AI and Machine Learning Solutions.

Predictive Analytics and Forecasting

We build models that turn historical and real-time data into forecasts that drive operational decisions: demand forecasting, customer behaviour prediction, risk forecasting, equipment failure prediction, capacity planning, financial forecasting, and more.

Our airisDATA RWA Forecast Challenger is one example: an explainable, AI-driven forecasting system for risk-weighted asset planning in regulated capital workflows. Built originally for a tier-1 bank facing Basel IV's projected +24% RWA impact, the system generates accurate, explainable RWA forecasts that lower funding costs and free up capital. The same forecasting patterns apply to demand planning, capacity forecasting, and any other domain where decisions depend on forward-looking estimates with explainability requirements.

Generative AI

We design and ship generative AI systems for the enterprise. RAG architectures over your proprietary data. Foundation model fine-tuning for your domain. Document understanding. Content and code generation. AI-augmented internal workflows.

We work across closed-source frontier models (GPT, Claude, Gemini) and open-source models (Llama, Mistral, Qwen, and others), recommending the right fit for your data sensitivity, latency, accuracy and cost constraints. Common use cases we ship: enterprise knowledge assistants grounded in proprietary data, document understanding and structured extraction, automated drafting of regulated documents (clinical study reports, regulatory submissions, contract drafts), code generation and modernisation assistants, and internal workflow copilots.

Natural Language Processing

NLP-driven document understanding, classification, entity extraction, summarisation and conversational interfaces. The transformer-based architecture our airisDATA practice built for the LIBOR contract review work transfers directly to clinical documents, regulatory submissions, claims processing, custody agreements and any other corpus where you need to extract structured meaning from unstructured text.

Our NLP work covers transfer learning from pre-trained language models, fine-tuning on domain-specific corpora, hybrid rules-and-ML pipelines for regulated environments where pure ML is not yet acceptable, and the production engineering that makes NLP systems reliable at enterprise volume.

Computer Vision

Computer vision systems for visual quality inspection, defect detection, document image processing, in-store retail analytics, medical imaging support and remote monitoring. End-to-end pipelines from data labelling through model training, deployment, and monitoring.

We work across the major computer vision modalities: image classification, object detection, semantic segmentation, instance segmentation, OCR and document understanding, video analytics, and multimodal systems combining vision with text or structured data.

Anomaly Detection and Fraud

Real-time anomaly detection for fraud, AML/KYC, equipment health, network behaviour, transaction monitoring and operational anomalies. Built on the same patterns we ship into tier-1 bank fraud and risk operations.

Our anomaly detection work covers supervised approaches when labelled data is available, unsupervised approaches when it is not, and hybrid approaches that combine the two. We design for the operational reality that the cost of a false negative and the cost of a false positive are usually very different, and that the threshold setting needs to be tunable as the deployment matures.

Recommendation and Personalisation

Recommendation engines, semantic search, customer intelligence, next-best-action systems and content personalisation for retail, financial services and digital experiences. We build collaborative filtering, content-based, hybrid and modern transformer-based recommendation systems.

MLOps and Model Lifecycle

The platform and process foundation that makes ML production-viable. Feature stores. Training pipelines. Model registries. Deployment automation. Monitoring. Drift detection. Retraining workflows.

Specific deliverables: feature store engineering on Tecton, Feast, or hyperscaler-native platforms; training pipeline orchestration on Airflow, Kubeflow, Argo, or hyperscaler-native; model registry implementation on MLflow, Vertex Model Registry, SageMaker Model Registry, or custom; serving infrastructure for batch and real-time inference; monitoring and drift detection tooling; automated retraining workflows.

Model Explainability and AI Governance

Explainable AI (XAI) tooling, decision-engine instrumentation, audit trails and the governance frameworks regulators now expect. Built first for SR 11-7 model risk in banking, applied across regulated industries.

Coverage includes SHAP and LIME-based explainability, model card generation, validation documentation that meets SR 11-7 and equivalent standards, audit-ready model registries with full lineage, and bias and fairness testing frameworks.