Services / AI & Computer Vision
AI & Computer Vision
Custom AI features and computer-vision pipelines built for production systems not just notebooks and demos.
What’s included
- Real-time object detection and tracking with OpenCV
- Custom model training and evaluation with TensorFlow/PyTorch
- Turning a working notebook model into a production service that runs reliably on live data
- Image and video processing pipelines, including live camera/stream input
- Python backend integration with existing Laravel or web applications
- Workflow automation using AI-driven decision logic
Relevant work
Roulette OpenCV a computer-vision project using OpenCV for real-time wheel tracking, applying Python and CV to a real, physically-constrained tracking problem. See more on the Portfolio page.
STACK
Python OpenCV TensorFlow PyTorch
TYPICAL ENGAGEMENT
Scoping call to define accuracy and latency requirements → proof of concept → production integration. Remote-friendly.
AI development services from feasibility to production
AI projects are easiest to control when the problem is defined before the model. I start with the workflow, input data, expected output, acceptable error rate, latency requirement, and deployment environment. That helps determine whether the best solution is an existing model integration, a custom-trained model, a computer-vision pipeline, or a simpler automation layer.
AI and computer vision services by use case
- Computer vision and object detection — image/video analysis, real-time detection, tracking, and camera-based workflows.
- Custom AI model development — model selection, training, evaluation, and deployment when off-the-shelf models are not sufficient.
- AI workflow and document automation — classification, extraction, summarization, routing, and decision-support flows.
- AI integration for existing software — adding language, vision, or automation capabilities to an existing Laravel or web application.
Production reliability matters more than a demo
A model working on sample data is only the beginning. Production systems need input validation, retries, fallbacks, monitoring, logging, API boundaries, and predictable behavior when confidence is low or a provider is unavailable. For real-time vision, the system also needs to account for camera conditions, frame rate, latency, hardware, and tracking stability. The article on computer vision in production goes deeper into these constraints.
For practical proof, see the Roulette OpenCV case study. If you are still budgeting the project, the AI development cost guide explains how data, custom training, integrations, and production requirements affect scope.
What to send for an AI feasibility review
Share representative input data, the output you expect, examples of unacceptable errors, the target response time, any privacy constraints, and where the feature needs to run. That is usually enough to decide whether the idea should move to a proof of concept, direct integration, or a custom-model phase.