Real automation, software, data or investigation work defines the engineering problem.
JMTEQ technology
ALITA
Augmented Logic & Intelligence for Technical Automation
Engineering intelligence across the entire automation stack.
ALITA is JMTEQ's engineering-intelligence system, developed through practical industrial automation, manufacturing software and complex production-system work.
It combines advanced AI reasoning with JMTEQ's automation engineering knowledge, retained system context, structured analysis methodologies, deterministic engineering techniques and evidence-based validation.
It doesn't just analyse files. It analyses systems.
Engineering intelligence
Not general-purpose AI.
General-purpose artificial intelligence is designed to operate across an enormous range of subjects. ALITA has a different purpose: support real engineering investigation, development and validation across industrial systems.
ALITA provides a specialist engineering-intelligence layer around advanced AI reasoning. It is not a renamed chatbot, autonomous engineer or independently trained foundation model. The engineering responsibility remains with JMTEQ.
The ALITA methodology combines
- Advanced AI reasoning
- Automation engineering knowledge
- Structured engineering methodologies
- Deterministic analysis and comparison
- Cross-system correlation
- Evidence-based validation
- Human engineering review
Developed through engineering work
ALITA did not begin as a finished software product.
It has evolved through repeated use on practical JMTEQ engineering work: industrial software development, modification of large codebases, VB.NET and .NET applications, Node.js server services, PLC analysis, legacy-system investigation, communications work, databases, root-cause analysis and technical reporting.
AI reasoning helps inspect larger codebases, compare evidence and structure options.
The useful investigation path becomes a clearer engineering methodology.
Checks, baselines, acceptance criteria and review rules become part of the method.
Repeatable work is progressively converted into specialist applications and pipelines.
The result becomes a structured engineering-intelligence system rather than a one-off prompt.
Cumulative engineering intelligence
ALITA learns how JMTEQ engineers.
This does not mean uncontrolled self-training or autonomous release of changes. It means JMTEQ progressively builds engineering intelligence around the underlying AI by retaining and refining the rules, context, baselines and validation methods that make engineering analysis useful.
A problem investigated once can improve the method used on the next system. That cumulative value is a major difference between ALITA and simply asking an AI tool for an answer.
Engineering rules
Constraints, standards and behaviours that must be respected.
Analysis methodologies
Repeatable procedures for investigating engineering systems.
System context
Architecture, terminology, interfaces, dependencies and design decisions.
Validation rules
Checks required before analysis, code or conclusions are accepted.
Known-good baselines
Reference software and configuration versions used for controlled modifications.
Engineering lessons
Previous problems become checks and method improvements for later work.
The complete automation stack
It doesn't just analyse files. It analyses systems.
Modern manufacturing systems rarely consist of a PLC alone. ALITA is designed to reason across the boundaries between machines, networks, servers, software, databases and manufacturing processes.
Many industrial problems cannot be understood properly by analysing only one layer. A machine behaviour may depend on PLC logic, configured network paths, server code, database transactions and the production decision that eventually feeds back to the line.
Capabilities
System-level engineering understanding.
ALITA correlates engineering evidence from across the automation environment so JMTEQ engineers can investigate significance, dependencies and impact rather than isolated differences.
PLC
PLC & machine control
PLC architecture, OB/FB/FC logic, compiled executable logic, data blocks, sequencing, hardware configuration, distributed I/O, PROFINET, PROFIBUS and online/offline comparison.
NET
Industrial communications
PROFINET, PROFIBUS, Industrial Ethernet, TCP/IP, ISO-on-TCP, OPC architectures, PLC-to-PLC communication and machine-to-machine interfaces.
APP
Manufacturing software
.NET, VB.NET, C#, Windows services, APIs, data acquisition systems, operator applications, communication services and legacy manufacturing software.
DB
Databases & manufacturing data
Production history, traceability, product genealogy, quality information, machine data, configuration, transactions, events, interfaces and data dependencies.
Practical PLC analysis example
More than listing project differences.
A PLC project comparison can involve executable logic, compiled code, data structures, hardware, network configuration and communication calls. The important engineering task is not merely reporting that two items differ.
ALITA is designed to help correlate configuration → PLC code → communication path → operational consequence, so differences can be considered in terms of behaviour, risk and likely plant impact.
Example evidence ALITA can help structure
OB / FB / FC logic
Compiled MC7 code
DB structure and data
Hardware configuration
PROFIBUS
PROFINET
Industrial Ethernet
NetPro connections
IP addresses
Local / remote TSAPs
Connection IDs
Active / passive setup
Communications processors
SDB / System Data
AG_SEND / AG_RECV
BSEND / BRCV
PUT / GET
TCON / TSEND / TRCV
Cross-system reasoning
Following a function through the factory.
Each layer provides additional engineering evidence. ALITA correlates those sources to build an engineering model of how the complete function operates.
Manufacturing requirement
Operator process
PLC sequence
Industrial communication
Server / application logic
Database record
Production or quality decision
Machine interlock
A production requirement may pass through each of these layers before it produces a machine response. ALITA can help correlate evidence across the chain rather than treating the PLC, application and database as unrelated components.
Legacy-system intelligence
Understand before replacing.
Established manufacturing software often evolves over many years, contains embedded operational knowledge, has incomplete documentation and may have lost its original developers. It cannot always be economically or safely rewritten simply because it is old.
The objective is not automatically to replace a legacy system. Often the better engineering decision is to understand it sufficiently well to modify it safely.
Evidence used to reconstruct behaviour
- Source code, compiled applications and deployment structure
- Databases, schemas, stored data and transaction patterns
- Configuration, PLC logic and industrial communications
- Logs, alarms, operator screenshots and diagnostic output
- Documentation, commissioning notes and observed plant behaviour
Evidence before conclusion
Engineering requires evidence.
Industrial automation requires a different standard from general AI-generated analysis. ALITA uses an evidence-led engineering methodology to distinguish what can be demonstrated from what can only be inferred.
What is different, why does it matter, what evidence proves it, and what should an engineer do about it?
Traceable engineering analysis
From problem to documented conclusion.
ALITA is being developed to produce traceable engineering analysis rather than merely plausible answers.
Engineering problem
Acquire evidence
Structured AI analysis
Cross-system correlation
Engineering validation
Engineering judgement
Documented conclusion
How ALITA works
From engineering evidence to engineering output.
Engineering evidence
PLC projects, source code, databases, networks, specifications, documentation, production data and system configuration.
ALITA engineering intelligence
AI reasoning, automation knowledge, engineering rules, structured analysis, system modelling and cross-system correlation.
Engineering validation
Evidence classification, dependency analysis, cross-checking, confidence assessment and engineering consistency.
JMTEQ engineering expertise
Practical review, engineering judgement, system knowledge and manufacturing experience.
Engineering output
Analysis, design, documentation, recommendations and implementation.
Human engineering remains in the loop
Supervised analysis. Controlled release.
ALITA does not independently release changes into production systems. AI increases analytical and implementation capacity, ALITA provides engineering context and methodology, and JMTEQ provides engineering judgement.
Define the required behaviour, constraints, system boundary and acceptance criteria.
Inspect source, PLC projects, data, configuration, logs and operational evidence.
Design and implement controlled changes against known baselines.
Test behaviour, correlate evidence and challenge assumptions.
Apply JMTEQ engineering judgement before accepting the result.
Package, document and hand over work with rollback protection where appropriate.
Engineering development
From understanding to implementation.
ALITA is not restricted to analysing existing systems. Once an engineering model has been developed, the same intelligence can support the design and implementation of new functionality.
It can assist JMTEQ engineers with functional design specifications, system architecture, PLC software design, manufacturing software development, database design, communication protocol development, interface specifications, modification impact analysis, test specifications and commissioning documentation.
Human engineering in the loop
ALITA increases the amount of information that an engineer can practically analyse, correlate and understand. JMTEQ engineers provide the practical context, experience and judgement required to validate and interpret those findings.
AI provides scale.
Engineering provides judgement.
ALITA combines the two.
Tool-building capability
ALITA also helps build engineering tools.
ALITA is increasingly used not only to perform engineering analysis, but also to help JMTEQ develop specialist engineering applications. When an engineering task repeats, the method can become a workflow, then a tool, then an automated or semi-automated analysis process.
This means the capability compounds rather than resetting for every project.
Manual engineering task
AI-assisted task
Structured methodology
Repeatable workflow
Dedicated engineering tool
Automated or semi-automated analysis
Commercial value
More of the system can be understood before an engineering decision is made.
ALITA is intended to improve the depth, traceability and scale of engineering investigation. It does not promise that every project is automatically faster or cheaper; it helps JMTEQ analyse more evidence before recommending what should be changed.
Unfamiliar systems
Faster investigation of established or poorly documented systems.
Deeper analysis
More evidence reviewed before engineering changes are made.
Cross-boundary understanding
PLC, software, network and database behaviour considered together.
Controlled development
Faster software work without losing sight of baselines and operational risk.
Evidence-backed conclusions
Clearer technical reports, recommendations and engineering rationale.
Legacy knowledge
Preservation and extraction of engineering knowledge embedded in older systems.
Report designation
Engineering analysis powered by ALITA
This designation indicates that underlying engineering information has been processed using the JMTEQ ALITA engineering-intelligence framework, with resulting evidence used by JMTEQ engineers to support conclusions, recommendations and implementation decisions.
Developed by JMTEQ
ALITA
Augmented Logic & Intelligence for Technical Automation
Engineering intelligence across the entire automation stack.From PLC logic to manufacturing servers. From industrial networks to production databases. From legacy-system investigation to new engineering development.
It doesn't just analyse files. It analyses systems.
AI provides scale.
Engineering provides judgement.
ALITA combines the two.
Engineering analysis powered by ALITA.
Discuss ALITA with JMTEQ