PLC
Controls & automation support
Fault finding, modifications, commissioning assistance and support for industrial control systems, including Siemens S7/TIA environments and HMI/SCADA integration.
Industrial automation • DataMiner/SNMP • engineering software • AI-augmented development
Robust plant systems for manufacturing lines that need to keep running.
JMTEQ provides practical engineering support across PLC, HMI, DataMiner/SNMP monitoring, database, reporting, integration and product-development work. The focus is simple: dependable control, transparent data and maintainable software for real production environments.
- Automation
- Siemens S7, TIA, WinCC and site support
- Monitoring & Data
- DataMiner, SNMP, SQL Server and reporting
- Software & AI
- Production software and AI-augmented product development
Engineering support for live plant systems
- Traceability and production database systems
- PLC/SCADA and DataMiner fault finding and modification support
- SNMP, Modbus, Eurotherm, load-cell and instrument integration
- Reports, dashboards and operational visibility
- Disaster recovery and supportability documentation
Services
Automation and software work with an engineering-first approach.
JMTEQ bridges the gap between shop-floor control systems and the data/reporting systems needed by engineering, quality and operations teams.
SQL
Production software & databases
VB.NET applications, SQL Server data stores, transaction logging, traceability, test-line data capture, reporting and controlled operator workflows.
MES
Interfaces & plant data
Interfaces between control systems, MES, Oracle/SAP-style data sources and local applications, with attention to validation, retries, audit trails and recoverability.
DMA
DataMiner & SNMP monitoring
DataMiner-facing monitoring solutions, SNMP aggregation, OID polling, driver output files, element/view generation, Visio mapping and operational support packs.
IO
Instrumentation & logging
Data acquisition and diagnostics for live values such as load cells, Eurotherm controllers, Modbus devices, analogue tags, setpoints and process measurements.
REP
Reports & dashboards
Operational reports, production histories, test results, supervisor sign-off records, CSV exports, trend charts and data views that make plant behaviour visible.
AI
AI-augmented product development
Structured use of AI development tools to analyse existing systems, accelerate implementation, investigate faults, produce documentation and take engineering products from an initial requirement through testing and controlled release.
DOC
Engineering documentation
Functional design specifications, commissioning packs, support manuals, disaster recovery notes, network inventories and handover documentation for long-term maintainability.
Capability
Built around real plant constraints.
Manufacturing software rarely lives in isolation. It has to work with PLCs, HMIs, instruments, databases, legacy applications, operators, maintenance engineers and production pressure. JMTEQ work is structured around those constraints from the start.
Typical deliverables are designed to be supportable after commissioning: clear configuration, visible status, controlled logging, sensible diagnostics and documentation that engineers can actually use.
Control systemsSiemens S7 / TIA / WinCC
MonitoringDataMiner / SNMP / Visio views
ApplicationsVB.NET / Windows services
DataSQL Server / Oracle-style interfaces
CommsSNMP / Modbus / instruments / plant networks
AI developmentAnalysis / implementation / verification / release
SupportFault finding / recovery / documentation
AI development
Engineering experience amplified by modern AI tools.
JMTEQ uses AI as an engineering development tool rather than as a substitute for engineering knowledge. Detailed system context, source code, operational evidence and clearly defined acceptance criteria are used to direct the work. Generated changes are then reviewed, tested and refined against the behaviour of the real system.
Used correctly, AI can significantly reduce the effort required to understand established codebases, investigate difficult faults, evaluate implementation options and produce supporting documentation. Its value is greatest when it is combined with the domain knowledge needed to recognise incorrect assumptions and verify the resulting solution.
Context-rich engineering analysis
Large source-code projects, database structures, logs, screenshots, process sequences and existing documentation are considered together. This gives the AI the engineering context needed to reason about the complete system rather than producing an isolated code fragment.
Legacy-system understanding
AI-assisted analysis is used to trace behaviour through established VB.NET applications, SQL Server databases, Oracle interfaces, Windows services and industrial communications code. This supports targeted modification without unnecessarily replacing proven systems.
Controlled implementation
Changes are developed against a known source baseline, with versioned releases, validation checks, diagnostic logging and rollback considerations. AI-generated work is reviewed and corrected using engineering judgement before it is treated as deployable.
Evidence-led fault investigation
Runtime logs, database errors, screenshots, trend data and observations from the live process are used to identify likely failure paths. The investigation can move across application code, SQL, networking, configuration and connected equipment rather than treating each area separately.
Complete product development
AI supports the full path from concept and architecture through coding, interface design, testing, packaging, installation, diagnostics, documentation and operational support. This is particularly effective for specialist products that combine software with networking, embedded systems or industrial equipment.
Engineering documentation
Technical findings can be converted into support manuals, design reports, commissioning procedures, database models, release notes, recovery instructions and customer-facing documentation while retaining the detail needed by engineers.
Working method
More than code generation.
Many AI-assisted development workflows begin and end with asking for a code fragment. JMTEQ uses a more complete engineering process: establish the correct baseline, supply the system context, define the required behaviour, inspect the proposed change, test it against real evidence and preserve a recoverable release.
Understand
Review the existing system, process, constraints and evidence.
Structure
Break the requirement into defined behaviours, interfaces and acceptance criteria.
Develop
Use AI-assisted analysis and repository-level development to implement the change.
Verify
Inspect code, test behaviour, review diagnostics and challenge incorrect assumptions.
Release
Package a controlled version with documentation, installation guidance and rollback protection.
Improve
Use field results and operational evidence to refine the next release.
Applied AI development workflow
- ChatGPT-assisted technical analysis, design review and fault investigation.
- Codex-assisted repository inspection, implementation and local validation.
- Source-controlled and versioned development baselines.
- Analysis of code, SQL, logs, configuration, screenshots and operational sequences.
- Iterative testing against real equipment and live-system behaviour.
- Controlled packaging, deployment, diagnostics and rollback planning.
- Conversion of technical work into maintainable engineering documentation.
Example
From concept to deployable product
Recent product-development work has included an embedded network appliance based on OpenWrt, developed from an initial access-control concept into a managed product with captive-portal onboarding, voucher access, client control, secure administration, physical-switch integration, diagnostic downloads and controlled upgrade packages.
The work required coordination across Linux services, browser interfaces, network routing, firewall behaviour, configuration migration, installation scripts, operational diagnostics and release testing. AI accelerated analysis and implementation, while repeated testing on the target hardware determined whether each release was genuinely usable.
Within industrial automation projects, the same method is used to understand long-established control applications, extend sequencing and calibration behaviour, diagnose SQL and communications faults, and preserve compatibility with operational plant systems.
Have a product idea, difficult legacy system or engineering problem?
JMTEQ can help structure the requirement, understand the existing technology and use AI-augmented development to move the work towards a tested and supportable solution.
DataMiner expertise
SNMP aggregation, DataMiner integration and operational handover.
JMTEQ supports DataMiner-style monitoring environments where reliable polling, clean data structures and maintainable handover are as important as the front-end views. This includes the application layer that gathers live SNMP data and the DataMiner-facing outputs used by drivers, elements, views and Visio pages.
Monitoring systems and aggregator applications
Experience includes VB.NET Windows service applications that poll SNMP devices from configuration, maintain local data stores, generate output structures for DataMiner drivers and provide a supportable route for redundant monitoring deployments.
Project capability
- SNMP aggregator service and support application architecture.
- Configuration-driven polling with OIDs, communities, timeout, retry, poll-channel and poll-period control.
- DataMiner element, view, Visio, Visio page and site-generation file support.
- Driver-facing output files, XML structures, local datastore files and full snapshot outputs.
- Passive-server/redundancy behaviour, service recovery, email alerting and rollback planning.
OID
SNMP polling design
Structured polling of SNMP devices using configured IP addresses, OIDs, read communities, SNMP version, retries, timeouts, poll times and controlled sleep between reads.
DRV
DataMiner driver outputs
Generation of stable output files and XML-style structures for DataMiner drivers, including parameter names, units, scaling/multiplication and grouped values.
VIS
Views, elements & Visio
Support for DataMiner element naming, view organisation, site-generation files, Visio references and Visio page mapping so monitored assets remain navigable.
RED
Redundancy & recovery
Passive-server behaviour, local datastore design, auto-restart service recovery, failover considerations, rollback notes and operational recovery documentation.
APP
Windows service applications
Service/application/shared-code structures for long-running monitoring tools, including practical diagnostics, status visibility and maintainable configuration files.
OPS
Operations handover
Engineering support manuals, installation guides, commissioning checklists, rollback/recovery steps and clear notes for support engineers taking ownership.
Typical work
Project types JMTEQ is suited for.
Focused engineering support for systems that sit between controls, monitoring, data and production operations.
Line-side software changes
Modify existing production applications, add new workflows, extend validation logic and keep changes compatible with live plant operation.
Traceability and reporting
Capture process events, operator actions, test results and machine data so engineering and quality teams can prove what happened and when.
Controls/data integration
Connect PLCs, controllers, instruments, SNMP devices, DataMiner outputs and databases with robust read/write logic, retries, validation and diagnostic visibility.
Specialist product development
Take a technical concept through architecture, implementation, target-system testing, controlled packaging, diagnostics and maintainable release.
Supportability reviews
Assess existing systems, document architecture, identify single points of failure and produce recovery/handover packs for maintenance and monitoring teams.
Approach
Clear scope, engineered implementation, usable handover.
Understand the system
Review the process, existing code, PLC/HMI structure, database schema, communications and operational constraints before proposing a change.
Define the change
Agree the required behaviour, data flow, failure handling, operator visibility, logging and acceptance criteria.
Implement carefully
Build the modification with practical diagnostics, backwards compatibility where required, and a clear route for testing and rollback.
Document and support
Provide documentation, commissioning notes and support information so the system can be maintained after the project is complete.
Need practical engineering support?
Send over the problem, the system context and the outcome you need.
Contact
Talk to JMTEQ about automation, software, product development or AI-assisted engineering work.
For the fastest response, include the site/system involved, the current issue, product idea or required change, any relevant screenshots/logs, and the timescale you are working to.
JMTEQ
Industrial automation, DataMiner/SNMP monitoring, control software, AI-augmented product development and engineering data systems.
julian.meacham@jmteq.comSearch summary
Industrial automation, engineering software and AI-augmented product development.
JMTEQ provides UK-based industrial automation and engineering software support for manufacturing, test and specialist technical environments. Services include Siemens S7, TIA Portal, WinCC, PLC/HMI support, VB.NET production applications, SQL Server traceability systems, DataMiner/SNMP monitoring, AI-assisted software development, legacy code analysis, product prototyping, OpenWrt development, embedded network products, release engineering, deployment automation, reporting, dashboards, commissioning support and engineering handover documentation.
Industrial automation
PLC and HMI support
Siemens S7
TIA Portal
WinCC
DataMiner support
SNMP monitoring
SNMP aggregation
VB.NET production software
SQL Server traceability
Production reporting
AI-augmented engineering
AI-assisted software development
AI product development
Legacy code analysis
Product prototyping
OpenWrt development
Embedded network products
Release engineering
Deployment automation
Modbus integration
Eurotherm integration
Load-cell logging
Engineering documentation
FAQ
Common questions about JMTEQ services.
What does JMTEQ do?
JMTEQ provides UK-based industrial automation, control software, production database, DataMiner/SNMP monitoring and engineering support services for manufacturing and test environments.
Does JMTEQ support DataMiner systems?
Yes. JMTEQ supports DataMiner-facing monitoring applications, SNMP aggregation, OID polling, driver output files, element and view structures, Visio mapping, redundancy behaviour and operational handover documentation.
Does JMTEQ work with Siemens PLC systems?
Yes. JMTEQ supports Siemens S7, TIA Portal, WinCC and related plant control systems, including fault finding, modifications, commissioning support and documentation.
Can JMTEQ build production reporting systems?
Yes. JMTEQ builds VB.NET and SQL Server applications for test data, traceability, operator workflows, reporting, dashboards, CSV exports and controlled data logging.
Can JMTEQ help with Modbus, Eurotherm and instrumentation data?
Yes. JMTEQ supports plant data acquisition and diagnostics for Modbus devices, Eurotherm controllers, analogue tags, load cells, process measurements, setpoints and live-value logging.
Does JMTEQ provide engineering documentation and handover packs?
Yes. JMTEQ produces support manuals, commissioning checklists, recovery notes, rollback plans, network inventories and engineering handover documents for long-term maintainability.
How does JMTEQ use AI in software development?
JMTEQ uses AI to assist with system analysis, source-code investigation, implementation, fault finding, testing and technical documentation. The work is directed and verified using engineering knowledge, operational evidence and controlled release processes.
Can JMTEQ help develop a specialist technical product?
Yes. JMTEQ can help take a specialist software, industrial or embedded-network product from an initial requirement through architecture, implementation, testing, diagnostics, packaging, deployment and support documentation.