AI, Machine Learning

MLOps and production model deployment

  • Model registry, versioning and reproducible training pipelines.
  • Automated deployment to batch or real-time inference endpoints.
  • Drift monitoring, performance SLAs and rollback of bad models.
  • On-prem or cloud — aligned with data residency requirements.

Data Engineering Services for AI projects

We build data platforms on-premises and in the cloud, sized for analytical workloads rather than copied from an operational CRM stack.

Example methods and projects:

Computer vision

  • Image classification
  • Object detection (YOLO and related detectors)
  • Instance and semantic segmentation (Mask R-CNN and successors)
  • OCR on documents and industrial media

Natural language processing

  • Text classification, including aspect-based sentiment
  • Named-entity recognition
  • Assistants and retrieval systems — statistical or rule-based, depending on risk and data

Plus classical supervised learning and time-series models (including ARIMA-family) where they outperform a heavier network.

Data platform engineering

Our highly skilled data engineers have expertise in the technologies commonly used in both cloud and on-premises data architectures.

We have the capability to create a data architecture from scratch or enhance an existing data platform with additional features, such as designing data storage layers (data lakes and warehouses), implementing data pipelining (ETL jobs or advanced batch data processing), or integrating analytical components (BI tools and deploying AI models).

Application Technology and programming languages
Health — narrative models, classification and statistical analysis. IT security — classifiers for malicious commands, behaviour models, anomaly detection. Search and documents — NLP over internal corpora and crawls. Robotics and embedded — simulation, computer vision and control where we have delivered, not as a showroom list. Numerical methods — solver warm-start and surrogate models.

NLP: spaCy, Hugging Face Transformers, NLTK; annotation with Doccano or Prodigy; encoder and decoder architectures (BERT-family, long-context transformers, current LLM APIs where they fit).

Computer vision: PyTorch, TensorFlow / Keras, Detectron2, YOLO; annotation with CVAT. Domains we actually ship in: medical imaging, document analysis, industrial and CCTV inspection.

Engineering: Python and R for modelling; Java / Spring and Node where the product requires it; Docker, Kubernetes, Terraform, Ansible; PostgreSQL, Kafka, Spark when the volume justifies them; AWS, GCP and Azure.

More services