VERIONICS

Engineering Analysis Platform

Verionics is a physics-driven engineering analysis platform for industrial data processing, simulation and decision support. The platform combines finite element modelling, machine learning and agent-ready workflows to accelerate engineering analysis while keeping results traceable and physically interpretable.

Document type
Technical datasheet
Product class
Engineering analysis software
Status
Early engineering platform
Primary domain
Industrial computation

PURPOSE

  • Engineering data analysis
  • Signal processing
  • Simulation
  • Digital twins
  • Industrial diagnostics
  • Decision support

TARGET APPLICATIONS

  • Pipeline inspection and ILI
  • NDT
  • Industrial diagnostics
  • Scientific computing
  • Digital twins
  • Measurement systems
  • Sensor data analysis

CORE TECHNOLOGIES

Technology Role in the platform Engineering output
Finite Element Method (FEM) Numerical simulation of geometry, materials, fields and boundary conditions. Model state, calculated fields, derived engineering quantities.
Machine Learning Pattern extraction, assisted classification and model calibration where data quality supports it. Candidate labels, fitted parameters, confidence information.
Physics-based modelling Explicit assumptions, units, constraints and governing relationships. Physically interpretable results and known limits.
Inverse problem solving Estimate hidden geometry or state from measured signals and model constraints. Recovered parameters with residuals and verification checks.
Numerical optimisation Parameter search, fitting, sensitivity studies and calibration loops. Repeatable parameter sets and comparison records.
Agent workflows Structured task execution through stable context and command interfaces. Reusable scripts, preserved context, human-readable logs.
Automation Batch processing, report preparation and repetitive engineering operations. Consistent outputs across projects and runs.
Data visualization Inspection of signals, model fields, residuals, tables and engineering state. Reviewable visual evidence tied to source data.

DESIGN PRINCIPLES

  • Physics first. Calculations start from engineering assumptions, units and constraints.
  • AI assists engineers. Automation supports engineering judgement rather than replacing it.
  • Traceable calculations. Results remain connected to source data, scripts and assumptions.
  • Context-aware workflows. Project state is explicit and reusable between tasks.
  • Deterministic where possible. Repeatability is preferred when the task allows it.
  • Transparent limitations. Model scope and uncertainty are part of the output.
  • Engineering before marketing. The platform is described by what can be checked.

AGENT-READY ARCHITECTURE

Verionics is designed to be equally convenient for engineers and AI agents. High development speed comes from organised engineering context, stable commands and reusable scripts, not from opaque automation.

Structured project context
Stable command interface
Scriptable workflows
Reusable engineering scripts
Predictable automation
Human-readable outputs
Machine-readable outputs
Context preservation between tasks

CAPABILITIES

Data importProject files, measurement data, tables and generated artefacts.
Data normalizationUnits, coordinate systems, channel alignment and schema handling.
VisualizationSignals, fields, maps, tables, residuals and model state.
Numerical analysisFitting, sensitivity analysis, statistics and parameter studies.
SimulationFEM-backed studies and physics-oriented computational workflows.

WORKFLOW OPERATIONS

Signal processingFiltering, feature extraction, comparison and calibration support.
Batch processingRepeatable execution across data sets and parameter variants.
AutomationCommands, scripts, generated reports and project checks.
Custom workflowsTask-specific modules for inspection, modelling and reporting.
ReportingHuman-readable summaries with source links and computation records.

ARCHITECTURE

Data
Processing
Physics
Machine Learning
Engineering decision

TRACEABILITY CHAIN

  1. Input data and project context are recorded.
  2. Processing steps are executed through scripts or stable commands.
  3. Physics assumptions and model limits remain visible.
  4. Automated assistance produces reviewable intermediate outputs.
  5. Engineering decisions are linked back to evidence.

EXTENSIBILITY

  • Custom modules
  • Python integration
  • External tools
  • Automation scripts
  • Agent integrations
  • Project-specific reporting

CURRENT STATUS

Early engineering platform under active development. Designed with long-term industrial applications in mind.

ROADMAP

  1. 01 Physics engines
  2. 02 Inverse problem framework
  3. 03 Advanced visualization
  4. 04 Digital twins
  5. 05 Multi-agent workflows
  6. 06 Industrial deployment