AI Quality Control Services for Manufacturing
Manual inspection is the bottleneck your quality system is built around
Visual inspection by human operators is consistent at the start of a shift and inconsistent by hour eight. Some defect types, including surface micro-cracks and colour variation, are at the edge of human visual discrimination even under ideal conditions. Statistical sampling catches defects in aggregate but doesn't prevent defective units from shipping until the batch rejection threshold is crossed, by which point the root cause has been running for hours. AI visual inspection checks every unit at line speed, with consistent accuracy regardless of shift length, and feeds real-time defect data into your SPC system while the process can still be corrected.
Computer vision defect detection trained on your specific defect types and product variants
Inline inspection at production line speed, checking every unit without slowing the line
Defect classification by type and severity with confidence scores and image capture for every rejection
Real-time defect rate data feeding your SPC system for process control and root cause analysis
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Manual visual inspection missing defects that reach customers because inspectors fatigue over a 10-hour shift?
Sampling-based QC passing defective batches that inspection missed because you cannot check every unit?
Plain answer
RaftLabs builds AI quality control software for manufacturing: computer vision defect detection trained on your own defect classes, severity grading beyond pass or fail, inline integration with your production line and SPC system, and rejection workflows. The first inspection point is validated at full line speed before expansion to more lines and defect types.
What to remember
- The training dataset must be captured under actual production lighting and camera conditions, with enough defect and pass examples to teach the model the boundary.
- Every rejection is captured with defect class, severity grade, confidence score, and a cropped image, building the archive used for both root-cause analysis and model retraining.
- False positive rate is tracked alongside detection accuracy and calibrated against your quality cost model, since excessive false positives drive rework cost that offsets defect-escape savings.
- Inspection throughput matches or exceeds line throughput, validated at full production speed during commissioning.
- Inspection results are written to your QMS and ERP in real time, with full production traceability.
Proof
- shipping production software across healthcare, fintech, logistics, and retail
- Since 2015
- RaftLabs delivery record
- average client rating across delivered projects
- 4.9/5
- Clutch, verified reviews
- scope and cost agreed in writing before any development starts
- Fixed price
- Every RaftLabs engagement
What poor quality actually costs a line
The cost of poor quality is decided in the minutes between a defect starting and someone catching it, while scrap, rework, warranty, and recall are already on the clock. Manual inspection is consistent at the start of a shift and drifts by hour eight, and sampling catches defects in aggregate only: a defective unit keeps shipping until the batch crosses its rejection threshold, hours after the root cause began.
AI visual inspection checks every unit at line speed. Accuracy holds regardless of shift length, and real-time defect data reaches your SPC system while the process can still be corrected.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
- 01
Buy
Choose a maintained product when it handles the core workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
- 02
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
- 03
Build
Own the system when the workflow shapes margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
- 04
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
Capabilities
What we build
Computer vision defect detection
Detection models matched to your inspection task, trained on your own defect taxonomy with confidence-scored thresholds tuned to your quality cost model.
Built with: YOLOv8 · EfficientNet
Inline inspection integration
GigE Vision camera and lighting design specified for your defect types, with TensorRT inference triggering the reject gate within the inter-unit gap.
Built with: Basler · NVIDIA Jetson
Defect classification and severity grading
Defect-type and severity-grade classification beyond pass/fail, with every rejection archived with a cropped image for root-cause and retraining use.
SPC data integration
Real-time defect data feeding P-charts, Cpk/Ppk metrics, and Western Electric rule-based drift detection, replacing the end-of-shift tally sheet.
Built with: InfinityQS · Minitab
Rejection workflow automation
Automatic reject/rework/scrap routing with reason codes populated into your ERP's quality notifications, plus batch-hold workflows at threshold breach.
Quality analytics dashboard
Floor-level real-time status and management-level cross-line trend and Pareto analysis, correlating defect rate with production parameters.
How we work
From scope to a live first inspection point
- Step 0101
Inspection and defect-taxonomy scoping
We map your inspection line speed, current defect types, and defect escape cost. You leave the scoping phase with a written scope document and a fixed-price quote.
- Step 0202
Data collection and hardware design
Camera, lighting, and inference hardware are specified for your defect types. A structured image-collection phase then gathers training data under production conditions.
- Step 0303
Model training and integration
Detection models are trained and validated on your defect classes. SPC and QMS integration is built in parallel.
- Step 0404
Commissioning at full line speed
Validated at production throughput with your quality team before go-live.
The tradeoff you scope up front: missed defects versus false rejects
Every inspection model sits on one dial. Turn the confidence threshold up and you catch fewer real defects. Those false negatives are the units that reach a customer. Turn it down and you scrap or rework good parts. Those false positives burn margin on the line. The two failures cost different amounts, and the right setting depends on your numbers, not a vendor default.
We calibrate that threshold against your quality cost model. A safety-critical automotive part weights a missed defect far heavier than a false reject, so the dial moves toward over-flagging and a human reviews the borderline calls. A high-volume consumer good on thin margins cannot absorb the rework, so the balance shifts the other way. Either way we track false positive rate next to detection rate, because a model with strong detection that wrongly rejects good parts can cost more than the defects it catches.
We train models on your defect classes, not a generic library. Each class needs enough labelled examples to tell a real flaw from normal process variation, and the rare defects are the hard part: some flaws appear so rarely that collecting examples takes sustained effort. For those we combine collected fails, targeted augmentation, and anomaly detection that flags anything outside the learned-good distribution. Inference runs at the edge on the line, so a network outage never stops inspection, and a human-review queue handles low-confidence calls and feeds every correction back into the next training round.
Why us
Why manufacturers choose RaftLabs
- 01
The engineers who scope also build
The engineers who assess your inspection line also build the solution. No bait-and-switch, no handoff to a different crew after the contract is signed.
- 02
Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts.
- 03
Shipping production software since 2015
Real engagements across SaaS, fintech, healthcare, and logistics, not every one published as a named case study. Our AI work spans computer-vision OCR, real-time edge inference, and image models in production.
- 04
Designed to your line speed, not the other way around
Throughput impact during inspection is treated as a design failure, not an acceptable tradeoff.
- 05
False positive rate tracked, not just detection rate
Threshold tuning balances catching defects against unnecessary rework cost.
Proof
The closest system we have shipped
- 01
AI OCR Software Scales Gas Station Operations
Not a production line, but the same shape of problem: computer vision running at the edge, offline-first, reading vendor documents in real time. - 02
AI remote patient monitoring for chronic care
AI integration in the remote patient monitoring app lifts efficiency by automating data analysis and providing personalized insights through wearable health monitoring devices such as CGM and BPM. This advancement has cut clinical decision-making time by 20% (client-reported), enabling virtual care management, particularly in chronic care scenarios.- reduction in clinical decision-making time
- 20%
- compliance with HIPAA regulations
- 100%
- patients adopted in just 12 weeks
- 150+
- 03
Mobile POS app case study: card, tap, QR, and link payments for UAE merchants
A mobile POS and merchant-acquiring platform for a UAE FinTech operator: four ways to take a payment on a phone, two processors behind one ledger, and KYC built for a regulated operator.- from first commit to the first App Store release
- 13 weeks
- installs shown on the public Google Play listing
- 10K+
- tagged releases, from v0.0.1 in 2022 to v1.17 in 2026
- 67
What it costs
Every project starts at $10000.
The first phase covers one inspection point, with defect detection, classification, and the rejection workflow wired in.
Starting investment
Starts at $10000
Start with the line where human inspection misses the most. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Proof
What clients say about working with RaftLabs
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Work with us
Have a quality control AI project?
Tell us your inspection line speed, current defect types, and what your defect escape rate costs you. We will design the AI inspection system and give you a fixed cost.
- 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.
- All conversations are NDA-protected.
Common questions
Images captured under actual production lighting and camera conditions, annotated using CVAT or Label Studio to a labelling standard your quality engineers define before annotation starts. Both defect examples and pass examples are needed so the model learns the boundary between a real flaw and normal process variation.
No. We design the inspection system around your line speed from the start. Camera selection, frame rate, resolution, and inference hardware are chosen so inspection throughput matches or exceeds line throughput, validated at full production speed during commissioning.
The anchor question of the buyer checklist: what false-accept and false-reject rates will you prove on our own live parts, verified by site acceptance testing? Accuracy quoted on a vendor dataset does not count. Most systems demo well; the checklist separates production-grade from the rest. A polished demo tells you very little.
False accepts reach the customer. False rejects quietly drain throughput and trust on the floor. Accurate in one direction only is not accurate. When the AI is wrong, who catches it, how often is it checked, and who pays for the rework? Name the decision the system supports, who makes it today, and the consequence of an incorrect recommendation, before proposing a model.
Ask whether the image set covers different lighting, suppliers, and product variants, and whether inference runs at the edge or needs a cloud round trip. A network dependency on a production line is a risk: edge inference keeps inspection running when the network does not. Rare defects are rare by definition, so ask how the system handles flaw types with few examples.
Yes. Inspection results are written to your QMS and ERP in real time. We integrate with SAP QM via OData or BAPI, Oracle Quality Cloud via REST, ETQ Reliance, MasterControl, and custom QMS platforms via database or file-based integration, with defect records carrying full production traceability.
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