Computer Vision

Model Optimization and Deploy

A model that works in the lab but does not hold up in production is worthless.

Building an AI model is only half the work. The other half is making it run — in production, at real scale, with the latency, hardware and cost constraints the context imposes. This is where most projects get stuck.

We design the entire pipeline from model to production output: performance optimization, reduction of computational requirements, deployment on cloud, on-premise or edge infrastructure. The right model, on the right hardware, with the right latency.

Problems we solve

Blocked knowledge, critical dependencies and inefficiencies holding back growth

Models too heavy for the operational context

A model trained without hardware constraints cannot be deployed on edge, mobile or hardware with limited resources.

Unacceptable latency in production

Slow inference means blocked pipelines, waiting users, real-time systems that cannot keep up.

Out-of-control inference costs

Large models on expensive GPUs cost a lot. With quantization, pruning and distillation, similar performance can be achieved at a fraction of the cost.

Undetected drift and degradation

A model in production degrades over time. Without active monitoring, it is not noticed until the problem is already large.

Use Cases

Real projects, measurable results

Computer vision and audio models optimized to run on hardware with limited computational resources:…

Optimization for edge and mobile hardware deployment

Computer vision and audio models optimized to run on hardware with limited computational resources: footprint reduction through quantization, pruning and knowledge distillation.

Enterprise-level performance on hardware with limited resources.

Optimization for edge and mobile hardware deployment

Configuration of scalable model serving stacks — TorchServe, Triton Inference Server, vLLM — to han…

Serving and production infrastructure

Configuration of scalable model serving stacks — TorchServe, Triton Inference Server, vLLM — to handle high volumes with controlled latency.

The right architecture for the right volume, with the latency the use case requires.

End-to-end MLOps pipeline: model versioning, CI/CD for updates, production performance monitoring, …

MLOps and production monitoring

End-to-end MLOps pipeline: model versioning, CI/CD for updates, production performance monitoring, automatic drift and degradation detection.

The model does not update itself. The system that manages it does.

Technology Corner

The architecture and technologies that make the difference

Our projects don't rely on off-the-shelf solutions. Every system is designed on the right architecture for the specific problem — and the technology choice makes the difference between a tool that works in demo and one that holds up in production.