Digital studio in Valais — web applications, AI and computer vision for Swiss SMEs.
Digital studio in Valais — web applications, AI and computer vision for Swiss SMEs.
Industrial computer vision & automated quality control — wine, watchmaking, food, pharma.
Custom machine vision and industrial computer vision, designed and deployed from Sierre, Valais. Automated quality control, defect detection, OCR and edge-deployed AI for industry 4.0 factories across Switzerland and Europe.
European industry is dense, demanding and increasingly automated. Swiss pharmaceutical and chemical clusters, German and Italian mechanical manufacturers, food and beverage producers, logistics operators, viticulture — every one of them has visual inspection tasks that used to rely on human eyes. Machine vision, also called industrial computer vision, lets you inspect, count, measure, read and sort automatically, at line speed, with results you can audit.
TechSolve designs, installs and maintains custom computer vision systems for SMEs and industrial groups across Switzerland and Europe. From the industrial camera to the deep-learning model, via lighting, IP65 enclosures and supervisory dashboards, we deliver turnkey solutions with a single goal: improving quality and throughput without making life harder for your operators.
100% in-line inspection of parts: dimensions, assembly, presence/absence, colour, surface finish. Replacing statistical sampling with full traceable inspection for ISO 9001 and GxP certifications.
Automatic reading of labels, lot numbers, DataMatrix codes, expiry dates, invoices and delivery notes, with direct integration into your ERP or traceability system.
Scratches, chips, porosities, stains, misassemblies. Deep-learning models trained on your actual defect library, with continuous learning as new cases come in from the line.
Anonymised people counting (retail, museums, tourist offices), object tracking on conveyors, vehicle monitoring for logistics, occupancy measurement.
Consent-based access control, licence-plate recognition for car parks, visual badges. Strictly aligned with GDPR and Swiss FADP, with a documented data protection impact assessment.
High-speed decoding of 1D and 2D codes (EAN, Code128, DataMatrix, QR) for logistics, pharma and food traceability — up to several hundred codes per second.
We assemble hardware, optics, deep-learning models and supervision as an end-to-end system. Lighting, often the real key to project success, is designed per application; the model is chosen based on throughput and the precision required.
For a Swiss wine producer, we built an optical sorting system on a vibrating table: the camera detects damaged, rotten or unripe berries and triggers a compressed-air jet to eject them. Result: harvest quality equivalent to manual sorting, at three times the throughput, without fatigue or operator-to-operator variability.
In European pharma clusters, we integrate lot-number reading, seal verification and particle detection in liquid vials. Every installation is documented for GMP and GxP audits and keeps inference history for the full legal retention period.
High-throughput barcode reading in receiving zones, volumetric parcel measurement for billable-volume calculation, content verification when handling returns. Systems integrate with your WMS — SAP, Odoo, Microsoft Dynamics — via REST APIs.
Anonymised people counting at store, museum and ski resort entrances. Data feeds opening-hours, staffing and investment decisions, with zero identification and no image retention — strictly privacy-preserving by design.
Industrial vision projects succeed or fail before the first line of code. That is why we structure every engagement in four short phases.
A half-day on your production line to study the scene: working distance, conveyor speed, existing lighting, integration constraints. We leave with dozens of reference photos to test options against.
We train a first model on a representative dataset and demonstrate it on site with test hardware. The POC validates technical feasibility and gives you a precise budget for full deployment. When off-the-shelf models fall short, we train custom deep learning on your own annotated images.
Final hardware selection, enclosure design, mechanical integration, wiring, software tuning, factory and site acceptance tests (FAT/SAT). Operator training and full documentation handover.
Vision models need monitoring: drifting conditions, new defect types, evolving products. We offer maintenance contracts covering monitoring, scheduled retraining and software updates. Computer vision work often pairs with our web application development and AI and machine learning — see our full services overview.
On a well-scoped quality-control task — controlled lighting, normalised parts — we routinely hit 98% to 99.9% accuracy with less than 0.5% false positives. For harder tasks, such as subtle visual defects or products with significant variability, we combine classical methods (OpenCV) with deep learning (YOLO, EfficientNet) to exceed 95% while keeping inference latency below 100 ms.
For a pilot: an industrial camera (Basler, FLIR, IDS), a dedicated PC or NVIDIA Jetson Nano/Orin, and controlled LED lighting. For tough environments — chemical plants, outdoor installations, food processing — we specify IP65/IP67 enclosures and thermal cameras when relevant. The hardware stays modest compared with the defects avoided and inspection time freed up, and is typically paid back in under a year.
Critically important. Lighting accounts for 60% to 80% of project success in machine vision. Poor lighting can defeat even the best model. We always design the optical scene upfront — diffuse, backlight, dark-field or structured — based on the defect you need to detect. A half-day on-site lighting audit is included in every industrial engagement.
Edge (Jetson, Raspberry Pi, industrial PC on-site) is best for real-time production lines, sites without reliable internet and sensitive data. Cloud (AWS, Azure Europe) suits heavy post-processing, multi-site aggregation and after-the-fact analysis. We often combine both: edge inference for real-time decisions, cloud reporting for monitoring and retraining. Most European factories default to edge for latency and reliability.
It is heavily regulated under GDPR and the EU AI Act, and equally strict under Swiss FADP. We only implement compliant use cases: anonymised counting (no identification), depersonalised flow analysis, or consent-based recognition in private settings (secure access, badge readers). Every project includes a data protection impact assessment (DPIA) and proper documentation for your DPO.
Real-time (30 to 120 FPS) is for production lines, automated sorting and security: typical latency of 20 to 100 ms, edge deployment mandatory. Batch is for after-the-fact analysis (archive photos, lot-level quality control, monthly quality reports) — thousands of images processed overnight on a cloud server or local NAS. The choice has a significant impact on both architecture and budget.
A focused POC — one defect, one line, one part family — ships in 3 to 6 weeks. It includes the optical study, collection of a representative dataset, training of a first model and an on-site demonstration. You leave with a detection rate measured on your own products (typically 95 to 99%), an estimate of defects avoided and inspection hours freed up — enough to decide on full industrial deployment without a heavy upfront hardware commitment.
Across European manufacturing (chemicals, food and beverage, viticulture, mechanical, watchmaking), ROI typically lands between 8 and 24 months. Gains come from reduced outbound defects (recall cost, brand impact), lower manual inspection load (one operator can supervise 3 to 10 vision-monitored lines instead of one) and stronger traceability for ISO and GxP certifications.
Historically, 'machine vision' refers to industrial quality-control systems (calibrated cameras, controlled lighting, fast cycles), while 'computer vision' covers the broader set of visual algorithms (deep learning, object recognition, OCR). Today the two converge: modern industrial systems use deep learning (YOLO, PyTorch) for flexibility. At TechSolve we combine both approaches based on production constraints.
Book a free scoping call. We will walk through your line, your defects and your constraints, then propose a short proof-of-concept with fixed pricing before any hardware is ordered.
TechSolve Ribeiro — Sierre, Valais, Switzerland — +41 78 925 66 30 — info@techsolve.ch