Manufacturing · Defect detection
Every defect that ships costs three times what it costs to catch on the line
Banao builds AI defect detection that catches failures at the point they form — in process parameters, assembly torques, dimensional readings, and line-camera signals — before they become escapes.
We combine statistical process control with machine learning to surface deviations that rules-based SPC misses. The result is a real-time signal on your floor, not a weekly quality report.
The first call is free · 45 minutes · no obligation
What we build
What a Banao defect detection system covers
Defect detection is not one tool. It is the right detection method matched to where in the process failures actually start.
Process-parameter anomaly detection
Machine learning trained on your historical process runs flags deviations in temperature, pressure, torque, and cycle-time before they produce a bad part. You act on the signal, not the scrap.
Multi-source defect correlation
When the same defect type traces back to a raw-material batch, a specific operator shift, or a tool wear cycle, the model surfaces that link. Rules-based SPC cannot find cross-variable patterns at this depth.
In-process detection at the earliest stage
Catching a defect at station three costs a fraction of catching it at final QC. We identify the earliest reliable detection point in your process and instrument it first.
Defect classification with root-cause flags
The system classifies each failure mode and tags it with the most likely upstream cause — giving quality engineers a starting point, not just a count.
Rework loop quantification
Rework is often invisible in quality cost models. We surface rework frequency by root defect type and the process conditions that drove each event — so reduction targets are grounded in data.
Live quality cost dashboard
Defect rates, escape rates, rework hours, and scrap costs tracked by line, shift, and batch — updated in real time so quality heads see today's number, not last quarter's.
Receipts
Where this pattern is already working
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Process anomaly detection reduced escapes at final QC
End-of-line inspection was catching defects too late to act on their root cause. Banao instrumented upstream process parameters and trained an anomaly model that flags deviations before they produce a bad part.
Cross-variable correlation identified a recurring shift-change defect
A defect pattern that appeared intermittently and resisted manual investigation turned out to be a cross-variable interaction between ambient temperature and tool pressure. The model found it in historical run data; the fix was a process adjustment, not a hardware replacement.
Dogfooding
We have built quality systems for our own operation first
Banao runs AI across a ~300-person engineering company — InterviewGod screens our own technical hires, Vikaas runs our own demand pipeline, and we monitor system quality metrics the same way we ask your quality team to. We do not describe AI in production from the outside.
A defect detection system needs to survive shift changes, data gaps, and a floor team that will not coddle it. We know what that takes because we have built and depended on it ourselves.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When AI defect detection is not the right answer yet
We will tell you before the build, not after:
- Sparse defect history: if your defect rate is very low or your data history is short, the model has little to learn from. We will say so in week one.
- No upstream data: if the only data you have is a pass/fail at final QC, there is nothing to detect upstream. The first project may be instrumentation, not modelling.
- Defect definitions that shift: if what counts as a reject changes with each customer order, a trained classifier deteriorates within weeks. Stability in the specification is a prerequisite.
How we start
How we start — find the signal before building the system
We do not quote a defect detection platform off a spec sheet. We look at your actual process data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your process historian and QC records, identify your highest-frequency defect types and their earliest upstream correlates, and hand back a detection feasibility report with ROI estimates — yours to keep. If you proceed, the Sprint fee is credited against the build.
- 02
Build
Model training on your defect history, integration with your process historian, SCADA, or MES, and a live anomaly signal wired to your quality workflow. Data pipeline and alert logic are part of the deliverable.
- 03
Production and continuous improvement
Floor deployment with quality-team onboarding and a live dashboard. Model retraining cadence is set from day one; accuracy is tracked and reported every quarter.
FAQ
Frequently asked questions
What data do you need to build a defect detection model?
Process historian or MES records covering a range of your defect types — typically six to twelve months of run data with at least a few hundred defect events across the types you care about. The Discovery Sprint establishes whether your data is sufficient and what augmentation or collection is needed.
How is this different from our existing SPC system?
Rules-based SPC monitors one variable at a time against fixed control limits. Machine learning finds cross-variable patterns, non-linear drift, and interaction effects that SPC control charts miss. We often deploy ML alongside SPC rather than replacing it — the models catch what the charts do not.
Does it work with our existing MES or process historian?
Yes. We connect to your existing data infrastructure — OSIsoft PI, Ignition, SAP MII, custom historians, flat-file exports — and build the detection layer on top. We do not require a new platform.
How quickly can we see a signal on the floor?
The Discovery Sprint hands back a feasibility report in two weeks. A production model running on live process data is typically eight to fourteen weeks from Sprint completion, depending on data readiness and integration complexity.
What happens when the model flags something the floor team disagrees with?
The floor team logs their finding, and that outcome feeds back into the model. Disagreements are data, not noise. Over time the model converges on the signals your best engineers actually trust.
Get started
Show us your highest-frequency defect
Bring your process records and your worst repeat quality problem. In 45 minutes we will tell you whether the signal is in the data — and what it would take to detect it in real time.
Book a Discovery Sprint