings a measurable gain, and the gain grows as the models learn your plant.
What engineers ask first.
Let’s get startedFive questions to ask any industrial AI vendorWhat is Closed-Loop Physical AI?
AI that learns how a physical process responds to each move, then runs the key recurring decisions as closed-loop autonomous control, always inside the envelope your engineers set. Your operators can take over at any time.
Read the full answerHow does Imubit work with our APC and DCS?
On top of your APC where you have one, or directly on your DCS where you don’t, and never instead of either. Imubit writes each move through the control layer you already have.
Read the full answerDo our operators stay in control?
Yes. Your engineers set the limits, and your operators decide when the controller runs and can take over at any time.
Read the full answerWhat data does Imubit need, and does it have to be clean?
Your historian data and lab results, sent as they are. Our engineers screen and clean them in the platform and tell you if anything limits a use case.
Read the full answerHow is Imubit different from AI chat assistants?
Imubit is deterministic: under the same conditions it gives the same move, every time. Your assistants can ask its models questions, and closed loop never runs through a language model.
Read the full answerDo our engineers need to be data scientists?
No. Your process engineers build, test and retrain models of their own units with DLPC® Builder.
Read the full answerFind out what your gap to plan is costing you.
We start with the few decisions that cost your plant the most, and show you what closing them is worth before you commit.
One to two hours for your team to prepare and upload the data package
Results within a few working days of approval
No workshops
Send your data as it is. Gaps, outages and noisy tags are normal, and our engineers screen and clean them in the platform. If anything limits a use case, we tell you.