Skip to content
InferzoINFERZO
Machine learning

Most teams overbuild the model and underengineer everything around it.

We match the model to the problem, build it to ship, and keep it accurate after it lands.

Invoke us
Practices

Four things we do inside ML.

Computer Vision

A camera that records is just storage. We build the model layer that makes it act: detect, classify, count, alert. YOLO-family architectures trained on your actual environment, not a generic checkpoint aimed at your use case and hoping for the best. Edge or cloud, your call.

Fine-tuning & SLMs

GPT-4 knows everything about the internet and nothing about your business. Fine-tuning fixes that. We take the right base model, adapt it on your data with LoRA or QLoRA, and hand you something more accurate on your task than the frontier model you were paying per token for.

Predictive Analytics

Not every prediction problem needs a GPU. If your data lives in a database, a gradient-boosted tree usually beats a neural network and costs a fraction to run. We build churn models, demand forecasts, fraud detectors, and pricing models on your structured data.

MLOps & Evaluation

A model's accuracy at launch is usually its peak. Data drifts, the world changes, and the model does not know. We build the layer that catches it: evaluation frameworks, drift monitoring, retraining pipelines, and alerts that fire before your users notice the quality drop.

Click any card to go deep.