FOR INDUSTRY

Physical AI for problems where every experiment is expensive.

Most machine learning assumes data is cheap. Industrial engineering is the opposite regime: each datapoint is a physical trial that burns machine time, material and engineering hours. This is AI built for that constraint — and validated in it.

DLR · Fraunhofer FKIE · Tier-1 Automotive R&D · M.Sc. (Germany)

Three industries, one underlying problem.

A part that must come out in tolerance, a batch that must meet spec, and a platform that must not fail unannounced are the same engineering question asked three ways: what will this physical system do, and what should we set it to?

Defence & Aerospace

Platforms generate more telemetry than any crew can watch, and the failures that matter announce themselves quietly, long before a limit is breached. The blocker is not detection — it is that an operator will not act on a system that cannot show its reasoning, and cannot be audited afterwards.

  • Telemetry anomaly detection with dynamic, drifting baselines
  • Explainable alarms — which channels and timesteps drove the flag
  • Detections that report their own reliability instead of guessing
  • Structural and aerodynamic surrogates for rapid design iteration

Delivered

Built at the German Aerospace Center (DLR) and evaluated on the public NASA SMAP/MSL benchmark, including the trust-scoring contribution that refuses to raise an alarm built on contaminated evidence.

Satellite telemetry anomaly detection

Automotive & Manufacturing

Geometry, material and process settings are one physical problem owned by three teams with three toolchains. Answering "will this part come out in tolerance, and how do we make it?" means months of physical prototyping, because the solver that could answer it takes too long to be asked a thousand times.

  • Simulation surrogates that turn solver runs into millisecond queries
  • Joint design, material and process optimisation on the same objective
  • Predicted quality fields on the part geometry, at any mesh resolution
  • Vision systems that learn a scene live, without a labelled dataset

Delivered

The core of the founder’s applied R&D: leading the AI team for global OEM projects at a global Tier-1 automotive manufacturer, plus two industry-supervised M.Sc. theses and a deployed blow-moulding surrogate.

Physical AI for Process

Pharma & Process Industries

Batch processes live under the same constraint as a press line: the parameters interact, every experiment is expensive, and the window that produces acceptable output is discovered by experience rather than written down. On top of that sits a validation burden that makes an unexplainable model unusable regardless of accuracy.

  • Process-window search where each trial costs a batch
  • Data-efficient modelling from tens of runs, not thousands
  • Deviation detection with attribution an auditor can follow
  • Surrogates for process simulations inside optimisation loops

Method transfer

No pharma project delivered yet — stated plainly rather than implied. The claim here is method transfer: data-scarce process optimisation and auditable anomaly detection are the same problems solved in the automotive and aerospace work, and the regulated-validation burden is scoped explicitly at the feasibility stage.

How the optimisation works

Every vertical above states what actually backs it. Where a domain has not been delivered in yet, it says so — an engineering buyer checks, and a claim that does not survive checking costs more than it wins.

Built for the data regime you actually have.

The mesh is the model

Geometry enters as a graph, not a feature table, so a change made at one node propagates across the whole part exactly as the physics does. This is what lets a model trained on a handful of parts generalise to a new one, at any mesh resolution.

Data-efficient by construction

The validated models were trained on 3 parts and 64 datapoints — roughly 80% less data than conventional approaches require. When a datapoint is a physical trial, that ratio is the whole business case.

Auditable, or it does not ship

Every system here reports how it reached its answer: attribution over the inputs that drove a detection, the trade-off curve behind a recommendation, and a stated reliability when the evidence is weak. An engineer signs off on reasoning, not on output.

Data-efficiency figures were achieved in applied work at a global Tier-1 automotive manufacturer, where the founder established and led the AI team for global OEM projects.

Nobody signs a deployment before a benchmark.

So the engagement starts with a scoped, paid feasibility study on your own data, with a written go/no-go at the end. If the answer is no, you have that answer in weeks instead of after a year of pilot.

  1. 01

    Scoping call

    45 minutes, technical — no slideware

  2. 02

    Feasibility sprint

    Data audit + benchmark on your data, fixed scope

  3. 03

    Pilot

    One use case, one measured KPI, success criteria in the SOW

  4. 04

    Deployment

    Integration, handover and training for your team

On-premise / air-gapped deploymentNo data leaves your environmentIP and model weights assigned to youFixed-scope, fixed-price stages

You would be talking to the engineer who built it.

Not an account manager, and not a team that will read your problem statement for the first time after the contract is signed.

Vishwas Sharma, founder

Learn from someone who has actually built it.

M.Sc. Autonomous Systems, Bonn-Aachen International Center for Information Technology (b-it) · anomaly detection & Explainable AI for satellite operations at DLR · weightless neural networks on GPU at Fraunhofer FKIE · established and led the AI team for global OEM projects at a Tier-1 automotive manufacturer.

Meet the founder

Bring a real problem statement. The first call is technical.