AI strategy, enterprise LLM deployment, and data platform engineering for organisations ready to build competitive advantage on intelligence.
AI that delivers business outcomes, not demos. Production-grade generative AI, predictive models, and data platforms built with responsible AI practices, MLOps discipline, and governance embedded from day one. Our AI engineering teams cover the full stack from data engineering and feature stores to model serving, evaluation harnesses, and drift monitoring.
Domain-tuned assistants on GPT-4, Claude, Gemini, and open-weights (Llama, Mistral). RAG pipelines with vector stores (Pinecone, Weaviate, pgvector), guardrails via NeMo Guardrails or Llama Guard, and enterprise SSO plus role-based access. Deployed in customer service, sales enablement, and internal knowledge use cases.
Time-series forecasting, anomaly detection, and decision-support models on your data. Frameworks include XGBoost, LightGBM, Prophet, and PyTorch. Feature engineering pipelines, SHAP-based explainability, and model performance monitoring against agreed accuracy thresholds.
Production ML pipelines across language (NER, classification, summarisation), vision (object detection, OCR, defect detection), and structured data. MLOps on MLflow, Weights & Biases, Kubeflow, and Vertex AI. CI/CD for models, automated retraining, and drift detection built in.
Bias testing, explainability, fairness auditing, and governance frameworks that pass regulatory scrutiny. Aligned to NIST AI RMF, EU AI Act, and Singapore's AI Verify. Model cards, data lineage tracking, and audit-ready evidence packs for regulated industries.
End-to-end GenAI programmes with data engineering, MLOps, and responsible-AI guardrails delivered as one stack. Deployment patterns proven at scale on Azure OpenAI, AWS Bedrock, and self-hosted open-weights, with FinOps controls to keep inference costs predictable.