AI & Machine Learning

Computer Vision

We build computer vision solutions for real-world operations, including classification, detection, and quality automation with measurable outcomes.

Best fit: Visual intelligence for detection, inspection, and classification.

Computer Vision engineering environment

How We Apply It

Design pipelines for acquisition, labeling, and preprocessing

Train and validate task-specific CV models

Optimize inference for production environments

Integrate visual results into business workflows

Architecture Quality

Built for maintainability and performance from day one.

Production Workflows

Clear implementation process from prototype to deployment.

Reliable Delivery

Security, observability, and stable release standards.

Custom Model Training Strategy

We train custom models for specific business problems, either from scratch using private datasets or through transfer learning when speed and prior knowledge matter. If a client does not have a prepared dataset, we run research, map relevant data sources, and build the right dataset strategy to train a model tailored to the target problem.

Common Use Cases

Quality inspectionDefect detectionVisual classificationObject countingDocument image processing

Related Technologies

PyTorchOpenCVFastAPIEdge deployments

We Also Work With Many Other Technologies

Beyond our primary stack, we integrate with data, cloud, automation, and security ecosystems based on each project.

Data Storage

PostgreSQL, MySQL, MongoDB, Firebase, ElasticSearch, Kibana, Airtable

Infrastructure & Cloud

Docker, Kubernetes, Jenkins, GitHub Actions, Google Cloud, AWS

Integrations

Google Ecosystem, Custom API Integrations, MercadoPago, Stripe, Analytics Tools, Meta Ads Integrations

Security

Secure Auth, Role-Based Access, Encryption, Audit Logs, OWASP Practices

AI & Automations

n8n, Custom AI Workflows, RAG Systems

Other

C/C++, OpenGL, raylib

Engineering with the Right Stack

We Select Technologies for Reliability, Speed, and Long-Term Ownership

Beyond frameworks, we design robust software systems with clear architecture, deployment standards, and maintainability principles. For ML projects, we also cover custom model training, transfer learning, and dataset strategy.

Decision Process

Tech choices by business context

Architecture

Scalable and maintainable systems

ML Enablement

Custom datasets and transfer learning

Delivery

Production-ready implementation

Let's Build

Need This Technology in Your Product?

We help teams choose the right stack, design architecture, and ship reliable systems with measurable outcomes.