Service 03

Data Science, AI & Machine Learning

Work that starts from a decision someone has to make, then asks what data would improve it. Where a model earns its place we build one; where a rule or a query answers the question, we say so. Every model ships with the limits of what it can be trusted to do.

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What we offer

Scope of the service line

Data Science

  • Data analysis
  • EDA
  • Data cleaning & preprocessing
  • Statistical analysis
  • Feature engineering
  • Data visualization
  • Business analytics

Machine Learning

  • Classification
  • Regression
  • Clustering
  • Recommendation systems
  • Fraud detection
  • Anomaly detection
  • Customer prediction
  • Demand forecasting
  • Predictive modelling

AI

  • AI-powered applications
  • Generative AI applications
  • AI assistants and chatbots
  • Intelligent automation
  • AI APIs and integrations

NLP

  • Text classification
  • Sentiment analysis
  • Text analytics
  • Document processing
  • NLP-based chatbots
  • Language-based automation

Computer Vision

  • Image classification
  • Object detection
  • Image recognition
  • Image analysis
  • Visual inspection
  • Medical and industrial image analysis

Time-Series & Forecasting

  • Sales forecasting
  • Demand forecasting
  • Business trend prediction
  • Customer behaviour prediction

Big Data

  • Large-scale data processing
  • Pattern discovery
  • Data pipelines
  • Distributed analytics

Technology we use

The stack behind it

Grouped by layer. Selection depends on the system, not on preference.

Programming

PythonSQLRJavaScript

Data Science

PandasNumPySciPyMatplotlibPlotly

Machine Learning

Scikit-learnXGBoostTensorFlowPyTorch

AI / Deep Learning

TensorFlowPyTorchNLP frameworksComputer Vision frameworksGenerative AI / LLM APIs

Data Storage

PostgreSQLMySQLMongoDBSQL/NoSQLAmazon S3Google BigQuerySnowflake

Data Processing

Apache SparkHadoopApache NiFiPandas

Cloud AI/ML

AWSGoogle CloudMicrosoft AzureCloud ML platforms

How models reach production

A model is software, so it is versioned, tested and monitored like software. We define the acceptance criteria before training, keep the data pipeline reproducible, and record what the model was evaluated against. Data protection is part of the design: what is collected, where it is stored, who can reach it, and what is removed once it is no longer needed.

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Start the conversation

Tell us the outcome you need and the constraint you are working within. We will tell you honestly what is involved.

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