Machine Learning Solutions

Computer vision

Detection, OCR and quality inspection trained on your images, tested in your lighting, deployed on your line or in your cloud.

Scope a vision pilot

Vision systems for real conditions

Vision models fail in the gap between the demo dataset and your factory floor: glare, dust, camera angles, the defect that shows up twice a month. We build detection, OCR and quality-inspection systems around that gap, training on your images and evaluating on the hard cases your team collects, not on benchmark photos.

The engagement starts with your existing footage, or a capture plan if there is none. We label a first dataset, train a baseline and show you precision and recall per defect class within weeks. From there the loop is tight: review the misses together, relabel, retrain, until the numbers clear the bar you set.

Deployment fits your constraints: edge devices next to the camera when latency or connectivity demands it, cloud when it does not. The system ships with a review UI for borderline cases, so operators correct the model and every correction becomes training data for the next iteration. Retraining on that growing set is a scheduled pipeline, not a new project.

What you gain

Measured per class

Precision and recall reported per defect or document type, so you know exactly where the model is strong and where humans stay in the loop.

Works in your conditions

Trained and tested on your cameras, lighting and materials. The eval set is built from your worst cases, not clean samples.

Edge or cloud

ONNX-exported models run on devices at the line or as GPU services in your cloud, chosen by latency and cost.

OCR beyond print

Stamps, handwriting, skewed scans and multi-language documents, with confidence scores routing the doubtful ones to people.

A dataset that compounds

Labeling standards and tooling mean every production correction grows your dataset and your accuracy over time.

Machine Learning

From first images to the line

Collect and label

Audit existing images or plan the capture, then label a first dataset with class definitions your operators agree on.

Baseline fast

A first model within weeks, scored per class, so we know early whether data or modeling is the bottleneck.

Iterate on the misses

Review errors with your domain experts, expand the dataset where it hurts and retrain until targets are met.

Deploy where it runs

Optimize and package for edge or cloud, wire in monitoring and the human review loop, then hand over.

What you get

Labeled dataset with documented labeling standards

Trained detection or OCR model, exported for your target hardware

Per-class accuracy report on a held-out test set

Deployed inference service with review UI

Retraining pipeline and operations runbook

Questions, answered

How many images do we need to start?

With modern pretrained backbones, a few hundred per class is often enough for a first baseline, and augmentation stretches small sets further. The feasibility study answers it precisely: we train on what you have and report where more data would pay off.

Rare defects appear a few times a month. Can a model learn them?

Yes, with the right approach: augmentation, synthetic examples and anomaly detection that flags anything unusual rather than only known classes. Recall on rare cases is reported separately, so the risk stays visible.

Does this run without internet on the shop floor?

Yes. Models export to ONNX and run on industrial PCs or edge devices next to the camera, with updates shipped on your schedule and no cloud dependency at inference time.