Industrial Automation & OT Cybersecurity Consulting

A lot of industrial technology spend goes to the second-best problem.

A site installs analytics on a unit whose control loops are in manual. A vendor is selected before the requirement is written. A pilot runs for a year with no baseline to compare against. None of this is incompetence — it is what happens when the technology conversation starts before the operational one.

Our work starts at the other end: the loop that hunts, the record that cannot be found, the decision that cannot be justified. Sometimes the answer is software. Often it is an instrument, a valve, or a procedure.

The ISA-95 automation pyramid: field devices, control, supervision, operations and enterprise levels

ISA-95 · where a problem sits determines what can fix it

Services

What we are asked to do.

Industrial automation

Automation architecture, control system modernisation and migration planning, instrumentation reviews, and reliability improvement on existing assets.

Industrial AI

Where machine learning earns its place against the data a site actually holds — and where a control fix, an instrument or a procedure would do more for less.

Digitalisation

Plant data, operations records and reporting: what to digitise first, what it connects to, and what it must produce to be worth the disruption.

OT cybersecurity

OT security assessments, IEC 62443 guidance, network architecture and segmentation reviews, risk assessment, and a roadmap ordered by risk rather than by product.

Operational performance & KPIs

KPI design that engineers and finance both accept, performance reporting, operational analytics, and energy and maintenance analysis.

Investment and business case support

CAPEX versus OPEX analysis, payback and risk assessment, technical due diligence, and business cases written so that an engineering argument survives a finance review.

How an engagement runs

Six moves, and permission to stop at any of them.

This is not a methodology we sell. It is the order the work tends to happen in, and every stage is a point where the honest answer may be "no further".

The operational problem

Stated in the plant's own terms — this loop, this unit, this record, this decision — not as a technology category.

Systems and data

What the DCS, historian, CMMS and document store already hold, at what resolution and quality. This determines what is possible and usually shortens the list.

The business case

What a result is worth, how it will be measured, and against which baseline. Agreed before anything is specified.

The recommendation

The appropriate technology and supplier for the problem — sometimes ours, often not, occasionally none. Written plainly enough to be argued with.

Pilot

On a real loop, unit or shift, scoped so that a wrong answer is cheap and a right one is visible.

Measure

Compare to the baseline agreed at step three. Keep what worked, stop what did not, and decide the next move on evidence.

Evidence

The baseline is the deliverable nobody asks for.

Most industrial digital projects cannot prove what they achieved, because nobody recorded the starting condition. By the time the question is asked, the plant has changed, the feedstock has changed, and the argument is unwinnable.

Fixing that costs very little and has to happen first.

Maintenance, production and energy KPIs
Reliability and asset-health measures
Safety and compliance indicators
Adoption measures — whether the tool is actually used
Reporting that engineers, operations and finance all recognise

Who engages us

The people who carry the consequence.

Plant & operations managers

Throughput, variability and the cost of running below design.

Engineering & automation managers

Control system life cycle, modernisation and where to spend a limited capital budget.

OT and IT managers

Segmentation, remote access and security work that operations will actually accept.

CTOs and technical directors

Whether an industrial AI or digital programme is grounded in the data that exists.

EPCs and system integrators

Specification, commissioning support and defensible automation scope at handover.

Investors

Technical due diligence on industrial technology and the operations behind the numbers.

Consulting FAQ

Questions about how we work.

What does industrial AI consulting actually involve?

Deciding where machine learning or AI methods create operational value in a plant, and where they do not. In practice: reviewing operations and the data that actually exists, identifying candidate use cases, testing their feasibility against that data, and agreeing one first step that can be measured. It frequently concludes that a control or instrumentation fix is the better investment.

Are you independent, or do you resell a particular vendor?

Independent. SynapseAI does not take reseller margin on third-party automation hardware, so technology selection advice is not shaped by what would be most profitable to sell you.

Do you work on OT cybersecurity and IEC 62443?

Yes. OT cybersecurity assessments, IEC 62443 guidance, network architecture and segmentation reviews, risk assessments and prioritised security roadmaps for operational technology environments.

How do you show whether an automation or digital investment paid off?

By agreeing the measurement before the work starts. KPI frameworks covering maintenance, production, energy, reliability, asset health, safety and adoption are defined first, so a baseline exists before any change is made and the result is defensible afterwards.

Do you work with plants directly, or only through vendors?

Both. Plant owners and operators engage SynapseAI for consulting and software. Separately, automation and industrial software companies engage SynapseAI for commercial work in EMEA and Asian markets — see commercialisation.

Engineering advice, not a slide deck.