03 — DATA PRODUCTS
AI that can engineer. Not just explain engineering.
Artificial intelligence can generate technical explanations, write code, interpret drawings, and propose designs. But engineering demands something more: mathematical correctness, physical feasibility, manufacturing constraints, reliable measurements, and systems that function in the real world.
GYRATE is building expert-verified datasets, engineering training environments, reinforcement-learning tasks, and evaluations for AI systems working across engineering, manufacturing, robotics, and the physical sciences.
Our focus is on teaching AI systems to perform real engineering work: understand requirements, develop designs, use engineering software, solve physics problems, evaluate manufacturing decisions, and verify results against measurable constraints.
Beginning with our expertise in electric machines, electromechanical systems, and industrial manufacturing, our ambition is to develop training and evaluation infrastructure spanning increasingly complex physical technologies.
From engineering knowledge to verified physical intelligence.
OUR DATA PRODUCTS
Sixteen ways to teach AI how engineering works.
- 01
Expert-Verified Engineering Datasets
Structured training data created and reviewed by engineers with expertise in the relevant discipline.
Datasets designed around actual engineering work: calculations, requirements analysis, design decisions, technical drawings, component selection, simulation, manufacturing planning, and failure diagnosis.
Each task can include defined inputs, expected outputs, worked solutions, engineering assumptions, references, and verification criteria.
APPLICATIONSSupervised fine-tuning, technical reasoning, domain adaptation, and engineering-agent development.
- 02
Engineering Rubrics and Automated Verifiers
Evaluation systems designed to distinguish technically correct engineering work from plausible but incorrect answers.
Expert-written grading rubrics assess assumptions, calculations, units, safety factors, physical constraints, design trade-offs, and technical justification.
Executable verifiers check properties that can be tested objectively, including equation consistency, numerical tolerances, geometry validity, constraint satisfaction, and simulation outputs.
APPLICATIONSReinforcement learning, reward-model development, engineering-agent verification, and automated evaluation.
- 03
Physics-Based Reinforcement Learning Environments
Interactive environments where AI agents learn engineering skills through repeated design, analysis, simulation, and feedback.
Tasks can require agents to optimize motor geometry, reduce structural mass, improve thermal performance, tune controllers, or satisfy competing performance and manufacturing constraints.
Environments are built around measurable objectives, physical constraints, and defined success conditions.
APPLICATIONSReinforcement learning for design optimization, physics-based decision-making, and autonomous engineering workflows.
- 04
Engineering Tool-Use Environments
Training and evaluation environments that require AI agents to operate real engineering software and tools.
These environments target workflows involving CAD modelling, numerical computation, electromagnetic analysis, finite-element analysis, computational fluid dynamics, engineering scripting, and manufacturing planning.
Agents must select appropriate tools, prepare valid inputs, execute operations, interpret results, identify failures, and revise their work.
APPLICATIONSEngineering agents, tool-calling reinforcement learning, multi-step automation, and software-based engineering execution.
- 05
CAD and Mechanical Design Data
Datasets for understanding, generating, modifying, and evaluating engineering geometry.
Tasks span parametric modelling, mechanical components, assemblies, engineering drawings, dimensioning, tolerances, material selection, and design-for-manufacturing.
Evaluation considers more than visual similarity. A CAD model must have valid geometry, correct interfaces, appropriate dimensions, and properties consistent with its engineering requirements.
APPLICATIONSText-to-CAD, drawing-to-CAD, CAD editing, mechanical design agents, and geometry verification.
- 06
Electrical and Electromagnetic Engineering Data
Specialized data covering electrical machines, electromagnetic fields, magnetic circuits, electrical systems, power electronics, and motor control.
Training tasks can involve motor sizing, winding selection, torque calculations, efficiency estimation, losses, thermal constraints, electrical architecture, and machine performance analysis.
Solutions are evaluated against appropriate circuit theory, electromagnetic models, numerical methods, and engineering constraints.
APPLICATIONSMotor-design agents, electromagnetic reasoning, electrical engineering models, and machine optimization.
- 07
Engineering Simulation and Scientific Computing Data
Datasets built around computational engineering and physics-based analysis.
Supported task areas include structural mechanics, thermal analysis, fluid mechanics, electromagnetics, dynamics, and multidisciplinary simulation.
Training examples can include model preparation, mesh selection, material parameters, boundary conditions, solver configuration, convergence checks, numerical results, and engineering interpretation.
APPLICATIONSSimulation agents, scientific machine learning, engineering foundation models, and physics-informed model development.
- 08
Manufacturing Process and Design-for-Manufacturing Data
Training data that connects engineering designs to the processes required to manufacture them.
Tasks cover material and process selection, machining, fabrication, electric motor production, tooling, tolerancing, process sequencing, assembly planning, and manufacturability analysis.
The objective is to help models evaluate whether a design can be produced with specified equipment, processes, tolerances, costs, and quality requirements.
APPLICATIONSManufacturing engineering agents, process planning, design-for-manufacturing analysis, and production preparation.
- 09
Industrial Automation and Machine-Tool Environments
Training and evaluation environments for agents operating industrial software and simulated production equipment.
Tasks can include machine programming, toolpath validation, fixture planning, production scheduling, robot programming, PLC logic, and equipment coordination.
Evaluation checks whether proposed operations satisfy equipment constraints, process requirements, safety conditions, and defined production objectives.
APPLICATIONSIndustrial automation agents, machine-tool workflows, manufacturing software automation, and production engineering.
- 10
Robotics and Physical AI Environments
Physics-based environments for robotic control, manipulation, and industrial assembly.
Tasks can involve motion planning, grasping, component placement, insertion, fastening, actuator control, collision avoidance, and handling variation in physical properties.
Training and evaluation incorporate appropriate simulation, contact dynamics, task constraints, and quantitative performance measurements.
APPLICATIONSRobot learning, industrial manipulation, assembly planning, reinforcement learning, and sim-to-real research.
- 11
Manufacturing, Inspection, and Test Data
Structured datasets connecting engineering intent with measured manufacturing and product performance.
Potential sources include dimensional inspection, motor testing, thermal measurements, vibration analysis, quality records, production process parameters, and functional validation results.
Such data can help models understand the differences between predicted performance, manufactured characteristics, and measured outcomes.
Only appropriately authorized data may be used for external training or licensing.
APPLICATIONSQuality prediction, engineering model validation, manufacturing analysis, fault detection, and physical-world model development.
- 12
Multimodal Engineering Data
Datasets requiring AI systems to reason across multiple technical information formats.
Inputs may include engineering drawings, CAD geometry, photographs, simulation images, thermal maps, inspection reports, sensor signals, schematics, tables, and technical documents.
Tasks focus on extracting correct engineering information, understanding relationships between representations, and producing technically useful outputs.
APPLICATIONSMultimodal engineering models, visual inspection, technical document understanding, and engineering-agent perception.
- 13
Engineering Preference Data and RLHF
Expert comparisons that capture the trade-offs involved in real engineering decisions.
Engineers evaluate alternative solutions against explicit criteria such as physical correctness, reliability, manufacturability, cost, efficiency, safety, and requirement satisfaction.
The resulting preference data can support reinforcement learning from human feedback and preference-based model optimization.
APPLICATIONSPreference training, reward modelling, technical decision-making, and engineering model alignment.
- 14
Engineering Benchmarks and Custom Evaluations
Benchmarks designed to measure whether AI systems can actually perform engineering work.
Evaluations can test numerical reasoning, design validity, CAD generation, simulation competence, manufacturing decisions, tool use, verification, and completion of multi-stage engineering workflows.
Each benchmark requires clearly defined tasks, grading criteria, reference evidence, and reproducible scoring procedures.
APPLICATIONSFoundation-model evaluation, engineering-agent benchmarking, regression testing, and capability assessment.
- 15
Failure Analysis and Reliability Data
Structured examples of engineering failures, incorrect assumptions, defective designs, manufacturing deviations, and unsuccessful technical decisions.
Tasks can require models to identify failure mechanisms, interpret evidence, trace potential causes, assess reliability, and recommend technically justified corrective actions.
The objective is to improve a model's ability to recognize when and why engineering work fails.
APPLICATIONSFault diagnosis, reliability engineering, root-cause analysis, model error analysis, and technical verification.
- 16
Custom Engineering Data and Training Programs
Training datasets, evaluation tasks, and environments designed around a customer's engineering domain, software tools, workflows, and model objectives.
Potential engagements include defining capability requirements, preparing expert demonstrations, designing verification systems, constructing training environments, evaluating model failures, and preparing data for supervised or preference-based training and reinforcement learning.
APPLICATIONSCustom engineering AI development, specialized model training, industrial AI research, and domain-specific evaluations.
OUR TECHNICAL DOMAINS
Engineering and the physical sciences.
GYRATE's initial technical foundation is in electromechanical engineering and manufacturing, with a broader ambition spanning interconnected engineering disciplines.
Electrical and Electromagnetic Engineering
Electric machines, electromagnetic systems, power electronics, electrical circuits, electric drives, and power conversion.
Mechanical Engineering
Machine design, mechanisms, structural analysis, materials, transmissions, bearings, tolerances, and mechanical assemblies.
Thermal and Fluid Engineering
Heat transfer, cooling systems, thermodynamics, fluid mechanics, thermal management, pumps, and fluid machinery.
Control Systems and Automation
Dynamic systems, feedback control, motor control, industrial automation, robotic systems, and mechatronics.
Manufacturing Engineering
Machining, fabrication, production processes, tooling, quality control, industrial metrology, assembly, and process optimization.
Computational Engineering
Numerical methods, finite-element analysis, computational fluid dynamics, multiphysics simulation, scientific computing, and engineering optimization.
Robotics and Physical AI
Robotic mechanisms, actuators, manipulation, motion planning, robot control, assembly automation, and simulation-based training.
Systems Engineering
Requirements, system architecture, multidisciplinary trade-offs, technical interfaces, verification, reliability, and lifecycle engineering.
Our long-term ambition includes deeper coverage of materials science, electronics, aerospace systems, energy systems, scientific computing, and other physical engineering disciplines as the necessary expertise and validation capabilities are developed.
HOW OUR DATA IS DESIGNED
Correctness must be measurable.
Engineering data cannot be judged solely by how convincing an answer sounds. Our approach is built around four principles.
Expert Knowledge
Tasks should be designed or reviewed by engineers with relevant technical expertise.
The objective is to capture correct engineering methods, decisions, calculations, constraints, and explanations.
Executable Verification
Where possible, engineering outputs should be checked using equations, tests, geometry validation, engineering software, and numerical or logical constraints.
Expert judgment remains necessary where technical quality cannot be reduced to one automated score.
Physical Grounding
Simulation results, manufacturing measurements, and experimental evidence should be distinguished clearly.
Physical observations can provide stronger evidence about real-world performance, but they must be collected and interpreted under appropriate test conditions.
Reproducible Evaluation
Training and benchmark tasks should have documented assumptions, data provenance, evaluation criteria, and clearly defined measures of success.
A model's engineering capability should be demonstrated through repeatable results, not asserted through marketing.
FROM SIMULATION TO PHYSICAL EVIDENCE
Connecting engineering AI to the real world.
GYRATE's long-term advantage comes from the relationship between our Data Products, AI Engineering Workforce, electromechanical Factory, and MIDAS programmable manufacturing vision.
- 01
Engineering AI generates and evaluates designs.
- 02
Simulation estimates physical performance.
- 03
Manufacturing turns designs into physical components and assemblies.
- 04
Inspection and testing measure what was actually produced.
- 05
The differences between expectations and measurements can inform future engineering work.
By connecting these stages, we aim to develop training and evaluation systems grounded not only in engineering knowledge, but also in the measurable consequences of engineering decisions.
This requires careful handling of intellectual property, confidentiality, data quality, and permissions. Customer designs, factory records, and private engineering information are not automatically training or licensing assets.
Our objective is to create reliable, legally usable engineering intelligence products that contribute to increasingly capable physical AI systems.
WHO WE ARE BUILDING FOR
Teams building engineering and physical intelligence.
Frontier AI Laboratories
Developing models that can reason about engineering, use technical tools, and solve physical-world problems.
Engineering AI Companies
Building specialized agents for CAD, simulation, analysis, design, and industrial workflows.
Robotics and Physical AI Companies
Developing systems for manipulation, control, assembly, motion planning, and real-world operation.
Industrial and Manufacturing Companies
Applying AI to engineering processes, production planning, inspection, quality, and industrial automation.
Scientific AI and Research Organizations
Developing models for computational physics, scientific computing, simulation, and engineering research.
Engineering Software Companies
Improving intelligent design tools, engineering assistants, automated analysis, and technical software workflows.
OUR AMBITION
Building the intelligence infrastructure for the physical world.
Software engineering AI learns from executable programs, tests, repositories, and developer workflows.
Physical engineering requires a much broader foundation.
AI must understand mathematics, geometry, materials, forces, energy, motion, physical constraints, manufacturing processes, and the differences between simulation and reality.
It must learn not only how to propose solutions, but how to evaluate whether those solutions are correct, feasible, manufacturable, and verifiable.
GYRATE is building toward a world where increasingly capable AI systems can participate meaningfully in the engineering and manufacturing of real physical technologies.
Our Data Products are intended to provide the training material, environments, expert assessments, and evaluation infrastructure that make those capabilities possible.
Our long-term goal is to connect engineering expertise, computational physics, industrial processes, and real manufacturing evidence into reusable foundations for engineering AI.
Not simply AI that knows engineering terminology.
AI that can perform engineering work—and whose results can be tested.
GYRATE DATA PRODUCTS
Engineering knowledge. Physical constraints. Verifiable intelligence.
Request access to browse our engineering datasets, evaluations and training environments as they become available.
For research and development collaborations, contact GYRATE to discuss a specific engineering dataset, evaluation, or training-environment requirement.