We develop artificial intelligence models for specific problems in science, industry, and organizations.
We design, train, and evaluate models from experimental, operational, or proprietary data, selecting the methods and architectures best suited to each problem.
We work with different families of models and learning methods to predict, classify, detect anomalies, discover patterns, optimize processes, and assist decisions.
What we develop
- Predictive modelsModels to estimate outcomes, behaviors, properties, and events from data.
- Detection and classificationSystems to recognize categories, conditions, anomalies, and relevant patterns.
- Clustering and discoveryModels to identify groups, relationships, and structures that are not previously defined.
- OptimizationModels and methods to explore alternatives and find better configurations for processes, experiments, or systems.
- Computer visionModels to extract information, detect objects, classify, or segment images.
- Decision systemsModels oriented to evaluate alternatives and assist decisions under multiple variables and constraints.
From data to model
Our process covers data preparation and structuring, experimentation with different approaches, training, evaluation, optimization, and validation.
When the model must be used outside the development environment, we implement it as an API, tool, or component of an AI product.
Compute infrastructure
Cognify combines local infrastructure for development, experimentation, and prototyping with on-demand cloud compute capacity for larger-scale workloads.
Our local environment includes NVIDIA RTX GPUs, and we scale to specialized GPU infrastructure when training, evaluation, or inference requirements demand it.