Cosmiqube

Service

Data analytics, machine learning and neural network solutions

Pipelines, dashboards and machine learning models built to survive contact with production data.

Most machine learning projects fail at the data, not the model. We start by establishing whether the data you have can support the decision you want to automate, and we tell you when a well-specified query would outperform a model. When ML is the right answer, we build it to be evaluated and monitored honestly.

What the engagement covers

Data pipelines

Batch and streaming ingestion with schema enforcement, so downstream consumers are not silently broken by an upstream change.

Warehousing and modelling

Dimensional models in the warehouse with tested, documented transformations rather than a sprawl of ad-hoc queries.

Analytics and reporting

Dashboards built around the decisions they support, with metric definitions agreed and written down before anything is charted.

Machine learning

Forecasting, classification, anomaly detection and recommendation, evaluated against a baseline so the benefit is measurable.

Deep learning

Neural network models where the problem justifies them — sequence, image and multimodal tasks — including fine-tuning existing models rather than training from scratch.

MLOps

Reproducible training, versioned models and datasets, and drift monitoring in production, because a model's accuracy decays quietly.

Tools we work with

Not an exhaustive list, and not a claim of certification — just what we reach for most often on this kind of work.

  • Python
  • PyTorch
  • TensorFlow
  • scikit-learn
  • dbt
  • Airflow
  • Spark
  • DuckDB
  • MLflow

Common questions

We are not sure we have enough data. Can you tell us?
That is a reasonable first engagement on its own. A short assessment of your data volume, quality and labelling will tell you whether a model is viable before you commit to building one.
Do you build on top of large language models?
Where they fit the problem. Retrieval, extraction and classification tasks often suit them well. We are equally willing to say when a smaller conventional model is cheaper, faster and more predictable.

Talk to us about data analytics & ml

Send over what you are dealing with. We will reply with a straight assessment of whether we are the right team for it.

Get in touch