Condition monitoring and manufacturing analytics, built for UK SMEs.
Condition monitoring, predictive maintenance and analytics on the data you already have, plus engineers who come to site when unplanned downtime needs a cause, not another alert. Works with the systems you already run.
Why condition monitoring and analytics stall at UK SME manufacturers.
Enterprise BI platforms were built for organisations with dedicated data teams and seven-figure budgets. Generic AI tools don't speak manufacturing. The result for most UK SMEs is the same: operational data piling up across systems, and no clear way to act on it.
Data is scattered
Sensors in the historian. Work orders in the CMMS. Quality in spreadsheets. Costs in the ERP. Nothing joins up — so nobody has the full picture.
No data team to hire
The median UK senior data scientist salary is £90,000 (ITJobsWatch, six months to September 2026). For most SME engineering teams that is not in the headcount budget. So nothing happens.
Enterprise pricing is a non-starter
Industrial AI platforms quote six-figure annual contracts before the first conversation. Finance won't sign that off — and shouldn't.
What predictive maintenance with AWI looks like.
Closed-loop reliability: a single place where your engineering team can ask questions of your operational data in plain English — and get accurate, sourced answers with charts and tables in seconds. Then engineers who come to your plant and act on what those answers show.
Ask in plain English
“Which line had the most unplanned downtime last quarter?” “Show me bearing temperatures on pump 4 vs. last year.” “What's our top three scrap reasons by shift?”
Connect to what you already have
SQL databases, historian connectors, static uploads, CMMS exports and more. Vendor-neutral.
Proactive monitoring
Set thresholds and anomaly rules in natural language. Get notified the moment something drifts, and the system explains why it flagged — then an engineer follows it to the cause and puts it right.
Sourced, auditable answers
Every answer cites the underlying records and shows its working. Engineers can verify before acting.
Specified and installed by engineers
Condition-monitoring specification, vibration analysis, vendor-neutral sensor selection, installation and data integration — the groundwork done on site by AWI Labs.
On your floor, not just in the cloud
When the data points at a real fault, our engineers are on your floor running the root-cause analysis and engineering the cause out — then verifying from the data that the fix held.
Built for UK manufacturing reality.
Founded and run by engineers, for engineers
An engineering partnership, not a software subscription. We read the data, diagnose the fault, come to site and engineer it out — then prove it held.
UK storage, customer-controlled AI region
Customer data is stored in AWS London (eu-west-2) by default, with a dedicated UK-only deployment available for stricter requirements. Because you bring your own AI provider (Anthropic, Microsoft, AWS Bedrock), you also choose the region your AI inference runs in — under your contract, not ours.
Priced in GBP
Transparent monthly tiers in pounds. Annual options for predictable budgeting.
Works with British manufacturing context
Built knowing how UK SMEs actually run — lean teams, mixed-age plant, multiple sites, the EFQM/ISO standards engineers actually use.
R&D Tax Credit eligible
Implementation work on AI-driven analytics often qualifies for UK R&D Tax Relief (subject to HMRC criteria and your accountant's assessment).
Further reading on condition monitoring and predictive maintenance.
Written for UK engineering and maintenance teams deciding what to monitor, what it costs, and what to do with the answer.
Predictive maintenance for SMEs: the complete guide
Where a smaller manufacturer should start, what it costs to run, and which assets are worth instrumenting first.
Uptime monitoring vs condition monitoring: cost or cause
Uptime tells you the machine stopped. Condition monitoring tells you why it was heading that way.
Vibration analysis explained: from sensor to decision
What a vibration signature actually shows, and how a reading becomes a maintenance decision rather than a chart.
The P-F curve: maintenance's most important graph
The window between the first detectable symptom and functional failure — and what widens it.
The true cost of unplanned downtime
The costs that never reach the downtime log: overtime, expedited parts, scrapped batches, missed delivery dates.
Building a business case for predictive maintenance
How to put numbers a finance director will accept behind a condition monitoring programme.
AI anomaly detection and equipment failure
How anomaly detection flags machine behaviour that has drifted, and where an engineer still has to do the judging.
Closed-loop reliability: detection is only half the job
Everyone else stops at the alert. Connect, Monitor, Detect, Diagnose, Act, Verify — the loop AWI closes.
Frequently asked questions.
What is manufacturing analytics software?
Manufacturing analytics software collects, organises and analyses operational data from your shop floor — sensors, SCADA, CMMS, ERP, spreadsheets — and tells you what changed. Most of it stops there, at an alert on a dashboard. AWI is built the other way round: AWI Analytics explains the change in plain English with the underlying records attached, and AWI Labs engineers then establish the cause on site, carry out or specify the work, and re-measure to show it held.
What is AWI Analytics?
AWI Analytics is the software half of AWI, an engineering reliability partner for manufacturing SMEs. It connects to the operational data sources you already run and lets engineers ask questions of that data in plain English, returning sourced answers with charts and tables. When an answer points at a real fault, AWI Labs engineers do the physical work — the root-cause analysis, the correction, and the re-measurement that shows it held.
Does AWI Analytics work with the systems we already use?
It is designed to be vendor-neutral and to work alongside the systems you already run, rather than requiring you to rip and replace. The exact set of connectors and integration approach depends on your specific stack — we cover this in a discovery call.
How long does implementation typically take?
AWI Analytics is designed for SME-scale engagements rather than multi-quarter enterprise rollouts. A realistic scope is to have a first useful output on real data within weeks, with full operational handover inside a 90-day implementation window.
Can condition monitoring and analytics qualify for Made Smarter funding?
Made Smarter Adoption supports SME manufacturers with premises in England, across nine English regions, with match-funded grants towards hardware and software plus funded advice. It is England-only — in Scotland the equivalent route is the Scottish Manufacturing Advisory Service via Scottish Enterprise. Criteria and grant rates change by region and cycle, so check yours at madesmarter.uk. AWI is not an approved or accredited supplier under either scheme, so eligibility is judged on your project rather than on the software you pick.
Can AI adoption qualify for UK R&D Tax Relief?
Implementation work that involves a genuine technical advance or resolves scientific or technological uncertainty can qualify for UK R&D Tax Relief, subject to HMRC's definition and your accountant's assessment. Off-the-shelf adoption typically doesn't qualify; bespoke integration work sometimes does. Take specialist advice before claiming.
How do I find out if AWI Analytics is a fit?
The best starting point is a 30-minute discovery call. We walk through your operational data, your priority use cases, and what realistic next steps would look like.
See it on your data.
Book a 30-minute discovery call. We'll show you AWI Analytics running on a sample of your operational data — and how we record what happened after the alert.