How many labeled images do we need?

For fine-tuning a pre-trained model, 500-2,000 labeled images per class can be sufficient. Training from scratch requires significantly more. We assess during the data audit.

Can this run at the edge (cameras, IoT devices)?

Yes. We optimize models for edge deployment using quantization, ONNX, and TensorRT. Target devices include Nvidia Jetson, Raspberry Pi, and industrial cameras.

What about real-time video processing?

We build real-time pipelines using frame sampling, batching, and optimized inference backends. Latency targets depend on your hardware and use case.

Computer Vision

Make your systems see what matters.

Object detection, image classification, OCR, defect detection, and video analytics: custom-trained on your domain and data.

Discuss a CV project Book a 30-min Call

Free, no commitment. Takes about 30 seconds.

6 wksMedian to production

40%Avg manual work cut

3 daysFirst engineer shortlist

What we build

Computer vision capabilities

Object detection

Locate and classify multiple objects in images or video frames. Works for products, people, vehicles, defects, and custom classes.

Image classification

Assign categories to images at scale. Binary or multi-class. Trained on your labeled dataset or fine-tuned from pre-trained models.

OCR and document extraction

Extract structured data from invoices, forms, IDs, and contracts. Handle variable layouts without manual template definition.

Defect detection

Identify manufacturing defects, surface anomalies, or quality issues from camera feeds. Reduce QA labor and false-negative rates.

Video analytics

Object tracking, activity recognition, and event detection in video streams. Real-time or batch processing.

Semantic segmentation

Pixel-level classification for medical imaging, satellite imagery, autonomous systems, and precision agriculture.

Tools

CV tooling we use

PyTorchTensorFlowKerasJAXOpenCVYOLODetectron2TorchvisionUltralyticsCLIPSAM (Segment Anything)DINOGrounding DINOTorchServeTritonONNX RuntimeTensorRT

Process

How we build CV systems

enzrossi-ai, process

$ step.01

$ step.02

$ step.03

$ step.04

step 01

Data collection and annotation

We assess your existing image data, design annotation schemas, and build labeled datasets. This is usually the hardest part.

step 02

Model architecture selection

We choose between training from scratch vs. fine-tuning a pre-trained backbone based on your data volume and compute budget.

step 03

Training and evaluation

Iterative training with precision/recall tradeoff analysis. We optimize for your specific error cost: false positives vs. false negatives.

step 04

Deployment and edge optimization

Deploy to cloud API, on-premise server, or edge device. We optimize inference speed with quantization and pruning if needed.

Before you decide

The doubts everyone has before hiring us.

And what we do about each one.

AI projects never make it past the prototype.
Your data is assessed in the first week, and you get an honest answer on whether there is enough signal to build something useful.
Data assessment in week one
This will just be a chatbot on top of an API.
Language models are used when they fit, like text and language tasks.
The right tool for the problem
Our data is too sensitive to leave our infrastructure.
Model training can run entirely in your cloud, so your data never leaves your infrastructure.
Training in your cloud
We'll end up locked in to you.
Trained weights, training code, data pipelines and all documentation transfer to you on delivery.
Models and code are yours

FAQ

Common questions

Build something visual

Tell us what you need machines to see.

Start a CV project Book a 30-min Call

Free, no commitment.Prefer email? contact@enzrossi.com

What happens next

1
You tell us what you need. It takes about 2 minutes.
2
A real person replies within 4 business hours.
3
A 30-minute call to understand the project and agree on next steps.