Computer Vision сompany

Computer vision
developemnt services

Bring in the experts who’ve built it before

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What is
computer vision?

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Computer vision is a subset of artificial intelligence that enables systems to interpret and make decisions based on visual data. It's like teaching a computer to see and understand the world similarly to how humans do.

Just as you recognise a familiar face in a crowd, computer vision algorithms can identify objects in images, analyse medical scans, or enable autonomous vehicles to navigate safely.

Why choose Devico for сomputer vision services?

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High retention rate

96%

We go beyond the 80% industry norm with reliable, expert support.

Wide expert network

3000

Access to over 3000 engineers and AI experts.

Proven track record

500,000

Over 500,000 man-days successfully delivered.

Support

24/7

Highly experienced management team available around the clock.

Gather accurate data

Computer vision adoption growth is expected to reach 270% over four years

How does it work?

01

Capture what matters

Feed the system with raw, relevant visual data – images, video, frames.

Capture what matters

02

Prepare for clarity

Filter the noise. Structure the visuals. Make every pixel count.

Prepare for clarity

03

Train the vision

Powerful models learn to recognize patterns, shapes, and meaning.

Train the vision

04

Test with precision

Every result is measured. Accuracy isn’t optional – it’s essential.

Test with precision

05

Deploy in the real world

The system goes live. Real-time decisions. Instant insight.

Deploy in the real world

06

Evolve with use

Performance is tracked, improved, and kept razor-sharp – always.

Evolve with use

How businesses are using
computer vision

Healthcare

Computer vision can analyse medical images for disease detection and treatment planning. It significantly improves diagnostic accuracy and speeds up the diagnosis process, leading to better patient outcomes.

Use cases:

  • Disease detection from medical imaging.
  • Surgical assistance with real-time image analysis.
  • Patient monitoring and anomaly detection.
  • Automated analysis of pathology results.
Finance & insurance

Financial institutions use computer vision for facial recognition to enhance security and automate processes like cheque deposit via mobile apps. This technology also helps in preventing fraud and ensuring compliance with regulations.

Use cases:

  • Facial recognition for secure customer authentication.
  • Automated processing of financial documents.
  • Detection of fraudulent activities.
  • Enhanced compliance with KYC (Know Your Customer) regulations.
Retail

Retailers leverage computer vision for personalised marketing and enhancing customer experiences. This technology is used to analyze customer behavior, manage inventory, and even create cashier-less stores.

Use cases:

  • Customer behavior analysis for personalized marketing.
  • Automated checkout systems.
  • Real-time inventory management.
  • In-store security and theft prevention.
Manufacturing

In manufacturing, computer vision helps with quality control and predictive maintenance. It ensures products meet quality standards and helps in maintaining equipment by predicting failures before they occur.

Use cases:

  • Automated quality control inspections.
  • Predictive maintenance of equipment.
  • Monitoring production lines for efficiency.
  • Safety compliance and hazard detection.

Engineered by experts. Built for impact.

Сore capabilities of
computer vision

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Image recognition

See what matters. Identify patterns, features, and objects in any image – from medical scans to product photos.

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Object detection

Know where and what. Spot objects in real time, whether on factory floors or city streets.

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Facial recognition

Faces become keys. Unlock security, personalization, and identity with precision and speed.

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Video analysis

Make sense of motion. Track activity, detect anomalies, and extract insight from every frame.

Advanced computer vision techniques

Criteria

Convolutional Neural Networks (CNNs)

Generative Adversarial Networks (GANs)

Transfer Learning

Definition

A class of deep neural networks, most commonly applied to analysing visual imagery.

A class of machine learning frameworks where two neural networks contest with each other to create new, synthetic instances of data.

Utilising a pre-trained model on a new, related problem.

Goal

Automatically and accurately recognise patterns in images.

Generate new, realistic images by learning the distribution of the original dataset.

Leverage existing models to reduce training time and improve performance on new tasks.

Algorithms

Convolutional layers, pooling layers, fully connected layers.

Discriminator and generator network.

Fine-tuning pre-trained models, domain adaptation.

Data Requirement

Requires large amounts of labelled image data.

Requires substantial data for both networks to learn the data distribution.

Requires less data than training a model from scratch, using pre-trained models.

Advantages

High accuracy in image classification tasks, ability to capture spatial hierarchies in images.

Capable of generating high-quality synthetic images, useful for data augmentation.

Significantly reduces training time and resources, improves performance with less data.

Applications

Image classification, object detection, facial recognition, medical image analysis.

Image generation, data augmentation, image-to-image translation.

Custom image classification, object detection, semantic segmentation.

Techniques

Backpropagation, activation functions (ReLU), dropout regularisation.

Adversarial training, optimisation of generator and discriminator.

Model fine-tuning, transfer learning architectures like VGG, ResNet.

Get in touch

Drop us a line about your project and we will contact you within a business day

Our locations

New York

HQ

521 Fifth Ave, NY 10175

+1 805 491 9331

London

Sales

9 Brighton Terrace, SW9 8DJ

+44 1922 214429

Warsaw

R&D

Towarowa 28, 00-847

info@devico.io

Lviv

R&D

Uhorska str. 14, 79034

info@devico.io

Questions & answers

Think faster quality checks, smarter cameras, automated monitoring, visual search – anywhere you need machines to “see” and act, a computer vision development company can help.

The cost depends on the complexity of the project, the technologies involved, and the scope of the solution. We offer tailored pricing based on your needs and goals to ensure you get the best value for your investment.

This varies depending on the specific project and set goals. Devico can help you assess your data readiness and explore strategies for maximizing its value. If you can’t provide a ready data set, we can use our field data collection for model development.

We use your visual data – products, locations, packaging, screens, etc. Our computer vision development services always adapt to your real-world use cases.

Yes. We work with both static images and real-time video streams to detect, track, or analyze moving objects in context.

We simulate edge cases and test models in real-world conditions – not just in perfect lab scenarios. The goal is performance where it matters: on your floor, with your data.

We’ll need access to sample visual data, context on your goals, and your preferred tech stack. From there, our computer vision development company runs with it.

Yes – we often start with a proof of concept to validate feasibility and ROI before going bigger.

Our computer vision development services include monitoring and retraining models regularly to keep them accurate as your environment changes.

Yes. We build models that spot issues instantly – from missing labels to faulty parts – and flag or act on them as needed.

Yes. Whether it’s edge devices, IP cameras, or cloud setups – our computer vision development services are built to integrate smoothly.

We follow strict compliance standards. Our computer vision development company can anonymize data and design models that respect privacy regulations like GDPR or HIPAA.