We take machine learning from a first experiment to a system in production, and stay on to support it.
We train named entity recognition and text classification models on your documents, one model per field you care about, so each one can be measured and improved on its own.
We build video analytics pipelines with NVIDIA DeepStream and deploy them to Jetson AGX Xavier, so inference runs next to the camera instead of in a distant data centre.
From data preparation to training and evaluation, we build neural network classifiers sized for the problem and the hardware they will run on.
We built conversational systems on trained neural models, and now combine them with large language models, grounding answers in your own documents and data.
We build models for sensor, usage and business data that arrives as a sequence, so you can plan ahead and be warned when something stops looking normal.
We build end-to-end pipelines for models in production, similar to managed services such as Amazon SageMaker Pipelines, and we run them on cloud or on your own premises.
When the right approach is not obvious, we run structured experiments, compare methods on your data and report what works, what does not, and why.
We take models into production: services, edge devices and integrations with the systems you already use, with documentation and support after hand-over.
Tell us what you need to read, see or predict. We will get back to you directly.