AI, Computer Vision & Machine Learning
AI that ships, not slideware: vision systems that inspect at line speed, models that run on the edge inside your product, analytics that predict failures — and, where it earns its keep, LLMs wired into real workflows. We bring the engineering discipline of hardware people to machine learning.
Computer vision
Detection & classification
Objects, defects, states — deep models trained on your data, measured against your acceptance criteria.
Segmentation & measurement
Pixel-accurate boundaries for gauging, robotics and quality decisions.
Classical vision (OpenCV)
When geometry and lighting do it better — deterministic, explainable, fast.
Optics & illumination design
Cameras, lenses and lighting engineered first, so the algorithm's job is possible (our embryo-detection POC reached 100% this way).
OCR & code reading
Characters, barcodes, DPM marks in harsh industrial conditions.
Machine learning engineering
Deep learning
TensorFlow, Keras and PyTorch — trained, validated and versioned properly.
Anomaly detection
Finding the defect you can't enumerate, in images and sensor streams.
Predictive analytics
Failure prediction and process drift from vibration, current, temperature, logs.
Time-series & sensor fusion
Filtering, feature engineering and models for multi-sensor machines.
Data pipelines & labeling
Collection rigs, dataset curation and labeling workflows that don't rot.
Deployment
Edge AI
Jetson, NPU-equipped MCUs/SoCs, quantization and pruning — models inside the product, offline.
Line-speed inference
Pipelines engineered to cycle time: from camera trigger to reject signal, deterministic.
MLOps
Versioned models, monitored accuracy, controlled retraining — production discipline for ML.
Cloud & hybrid
Heavy training in the cloud, light inference at the edge, telemetry in between.
Language models & agents
LLM integration
GPT-class models wired into products and workflows — with guardrails and evaluation.
RAG over your data
Assistants grounded in your documentation, manuals and tickets.
NLP applications
Classification, extraction and summarization for operational text.
Honest feasibility advice
Sometimes the answer is a lookup table. We'll tell you.
Feasibility & data
Define the decision, audit the data, kill the project early if physics says no.
Prototype
A measurable POC against agreed acceptance criteria — fast.
Engineering
Robust pipeline, edge deployment, integration with the machine/line.
Production & monitoring
Accuracy monitored over time, retraining under control.
How much data do we need to start?
Less than you fear, more than zero: we often begin with a few hundred labeled examples plus augmentation, and design the data-collection rig as part of the project.
Can the model run without internet, inside our machine?
Yes — edge deployment is our default for industrial work: quantized models on Jetson- or NPU-class hardware, fully offline.
What accuracy can you promise?
We don't promise before measuring — we define acceptance criteria with you, run a POC, and report honest numbers (including failure modes) before scaling.
Is our data safe with you?
NDAs as standard, on-premise training where required, and we're an IAI-certified supplier accustomed to defense-grade confidentiality.
Have a hard engineering problem?
- Email —
- rotem@segevtech.com
- Tel —
- +972-52-6444408
- Studio —
- Tel Aviv, Israel