AI for Manufacturing Companies

The sensor data requirement depends on the equipment type and the failure modes you want to predict. For rotating equipment (motors, pumps, compressors, conveyors), vibration data from accelerometers and temperature data are the highest-signal inputs. For electrical equipment, current draw and voltage readings capture degradation patterns before failure. For hydraulic systems, pressure sensor data and fluid temperature are most predictive. In practice, most manufacturing operations already collect more sensor data than they use. The common problem is not missing sensors but missing labels: you need to know when failures occurred historically so the model can learn what the sensor pattern looked like in the hours and days before each failure. We assess your sensor data and maintenance history records in discovery. If your maintenance records don't contain failure timestamps, we work with your maintenance team to reconstruct them from work orders and downtime logs. Minimum data requirement is typically 12-18 months of sensor history with at least 20-30 historical failure events for the equipment type being modelled.

Computer vision quality control trains a model on images of good and defective products from your production line. The model learns to identify the specific defect types your process produces: surface scratches, dimensional variance, colour deviation, missing components, weld quality, label placement, or whatever the relevant quality characteristic is for your product. At deployment, a camera positioned at the inspection point captures images of every unit in real time. The model scores each image and flags defects with the defect type and location marked on the image. Defective units are rejected or flagged for human review depending on the confidence score and the defect severity. The key inputs we need to start are a sample of defect images across each defect type you want to detect, and a sample of good-unit images. Minimum sample size is typically 500-1000 images per defect class. If you don't have labelled defect images, we can run a data collection phase before model training. The detection accuracy achievable depends heavily on defect visibility, image quality, and consistency of lighting on the line.

Yield optimisation AI analyses the relationship between process parameters and output quality or yield. The model is trained on your historical production records: what were the machine settings, material inputs, environmental conditions, and operator, and what was the resulting yield or quality outcome? The model identifies which parameter combinations produce the best yield and which combinations produce waste or rework. Output is a recommended process parameter set for each product and material combination, updated as new production data comes in. This works best when you have: consistent measurement of process parameters during production (temperature, speed, pressure, time, etc.), consistent measurement of output quality or yield, and enough historical records to detect the signal. For most discrete and process manufacturers, the data already exists in MES or SCADA systems. The challenge is extracting and labelling it. We do this extraction as part of the build.

Predictive maintenance tells you when a piece of equipment is likely to fail: the model scores current sensor readings against failure patterns and surfaces a risk alert when the signature matches. The output is a probability and an estimated time to failure. Prescriptive maintenance goes one step further and tells you what to do: not just that the motor is likely to fail in the next 7 days, but which specific component is showing the failure signature, which maintenance action addresses it, and when to schedule the intervention to minimise production disruption. Prescriptive maintenance requires more mature data infrastructure: you need not just sensor data and failure history, but also maintenance action records that link specific interventions to outcomes. Most manufacturers we work with start with predictive maintenance and add the prescriptive layer once the predictive model is validated and the maintenance team trusts the alerts. We scope the right starting point based on your current data maturity.

Most manufacturing AI engagements run 10-14 weeks from kick-off to production deployment. A predictive maintenance model for a single equipment type with 18 months of sensor history typically takes 10-12 weeks. A computer vision quality control system for a new product line with data collection included runs closer to 14-16 weeks. The timeline is driven by data readiness, not the AI itself. If your sensor data is clean and labelled, the model build is fast. If we need to reconstruct failure history from work orders or run a defect image collection phase, that adds 2-4 weeks before model training starts. We give you a fixed timeline at the end of discovery week, before any build begins.

We have shipped AI systems for discrete manufacturers, process manufacturers, and industrial operations across the US, UK, and Australia. Relevant sectors include automotive components, food and beverage, packaging, electronics assembly, pharmaceuticals, chemicals, and industrial equipment. The common thread is not the sector — it is having sensor data, quality records, or production history that is currently underused. If you have 12 months of SCADA, MES, or CMMS data and a specific cost or quality problem to solve, we can scope an AI model for it. If you are unsure whether your data is sufficient, we assess it in the first week at no cost.