The platform

One model of your plant.

One model every team can check. Advisory in the cloud, closed loop in the plant.

How it works

Map. Model. Control.

Where it sits

A learning layer on top of what you run.

Each layer of plant automation does its job on its own clock. Imubit adds a learning layer that writes setpoints to your existing APC or DCS every minute. Everything else keeps its role.

A learning layer. It learns each unit from its own data and writes setpoints to your APC or DCS every minute, inside the limits your engineers set.

Writes setpoints downReads historian data up

The model

Why historian data is hard to learn from.

Pattern-finding misses four things. The Deep Learning Predictor learns a nonlinear, dynamic model of each unit from years of its own history, with mass and energy balances and the known physics shaping the model where the data runs thin.

Dead time and dynamicsA firing or reflux change shows up in quality minutes or hours later.
Control actionA variable that process control holds tight shows little variance, so it can look as if it has no effect.
NonlinearityThe same move can be profitable at one throughput and unstable near a constraint.
Slow quality feedbackLab results arrive after the process has already moved.
Model evaluation

Why the model moved.

How each input moved the predicted product yield over the test period. All input movements count toward each contribution.

DLPC® Builder

Build a model of your own unit.

Import the data, auto train, validate. A process engineer does it without Python or a machine-learning background.

Open by design

Ask your plant model in plain language.

Use the language models your teams already work with to reach your Imubit models through the Model Context Protocol (MCP). The language model asks. The deterministic model answers, the same way every time.

  • Ask what a change would do before you make it.
  • Analyze the results and see why the model moved.
  • Build models of your own units, and your own analyses and workflows on them.

Part of the platform, not an add-on. Where a site does not use language models, everything works in the platform itself.

See a model built this way
Illustrative
What happens to yield if we raise feed on unit 2?
Deterministic model of unit 2

Yield rises with feed until the outlet temperature reaches its limit, then levels off. Past that point, more feed costs energy for no gain.

Outlet temperature limit
How it learns

Learn. Train. Run.

LEARN

Deep Learning Predictor

Learns the unit from its own history.

TRAIN

Deep Learning Controller

Reinforcement learning, millions of simulations and thousands of simulated years.

RUN

Closed loop

Deterministic, inside limits you certify.

Engineers set the economic objectives and the safety and operating limits the controller has to respect. Scenarios are tested in DLPC® Sim App before go-live, and the same plant state produces the same move every time, so engineers can certify it and operators can anticipate it.

The path

From open loop to closed loop.

Choose the units and handles behind the highest-value gaps.

Going from open loop to closed loop needs no new tools and no new architecture. The controller is trained on the same model your engineers built and tested.

In the plant

Runs inside your plant network.

Running in the plant

  • Runs on-premise, inside the plant network, through the On-Premise Application.
  • Enforces explicit, configured constraints.
  • Scope can be limited to selected handles or a single unit.
  • Operators keep full authority, with a direct path to take the controller out of service.

Built for production

  • Native integration with historians and with DCS and APC environments.
  • Version control and an audit trail for every model.
  • Model and data governance, with role-based access.
  • Performance visibility across assets.
Data security policy
The applications

What your engineers use.

DLPC® BuilderBuild and retrain nonlinear dynamic models from historian data, guided by first principles.
DLPC® ExplorerAnalyze gain relationships and evaluate operating trade-offs.
DLPC® Sim AppTest operating scenarios offline before anything changes on the unit.
DLPC® Performance DashboardMonitor model accuracy, engagement and plant performance.
On-Premise ApplicationRun constraint-aware closed-loop control inside the plant network.
DLPC® MCP & APIReach the models from your own AI tools and applications.