Visual quality inspection
Detect scratches, dents and casting defects on the line, and grade severity.
Machine learning & computer vision
Models that see, predict and decide. In production, not just in a notebook.
We build computer vision and machine learning systems that run inside your operations, from defect detection on the line to safety monitoring and demand forecasting, deployed at the edge or in the cloud and kept accurate over time.
01Inspection & safety02Forecasting03Edge or cloud
Computer vision
Turn every camera into a sensor.
Safety & hazard detection
Fire, smoke, PPE and restricted-zone monitoring from live camera feeds.
Counting & tracking
Count and classify vehicles, people or products, each counted once with tracking.
OCR & traceability
Read serial numbers, labels and plates to link every part to its record.
Predictive machine learning
Models that learn from your history to forecast, score and flag what needs attention. Need dashboards and reporting rather than models? See our data analytics & BI services.
Demand forecasting
Forecast demand by product, region and season to plan production and stock.
Predictive maintenance
Predict failures from sensor and machine data before they stop the line.
Churn & risk scoring
Identify customers, accounts or suppliers likely to churn or default.
Anomaly detection
Spot unusual transactions, readings or behaviour in real time.
How we build
From raw footage to a model that keeps improving.
Good models start with good labels. Our in-house data annotation team works alongside our ML engineers.
01
Collect
Images or video from your cameras, sensors or sample datasets.
02
Annotate
AI-assisted labelling with multi-pass human review.
03
Train
Custom detection, segmentation or classification models, tuned for your data.
04
Validate
Precision, recall, IoU and confidence thresholds tuned with you.
05
Deploy & improve
APIs or live streams on-prem, at the edge or in the cloud, with retraining on new edge cases.
Proof
All AI case studiesComputer vision running in production.
Three deployments across manufacturing quality, public safety and media analytics.
Automated visual inspection on a camshaft production line
A line-side camera reads each camshaft's engraved serial number and flags surface defects in real time, logging every part for traceability.
2-stagedetection and OCR pipeline
4classes: serial ROI, scratch, dent, cast mark
Real-time fire and hazard detection from video
Live and recorded camera feeds are analyzed in real time, with fire and flame events flagged and streamed to a browser dashboard.
Real-timefire and flame detection on live video
0.7confidence threshold tuned for few false alarms
Highway traffic counting for outdoor advertising analytics
A camera on a highway billboard counts and classifies passing vehicles, producing impressions data advertisers use for media planning.
4vehicle classes counted
4×frame-skip for high-FPS streams
MLOps
Accurate on day one. Still accurate on day 300.
Edge or cloud deployment
Run on an industrial PC or edge GPU on the shop floor, or scale in the cloud.
Real-time performance
Frame-skipping, model sizing and GPU acceleration tuned for your throughput.
Monitoring
Accuracy, confidence and drift tracked in production, not just at launch.
Active learning loop
Low-confidence cases go back to annotation, so models keep up with new conditions.
Portable models
Standard formats and open tooling, so you're never locked in to us.
Outcome reporting
Counts, classifications, defect logs and accuracy metrics delivered as data.
Technology
Open, portable, production-proven tooling.
Computer vision
YOLOv8 / v11Detectron2Mask R-CNNOpenCVSegment Anything
Machine learning
PythonPyTorchscikit-learnXGBoostPandas
Serving
FastAPIFlaskREST APIsLive video streaming
Infrastructure
DockerCUDA GPUsEdge devicesCloud GPUs
Engagement
Pilot first. Scale when the numbers prove it.
01
Discovery & pilot
Sample data review, labelling guidelines and a quick proof-of-concept model. Typically 2–4 weeks.
02
Dataset & model build
Full annotation, training, threshold tuning and validation. Typically 4–8 weeks.
03
Integration & deployment
APIs or streams on your infrastructure, connected to your dashboards. Typically 2–4 weeks.
04
Monitor & improve
Ongoing monitoring, re-annotation of edge cases and model refreshes.
It depends on the problem. Vision projects often start with a few hundred to a few thousand well-labelled images, using pretrained models as a base. The pilot tells us quickly whether more data is needed.
Yes. We deploy on edge GPUs and industrial PCs on-premise, with CPU fallback where needed, so video never has to leave your site.
With precision, recall and IoU on a held-out test set, plus confidence thresholds agreed with you. We report results as data, not opinions.
Low-confidence and new cases are fed back into annotation and retraining through an active-learning loop, so the model keeps up.
Yes. Our annotation team handles bounding boxes, polygons, segmentation and keypoints with multi-pass QA. See our data annotation services.
Data science covers analytics, BI and insight. This page covers production ML and computer-vision systems that run continuously inside your operations.
Have cameras or data you're not using?
Let's prove it with a pilot on your own data.
Share a sample of your images, video or historical data. We'll scope a pilot with clear accuracy targets before you commit to a full build.
At Techno Genesis, we’re passionate about crafting digital solutions with the latest technology, ensuring our clients stay ahead and embrace the future with confidence and innovation.
#Together, we shape a brighter tomorrow.
Let’s create a legacy of greatness, together.
Let's Talk
sales@technogenesis.in
Services
Address
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