Artificial Intelligence

AI Consulting & MLOps

From AI strategy and use case identification through to model development, deployment and production operations — end-to-end AI delivery for regulated enterprises.

What We Do

AI that reaches production — not just the proof of concept

Most enterprise AI programmes fail at the transition from pilot to production. The reasons are consistent: no clear business case, inadequate data foundations, models that cannot be explained to regulators, and MLOps infrastructure that was never designed for scale. AIONIX addresses all four — from the first strategy conversation to the live system in production.

Our AI practice combines strategic advisory with deep engineering capability. We hold expertise in classical ML, large language models, knowledge graphs, RAG architecture and computer vision — applied across financial services, healthcare, government and manufacturing.

  • Enterprise AI strategy and use case prioritisation
  • AI readiness assessment — data, governance and infrastructure
  • Machine learning model development and validation
  • Large language model (LLM) integration and RAG architecture
  • Knowledge graph design and ontology engineering
  • MLOps platform design, deployment and monitoring
  • Explainable AI and model governance for regulated industries
  • AI ethics, bias assessment and responsible AI frameworks
  • AI operating model and centre of excellence design
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AI Strategy

Use case identification, feasibility assessment, investment prioritisation and AI roadmap development tied to measurable business outcomes.

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Model Development

End-to-end ML model development — data preparation, feature engineering, model training, validation and explainability documentation.

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LLM & RAG

Enterprise LLM integration with retrieval-augmented generation (RAG) for grounded, auditable AI outputs. Prompt engineering and guardrail design.

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MLOps

CI/CD for ML models, feature stores, model registries, drift monitoring, retraining pipelines and production observability on AWS, Azure and GCP.

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Knowledge Graphs

Enterprise knowledge graph design using RDF/OWL, ontology engineering and SPARQL — enabling semantic search, reasoning and intelligent decision support.

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Responsible AI

Bias testing, fairness evaluation, explainability frameworks and AI governance documentation for APRA, MAS and EU AI Act alignment.

Other Services