MAÂT Discover
Diagnosis and feasibility
- Inventory of available machine data
- Digital-twin feasibility opinion
- Prioritised use cases and value hypotheses
Connect industrial machines, understand their physical behavior, simulate optimized configurations and compare real performance with engineering-validated possibilities.
The industrial challenge
Performance losses rarely show on a dashboard. They hide in the physics of motion.
How it works
Every step produces verifiable evidence before the next one.
Connect the real machine or import its data.
OPC UA, Modbus TCP, MQTT, read-only; signals mapped, units converted to SI.
Real machine vs optimized digital twin
Three synchronised behaviors: the real machine, its calibrated twin and its optimized twin. Pick a quantity, a scenario, show the constraints.
| Scenario | Real cycle | Simulated optimized cycle |
|---|---|---|
| Pick-and-place gantry — nominal conditions | 3.96 s | 3.06 s |
| Same gantry — increased friction (simulated wear) | 3.96 s | 3.06 s |
| Heavy cartesian palletiser | 8.31 s | 6.29 s |
The platform
Every suite relies on the same calibrated twin: what one computes, the others can check.
OPC UA, Modbus TCP, MQTT. Read-only, encrypted sessions, verified certificates.
ExploreVersioned passport: architecture, axes, drives, recipes, validity domain.
ExploreCalibrated dynamic model, fidelity score, governed versions and rollback.
ExploreSpeed, acceleration and jerk profiles optimised under torque and deflection constraints.
ExploreMotion autopsy, drifts, wear indicators. Prediction needs real history.
ExploreOEE, speed losses, micro-stops linked to the physical cycle.
ExploreEnergy per phase and per axis, costed reduction scenarios.
ExploreMulti-objective optimisation (NSGA-II) on the physical model, robustness and trade-offs made explicit.
ExploreLoads, friction, recipes: test without stopping production.
ExploreSourced answers on site data. Current version is rule-based, without generative AI.
ExploreCompare machines and sites, transfer validated recipes.
ExploreSimulated, estimated, measured and validated gains always kept apart.
Explore“Available”: implemented in MAÂT and proven on virtual machines, synthetic and simulator data. “Pilot”: offered with a validation plan. No gain has been measured at a customer yet.
Industries
Typical use cases per sector. These are intended applications, not customer references.
Machines modelled today are cartesian gantries (pick & place, palletisers, CNC routers). Other architectures require a dedicated model.
Solutions by role
One physical truth, presented in each profession's language.
Economic vision and investments
Technology & trust
What is in place today, and what is in development.
MAÂT neither controls nor writes any machine parameter. Every change remains a human decision.
OPC UA in SignAndEncrypt with verified certificate thumbprint; Modbus TCP and MQTT read-only.
Connectors declared by an administrator, disabled by default, traced activation.
Roles and per-site scopes, strong authentication (MFA), OIDC SSO, separation of duties.
Per-company isolation at database level (Row-Level Security), hosting in the European Union.
SHA-256 chained audit log; time-stamped validation dossiers.
Twin versions, validation gate (R² ≥ 0.95, NRMSE ≤ 5%), activation by another engineer, rollback.
Validity domain, constraint margins, model-vs-real gap shown with every recommendation.
Execution next to the machines for isolated sites.
ROI & business value
Every formula is shown. The result is indicative: only a pilot measures a real gain.
▲ Indicative estimate based on your assumptions. No result is guaranteed.
Pricing
A diagnosis, a pilot on one machine, then a subscription sized to your fleet.
Diagnosis and feasibility
One machine, one use case, evidence
First digital twins, up to 3 machines
Multi-machine optimisation, up to 10 machines
Industrial fleet, up to 30 machines
Multi-site, central governance — on quote
| Starter | Professional | Industrial | Enterprise | |
|---|---|---|---|---|
| Machines | 3 | 10 | 30 | 100 |
| Sites | 1 | 2 | 5 | 50 |
| Users | 5 | 15 | 50 | 500 |
| OPC UA connectors | 2 | 5 | 15 | 60 |
| Collection frequency | 1 Hz | 10 Hz | 100 Hz | 1,000 Hz |
| History | 12 mo | 24 mo | 36 mo | 60 mo |
| Optimisations / month | 20 | 100 | 500 | 5,000 |
| Availability | Availability target 99.5% | Availability target 99.5% | Availability target 99.7% | Target 99.9% — negotiated contractual SLA |
Prices excl. tax. Initial proposals, subject to HORUS commercial validation. (PB-2026.11-1)
Industrial Pioneer Program
For manufacturers, machine builders and integrators ready to run a rigorous technical pilot on a real machine.
Pilot terms (scope, duration, price) are set out in a written proposal. No free offer or exclusivity is promised.
Partners & OEM
Machine builders, integrators, engineering firms, automation specialists, mechanical experts and technology partners.
Deploy MAÂT at your customers, with a partner simulation environment.
Model new machine architectures with our engineers.
Deliver each machine with its calibrated twin.
Build industry modules together, each party's contributions protected.
Controlled distribution and resale.
Resources
Not yet. Available physical models cover 2-axis cartesian gantries (pick & place, palletisers, gantry CNC routers). Another architecture (6-axis robot, press…) needs a dedicated model, assessed during a Discover diagnosis.
No. MAÂT reads existing data read-only (OPC UA, Modbus TCP, MQTT) without changing the PLC program.
Yes. One-off acquisitions or data exports are enough to calibrate a twin; a continuous connection is used to track drifts.
Axis characteristics (strokes, masses, drives), the motion recipe and, ideally, position, speed and torque traces over a few cycles. The exact list is set during the diagnosis.
Yes. A MAÂT twin is a dynamic model (masses, friction, stiffness, drives) identified from data, not a geometric mock-up.
A 3D model shows the shape. A physical twin computes torques, deflection, energy and cycle time, and shows its measured fidelity against the real machine.
Through multi-objective optimisation (NSGA-II) of motion parameters on the calibrated twin, under torque, speed, deflection and accuracy constraints. Each proposal states its margins, validity domain and risks.
No. MAÂT never writes to a machine. A recommendation goes through a validation dossier, approval by another authorised person and a supervised trial.
Per-company isolation at database level, role-based access with MFA, chained audit log, hosting in the European Union. Data remains the customer's property.
Typically 8 to 12 weeks for one machine and one use case, depending on data access and the possibility of trials.
A fixed-fee diagnosis and pilot, then a monthly subscription based on machines and sites. No overage is billed without a signed amendment.
Yes, with per-site isolation and scopes. Multi-site comparison (FLEET) is currently offered as a pilot.
Discover how MAÂT can help your engineering teams understand, simulate and explore better machine performance.
Company
HORUS builds MAÂT, an industrial physical intelligence system linking data, models, simulation, optimisation and engineering decisions. Automate • Optimize • Scale.