From AI strategy and use case identification through to model development, deployment and production operations — end-to-end AI delivery for regulated enterprises.
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.
Use case identification, feasibility assessment, investment prioritisation and AI roadmap development tied to measurable business outcomes.
End-to-end ML model development — data preparation, feature engineering, model training, validation and explainability documentation.
Enterprise LLM integration with retrieval-augmented generation (RAG) for grounded, auditable AI outputs. Prompt engineering and guardrail design.
CI/CD for ML models, feature stores, model registries, drift monitoring, retraining pipelines and production observability on AWS, Azure and GCP.
Enterprise knowledge graph design using RDF/OWL, ontology engineering and SPARQL — enabling semantic search, reasoning and intelligent decision support.
Bias testing, fairness evaluation, explainability frameworks and AI governance documentation for APRA, MAS and EU AI Act alignment.
Talk to our AI practice about your use cases, data maturity and what it would take to reach production.