AI & Computer Vision Solutions

Machine Learning

Custom ML models for classification, regression, recommendation engines, and anomaly detection — trained on your data.

Computer Vision

Object detection, image segmentation, OCR, facial recognition, and real-time video analytics for industrial and commercial use.

Augmented Reality

Immersive AR experiences for retail, education, and manufacturing — real-time 3D overlays, spatial tracking, and interactive visualizations.

Natural Language Processing

Sentiment analysis, chatbots, document summarization, entity extraction, and multilingual language understanding.

Predictive Analytics

Demand forecasting, churn prediction, dynamic pricing, and risk scoring powered by statistical and ML models.

Intelligent Automation

End-to-end workflow automation combining AI models with business logic — from document processing to decision support systems.

Route optimization, driver behaviour analysis, license plate recognition, and predictive maintenance alerts.

Automated visual inspection, defect detection on assembly lines, and yield optimization via sensor data.

Product recommendation engines, shelf monitoring, customer foot-traffic heatmaps, and demand forecasting.

Medical image analysis, patient triage assistants, drug interaction checks, and appointment no-show prediction.

Real-time intruder detection, crowd density estimation, PPE compliance monitoring, and perimeter alerts.

Intelligent document parsing, invoice extraction, contract analysis, and automated data entry from scanned forms.

Frameworks

TensorFlow, PyTorch, scikit-learn, Hugging Face, OpenCV

Cloud AI

AWS SageMaker, Google Vertex AI, Azure ML, NVIDIA Triton

Data

Apache Spark, Airflow, dbt, PostgreSQL, BigQuery, Snowflake

MLOps

MLflow, Kubeflow, DVC, Weights & Biases, Docker, Kubernetes

What industries benefit most from computer vision?

Retail, manufacturing, healthcare, logistics, and security see the highest ROI. Applications range from quality inspection on production lines to real-time shelf monitoring in stores and medical image analysis in hospitals.

How does augmented reality integrate with computer vision?

AR relies on computer vision for scene understanding — detecting surfaces, tracking objects, and placing virtual content in real-world environments. Together they power experiences like virtual try-ons, interactive training, and spatial navigation.

What is the typical timeline for an AI proof of concept?

Most PoCs take 4–8 weeks depending on data readiness and model complexity. We start with a discovery workshop, prepare the dataset, train and validate the model, then deliver a working demo with performance benchmarks.

Do you support on-premise AI deployments?

Yes. We deploy models on edge devices, on-premise servers, or private cloud — critical for industries with strict data residency requirements like healthcare and government.

What makes Trasealla different from other AI companies?

We focus on production-grade solutions, not just research. Every model we build comes with monitoring, retraining pipelines, and API integration — ready for real business operations from day one.

Can computer vision work with existing CCTV systems?

Absolutely. We integrate with existing IP cameras and CCTV infrastructure using RTSP/ONVIF protocols, adding AI-powered analytics like people counting, intrusion detection, and behavior recognition without replacing hardware.