01
Understand
We start with your business problem, not a technology — what it costs, who it affects and what would change if it were solved.
- A free 30-minute conversation
- A measurable target
- No technical sales pitch
infusedML · Independent AI & machine-learning specialist
Practical AI for
engineering &
manufacturing.
Turn your machine, production and business data into practical AI solutions that predict problems, automate processes and improve decision-making.
Work directly with an independent machine-learning specialist — from first idea to working solution.
NORTH YORKSHIRE · WORKING ACROSS THE UKPRACTICAL · LOW RISK · PERSONAL
01 / WHAT COULD AI DO FOR YOUR BUSINESS?
Start with the problem.
Not the technology.
Four kinds of problem come up again and again in engineering and manufacturing. Each one starts with data you probably already have.
- 01Predict equipment problemsIdentify patterns in machine and sensor data that give earlier warning of potential failures.
- 02Automate inspectionUse computer vision to identify defects, classify components and monitor processes.
- 03Unlock your business dataMake technical documents, reports and other company information easier to search and use.
- 04Optimise processesFind patterns in operational data that improve productivity, quality and decision-making.
01 / PREDICT EQUIPMENT PROBLEMS
Could this machine have warned you before it failed?
“We can only find the problems after the machine has failed.”
- BUSINESS PROBLEM
- A critical pump fails without warning a few times a year.
- DATA
- Vibration, temperature and current already logged, plus maintenance records.
- AI
- A model learns the drift that comes before a failure.
- RESULT
- A service window suggested twelve days ahead, for a planner to review.
POTENTIAL VALUERepairs planned into scheduled downtime instead of lost production.
Take the branch: 2 more examplesFollow the branch to the examples01 A / CONDITION MONITORING BY SOUND
Could you hear a bearing failing weeks before it seizes?
- DATA
- A low-cost microphone or accelerometer beside each machine, recording a few seconds every few minutes.
- RESULT
- An alert when the score crosses a threshold, with the frequency band that changed and a suggested inspection date.
01 B / STOPPAGES, JAMS & LEAKS
Could you know about a jam before anyone walks past?
- DATA
- Audio from a microphone on the line, plus the stop and start signals from the PLC where they are available.
- RESULT
- An alert for each jam as it happens, and a log of every leak and short stop — without recording conversations.
02 / AUTOMATE INSPECTION
Could a camera find this defect automatically?
“Manual inspection is expensive, inconsistent or too slow.”
- BUSINESS PROBLEM
- Parts are checked by eye, and defects slip through late in a shift.
- DATA
- A few hundred labelled images of good and defective parts.
- AI
- A vision model checks every part against what good looks like.
- RESULT
- Doubtful parts diverted for review, in under 20 ms each.
POTENTIAL VALUEConsistent inspection at line speed.
Take the branch: 4 more examplesFollow the branch to the examples02 A / END-OF-LINE SOUND TESTS
Could every gearbox be listened to before it ships?
- DATA
- Recordings of each unit’s run-up test from a microphone on the stand, linked to serial numbers and past returns.
- RESULT
- A pass or fail with the reason, logged against the serial number. Failed units are held for rework.
02 B / ASSEMBLY & KIT CHECKS
Could one photo confirm a kit is complete?
- DATA
- Photos of the tray, rack or pallet from a fixed camera or a phone.
- RESULT
- Counts synced to the ERP, with gaps and missing parts highlighted on the photo.
02 C / READING CERTS & LABELS
Could goods-in read the certificate for you?
- DATA
- Scanned or photographed certificates, delivery notes and labels, and the purchase orders they should match.
- RESULT
- Fields filled in automatically, with anything uncertain sent to a person to check.
02 D / SAFETY MONITORING
Could your existing cameras warn of a hazard as it happens?
- DATA
- Video from cameras you already have, with the exclusion zones and PPE rules for each area.
- RESULT
- An alert to the area supervisor within a second, and a log of events for safety reviews.
03 / UNLOCK YOUR BUSINESS DATA
Could your engineers ask your data a question?
“Our people spend too much time finding and interpreting information.”
- BUSINESS PROBLEM
- Working out why OEE dropped means pulling reports from several systems.
- DATA
- Downtime logs, changeover records and scrap data, read-only.
- AI
- A language model queries the data and explains what it finds.
- RESULT
- An answer in seconds, with the working shown.
POTENTIAL VALUEDecisions made on the figures rather than a hunch.
Take the branch: 4 more examplesFollow the branch to the examples03 A / ASKING TECHNICAL DOCUMENTS
Could your engineers ask the manual a question?
- DATA
- Your manuals, SOPs, drawing notes and past fault reports, left in the folders they already live in.
- RESULT
- An answer in seconds, with links to the exact pages it came from.
03 B / THEMES IN FAULT LOGS & NCRS
What are a year of fault logs actually telling you?
- DATA
- Exports from your maintenance system, NCR log or complaints database.
- RESULT
- A ranked list of recurring problems, with real entries behind each one.
03 C / ORDERS & RFQS INTO YOUR ERP
Could emailed orders go straight into the ERP?
- DATA
- The order inbox, plus your customer and part master data.
- RESULT
- A draft sales order ready for approval, with anything missing or mismatched flagged.
03 D / CUSTOMER SPEC REVIEW
Could a long customer spec be checked against what you can do?
- DATA
- The customer’s documents, and a checklist of your capabilities and standard terms.
- RESULT
- Flagged clauses with page references for an engineer to review.
04 / OPTIMISE PROCESSES
Could your order history plan next month’s production?
“We have data, but we’re not getting enough value from it.”
- BUSINESS PROBLEM
- Call-offs are hard to predict, so materials are over-ordered or runs are rushed.
- DATA
- Two years of order history and call-off schedules.
- AI
- A forecasting model predicts demand with a realistic range.
- RESULT
- A 12-week forecast for materials and shift planning.
POTENTIAL VALUELess cash tied up in stock, and fewer rushed runs.
Take the branch: 4 more examplesFollow the branch to the examples04 A / BETTER PROCESS SETTINGS
Could your existing line run at better settings?
- DATA
- Months of process settings from the PLC, matched to the yield, scrap and energy records for each run.
- RESULT
- Suggested settings with the predicted yield and energy use, approved by an engineer before a trial run.
04 B / ROOT-CAUSE ANALYSIS
Why do good batches sometimes go bad?
- DATA
- Batch records, material lots, lab results and environmental readings brought together for 18 months of production.
- RESULT
- A short list of findings for engineers to test, such as an interaction between humidity and one resin lot.
04 C / SOFT SENSORS
Could you measure quality continuously, not every four hours?
- DATA
- Readings from the sensors already on the dryer, matched to past lab results.
- RESULT
- A live estimate that tracks the lab samples closely, with an alert when it drifts.
04 D / ENERGY ANOMALIES
Could your energy data tell you what was left running?
- DATA
- Hourly meter and sub-meter readings, with the site’s shift pattern.
- RESULT
- Unusual periods flagged within hours, with the likely equipment identified.
AN HONEST ANSWER
Not every problem needs AI.
Sometimes the right answer is better software — a tablet app instead of a clipboard, a link between the line and the ERP, or a dashboard people actually use. If that’s the case, I’ll say so, and I can build that too.
Software & integration examples02 / FROM PROBLEM TO WORKING SOLUTION
Prove the value.
Then build it.
You never commit to a large project on a hunch. Each step shows whether the next is worth taking — and “no” is a useful answer too.
02
Investigate
We examine your existing data and systems to see whether they can support a solution.
- Data quality & feasibility
- Systems you already have
- Risks & potential value
03
Prove
We build a focused proof of concept on your real data, without touching the production system.
- One specific question
- Measured against today
- Go, Modify or Stop
04
Build
If it works, we turn it into a practical solution that fits your systems and the people using it.
- Edge, on-site or cloud
- Links to PLC, MES & ERP
- People in the loop
05
Improve
We monitor, refine and develop the system as products, machines and your needs change.
- Accuracy tracked over time
- Documentation & handover
- Ongoing support if wanted
Every project starts with a free 30-minute conversation, then a short AI Opportunity Review.
How the AI Opportunity Review works03 / WHY AN INDEPENDENT SPECIALIST
Work directly with
the person building it.
I’m the developer behind infusedML, an independent machine-learning business based in North Yorkshire. I have a First-Class master’s degree in Computer Science and Artificial Intelligence from Loughborough University.
I spent two years as a Machine Learning Engineer on contract at Cummins, and have worked on machine-learning projects in process engineering, using real sensor and production data. I help engineering and manufacturing businesses work out where AI can actually make a difference — and I’d rather tell you an idea won’t work than build something useless.
Connect on LinkedIn- ACADEMIC FOUNDATION
- Awarded a FirstMaster’s degree in Computer Science and Artificial Intelligence, Loughborough University
- INDUSTRY EXPERIENCE
- Cummins · Process engineeringTwo years as a Machine Learning Engineer (contract) at Cummins, working on real engineering data
- HOW I WORK
- Problem → Data → Proof → SolutionBased in North Yorkshire, working with businesses across the UK
Direct access
You work directly with the person developing the solution — no hand-offs.
Specialist knowledge
Strong machine-learning capability without the cost of a large consultancy team.
Low overhead
No layers of account managers, consultants and project managers to pay for.
Speed
Small projects can be tested and developed quickly, so you see evidence early.
Practicality
The aim is a working solution, not an expensive AI strategy document.
Truly independent
No cloud platform, software package or technology to sell you.
04 / WHO I WORK WITH
Engineering and
manufacturing businesses.
Typically UK firms of 20–250 people with machines and data already in place, a recurring problem worth solving — and no in-house AI team. These are typical starting points.
Automotive suppliers
- Surface, weld and assembly inspection
- End-of-line sound and vibration tests
- Test rig and torque data analysis
Machinery & equipment makers
- Condition monitoring built into your product
- Manuals and service history for field engineers
- Simulation surrogates for faster design
Specialist & precision engineering
- Quote and cycle-time estimates from past jobs
- Tool wear from spindle load and sound
- Reading material certs at goods-in
Process manufacturing
- Soft sensors for lab-tested qualities
- Batch quality and yield prediction
- Energy use on dryers, kilns and boilers
Industrial services, logistics & fleet
- Predictive servicing from telematics
- Fault-code triage for service teams
- Demand and capacity forecasting
Advanced manufacturing
- Defect detection on complex parts
- Root cause of scrap and rework
- Process windows for new products
FREE GUIDE
Is your business
ready for AI?
Ten questions to ask before starting an AI project — from what the problem costs to what happens if the pilot succeeds.
05 / THE FIRST STEP
Could AI solve one of
your expensive problems?
Book a free 30-minute AI Opportunity Conversation. Tell me about a process, machine or business problem you’re trying to solve — no technical sales pitch, and an honest answer if AI isn’t the right tool.
Independent · North Yorkshire · working across the UK