Data not ready for AI
Data is scattered, inconsistent and poorly governed, so models cannot be trusted.
04 Artificial intelligence & data
Production-grade AI, built on data you can trust.
We build modern data platforms and deploy AI that is evaluated, monitored and governed, moving organisations from experiments to measurable value in production.
Challenges we solve
Pilots that never scale
Proofs of concept impress in demos but stall before production.
Risk and compliance concerns
Leaders are unsure how to deploy AI safely under the NDPA 2023 and sector rules.
Deliverables what we deliver
What you get.
AI strategy & use-case selection
Prioritised use cases scored for value, feasibility and risk.
Data platforms
Lakehouse architecture, ELT pipelines, data quality and cataloguing.
Retrieval-augmented generation
Assistants grounded in your documents and knowledge bases.
Intelligent document processing
Extraction and classification of forms, invoices and claims.
MLOps & monitoring
Deployment pipelines, evaluation, drift detection and observability.
AI governance
Policies, guardrails, human oversight and audit trails.
Approach how we engage
How we work with you.
Assess
Use-case discovery and data readiness assessment.
Prove
Scoped proof of value with defined evaluation metrics.
Productionise
Secure deployment, integration and MLOps foundations.
Govern
Monitoring, evaluation gates and continuous improvement.
Toolkit technologies & frameworks
What we work with.
- Python
- dbt
- Apache Spark
- Delta Lake
- PostgreSQL / pgvector
- Azure OpenAI
- AWS Bedrock
- LangChain
- MLflow
- Power BI
FAQ common questions
Questions we are often asked.
Where should we start with AI?
Start with a high-volume, rules-based process where data exists and outcomes can be measured, such as document intake or customer queries.
Will our data leave our environment?
We design deployments to keep data within your cloud tenancy and region, with access controls and logging.
How do you measure AI quality?
Each solution has defined evaluation metrics, for example groundedness, precision and latency, tested before and after release.
Do we need a data platform first?
Not always. Many use cases can begin on existing data, while we build the foundations in parallel.
Explore other services
Let's scope your artificial intelligence & data needs.
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