About Pixtell
A computer-vision partner grounded in business reality.
We help organizations adopt visual AI without building and managing a full in-house ML team from scratch.
Why Pixtell as a name
Pix for pixels and visual data. Tell for insight and decision-making.
Every image stream contains signals about quality, safety, behavior, and opportunity. Our role is to extract those signals and operationalize them.
What we solve
- No internal computer-vision team
- Slow and expensive specialist hiring
- Execution risk with fragmented freelancers
Pixtell operates as a dedicated team-as-a-service for end-to-end visual AI delivery.
About the founder
Pixtell was founded by Hassan Nasser, a computer-vision scientist and entrepreneur with a PhD in visual neuroscience.
His background combines research depth and delivery accountability, from scientific work to startup and product execution.
How that benefits clients
- Open-ended problems become testable milestones
- Scientific rigor balanced with practical delivery
- Systems designed for long-term maintainability
See how collaboration works in practice
From discovery to deployment, we keep execution transparent and milestone-driven.
Swiss Made Software label
What AI means for Pixtell
Pixtell builds applied computer vision solutions that automate the extraction of actionable insights from images. We transform raw visual data into structured information that integrates directly into operational workflows.
Typical use cases include defect detection on manufacturing lines, livestock or wildlife monitoring, weed and crop detection from drone imagery, infrastructure and asset counting from satellite data, and environmental or safety monitoring in complex environments.
AI technologies we use
We develop domain-specific visual AI systems using CNNs, Vision Transformers, object detection families (including DETR-style approaches), segmentation workflows (including SegFormer and SAM-based pipelines), and multi-object tracking systems.
We primarily rely on open-source architectures and deep learning frameworks. Where justified by deployment constraints, licensed commercial visual models can be integrated. For specific use cases, we may also integrate multimodal or external vision foundation models when they provide measurable added value.
Model development and governance
Models are adapted per use case through supervised training, transfer learning, real-world validation, and iterative optimization. Trained model weights are client-owned. We do not perform uncontrolled or unsupervised training on client data.
Deployment and hosting
Pixtell is infrastructure-agnostic. Solutions can run on edge systems or cloud environments, including Swiss-based infrastructure, private enterprise clouds, and public cloud providers. Infrastructure is selected based on compliance, performance, latency, and cost requirements.