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 that reduce unplanned downtime, cut quality escapes, and improve yield from your existing process data. Predictive maintenance models trained on your equipment sensor data predict failure before it causes a production stop. Computer vision defect detection inspects every unit in real time, catching defects manual inspection misses at volume. Yield optimisation models identify which process parameter combinations maximise output for each product-material combination. Engagements are scoped at a fixed price after a discovery phase that maps your sensor data, quality records, and production history to the specific AI capability being built.
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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.
Capabilities
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 (RMS, peak, kurtosis from accelerometer data sampled at 1-10kHz), temperature trends, current draw harmonic distortion, pressure fluctuations, and oil quality indicators against labelled historical failure events from your CMMS work order history. Feature engineering converts raw time-series sensor data into predictive features: rolling statistics (mean, standard deviation, skewness over 1h/8h/24h windows), frequency-domain features from Fast Fourier Transform (identifying fault frequencies for bearing defects at 1x-4x running speed harmonics), and cross-sensor correlation features that capture the interaction between temperature rise and vibration increase.
The model is trained using gradient boosting (XGBoost or LightGBM) for tabular sensor features with binary classification (failure within N days) and calibrated probability outputs -- so a 0.82 failure probability means roughly 82% of assets with that signature fail within the prediction window, not an arbitrary score. Outputs surface in your maintenance team's existing workflow: a prioritised risk list in your CMMS (Maximo, SAP PM, eMaint) with the contributing sensor signals highlighted, a recommended action (lubricate, inspect, replace), and estimated time to failure. Models are retrained quarterly on your accumulated maintenance data so accuracy improves as your specific failure patterns accumulate.
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 using convolutional neural networks (ResNet-50/EfficientNet-B4 as backbone with transfer learning from ImageNet, fine-tuned on your labelled defect images). Object detection models (YOLOv8 or Detectron2) locate and classify multiple defect types within a single image frame with bounding box output. Anomaly detection approaches (PatchCore, Student-Teacher networks) work when labelled defect examples are scarce -- the model learns what "good" looks like and flags deviations from the normal appearance distribution.
At deployment, every unit is inspected in real time at camera frame rate: inference runs on edge GPU hardware (NVIDIA Jetson Orin or dedicated vision system) to meet cycle time requirements without cloud round-trip latency. Defects are flagged with type and location marked on the image, confidence score, and the disposition recommendation (reject, rework, or escalate for manual review). Reject thresholds are set against your quality specifications -- not the model's default confidence threshold. False positive rate is measured during validation against your quality cost: a 2% false positive rate on a $50 rework cost per false rejection is a different tolerance than the same rate on a $500 unit. Inspection images for each flagged unit are logged with the model output, disposition outcome, and any manual override decision for ongoing model quality monitoring.
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.
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.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope AI for Manufacturing Companies in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
