Industry · Semiconductor industry
Industrial vision for defect detection
Automatic anomaly classification in production and technical documentation answered in seconds, without depending on one expert.
- Client
- Semiconductor industry
- Sector
- Industry
- Product
- Inspectra IQ
In numbers
The initial problem
Microscopic defects, inspections that do not scale, fragmented technical information, and slow answers to specialized questions in high-precision environments.
The previous process
Quality inspection depended on manual review, and technical knowledge was scattered across manuals, PDFs, and presentations that only a few experts knew well.
The solution implemented
Computer vision for anomaly classification with advanced visual analysis models and a detection pipeline, plus a RAG platform on Google Cloud to query technical documentation, PDFs, PPTX files, and manuals in natural language.
How we did it
Visual detection pipeline
We built the image capture and analysis workflow to classify anomalies in parts where the defect is microscopic.
Classification models
We trained and tuned the models with the plant's quality criteria, so classification reflects what the team considers a defect.
Queryable technical knowledge
We indexed manuals, PDFs, and presentations in a RAG platform on Google Cloud to answer specialized questions in natural language.
Integrated systems
- Computer vision
- Google Cloud
- RAG
- Technical documentation (PDF/PPTX)
The result
Better early detection, fewer unidentified errors, quality traceability, and fast access to knowledge without depending on a handful of experts.
What changed in the operation
- Defects are detected before moving further down the line.
- Inspection leaves a record per part and is auditable.
- Technical knowledge is no longer only in the experts' heads.
- Specialized questions are answered in seconds.
Frequently asked questions about this case
Does it replace the quality inspector?
No. It automates systematic review and flags what's doubtful so the specialist spends time on the cases that truly require it.
Does it need many images to work?
We work with the plant's own material and tune the model to its quality criteria. The pipeline is designed to improve as cases accumulate.
Does documentation search work on old manuals?
Yes. The RAG platform indexes existing PDFs, presentations, and manuals, with no need to redo the documentation.
Keep exploring
Other work by the team, across different sectors.
Intelligent traceability for the public sector
Field evidence capture with edge computing and computer vision, plus oversight dashboards with full traceability.- OCR of plates and vehicle type
- Geo-referenced evidence
- Oversight dashboards
AgricultureData analytics for agriculture: field decisions with same-day data
Production and geospatial data turned into season dashboards, with early anomalies and comparison between fields.- Early anomalies
- Benchmarking between fields
- Yield forecasting
AccountingAccounting automation with AI and tax filings
A workflow that receives, reads, validates, and files the firm's source documents with no typing, plus missing-item alerts per client.- Reading with OCR and AI
- Validation against ARCA and banks
- Alerts for missing documentation
Tell us which process you'd like to improve
We'll look at how it works today and send back a concrete diagnostic: what to automate, how, and what result to expect. No cost, no commitment.