Are you finding out a machine is going to fail at the same time it does, rather than days before?
Are quality escapes reaching your customers because visual inspection is inconsistent at volume?
AI for Manufacturing Companies
Unplanned downtime, quality escapes that make it to the customer, and production plans built on last year's demand patterns: these are the operational problems that erode manufacturing margins. AI applied to your sensor data, vision systems, and production records changes what is preventable versus what is a surprise.
We build AI systems for manufacturers: predictive maintenance from equipment sensor data, computer vision quality control on production lines, demand forecasting for production planning, yield optimisation models, energy consumption forecasting, and supply chain risk prediction. Each system is scoped against your data and a specific cost or quality target.
Equipment failure predicted from sensor data before it causes unplanned downtime
Defects detected on the production line by computer vision before they reach the customer
Production plans built on demand forecasts trained on your order history, not spreadsheet averages
Yield and energy optimisation models that find margin improvements in your existing process data
RaftLabs builds AI systems for manufacturing companies including predictive maintenance models trained on equipment sensor data to predict failure before it happens, computer vision defect detection on production lines, demand forecasting models for production planning, AI-powered yield optimisation using process parameter analysis, energy consumption forecasting, and supply chain risk prediction. Engagements are scoped at a fixed price after a discovery phase that maps your sensor data, process data, and quality records to the specific AI capability being built.
Manufacturing margin lives in the data your systems already collect
Most manufacturers have more process data than they act on. Sensor readings that never get analysed, quality records that inform reports but not decisions, production history that sits in a database without informing the next plan. AI is the tool that converts that existing data into decisions: when to schedule maintenance, which units to reject, how to set parameters to maximise yield.
What we build
Predictive maintenance
ML models trained on your equipment sensor data and maintenance history to predict failure before it causes unplanned downtime. We analyse vibration, temperature, current draw, pressure, and other sensor streams against historical failure events. The model outputs a failure probability and estimated time window for each equipment unit. High-risk equipment surfaces to your maintenance team with the contributing sensor signals, giving enough lead time to schedule the repair during planned downtime rather than emergency response.
Computer vision quality control
Defect detection models trained on images from your production line: surface defects, dimensional variance, missing components, label placement errors, weld quality, or colour deviation. Models are trained on your specific product and defect types, not generic benchmarks. At deployment, every unit is inspected in real time. Defects are flagged with type and location marked on the image. Reject thresholds are set against your quality specifications. Consistent at volume where manual visual inspection is not.
Demand forecasting for production planning
Forecasting models trained on your order history, customer demand signals, seasonal patterns, and promotional calendars to produce production-ready demand forecasts at the SKU and horizon level your planning team needs. More accurate than spreadsheet averages, and updated automatically as new order data comes in. Gives your production planners a demand picture they can trust when building the production schedule.
Yield optimisation models
Models that analyse the relationship between your process parameters and yield or quality outcomes. Trained on your historical MES or SCADA data: machine settings, material inputs, environmental conditions, and resulting yield. Identifies which parameter combinations maximise yield for each product-material combination. Output is recommended process parameters and the expected yield impact of each setting change. Runs on a schedule and updates recommendations as new production data comes in.
Energy consumption forecasting
Forecasting models that predict energy demand by production line, shift, and production mix. Trained on your historical energy consumption data alongside production schedules and equipment states. Gives your operations team a forward-looking energy demand picture for procurement and load management. Identifies which production configurations drive peak energy costs and models alternatives. Directly applicable to energy cost reduction and demand response programmes.
Supply chain risk prediction
Models that score supplier and material risk based on historical delivery performance, financial signals, geographic concentration, and lead time volatility. Identifies high-risk supplier relationships before they cause a production disruption. Incorporates external signals: commodity price movements, geopolitical risk indicators, and weather data for supply chains with geographic concentration. Surfaces risk concentration so your procurement team can take action before a disruption, not after.
What is unplanned downtime or quality escapes costing you per month?
If you have sensor data, production records, or quality images, there is probably an AI model that reduces that number. Tell us the problem and we will tell you what is possible.
Related services
Predictive Analytics -- forecasting and risk models across industries
AI Development -- end-to-end custom AI system builds
Computer Vision Development -- vision AI for detection and inspection
Generative AI in Manufacturing -- generative AI applications in manufacturing
IoT Development -- sensor connectivity and data infrastructure for AI systems
AI for Manufacturing by area
Manufacturing Software -- ERP, MES, quality control, production planning
Supply Chain Automation -- automated supply chain and procurement
IoT Development -- sensor integration and real-time data collection
Frequently asked questions
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.