Get your data ready before you build AI
Most AI projects stall on the data, not the model. We assess whether your data can support the AI you have in mind, fix what's missing, and build the pipelines AI tools depend on, for companies in Australia and Indonesia.
of AI projects without AI-ready data will be abandoned through 2026.
A model trained or prompted on inconsistent, undocumented data produces confident wrong answers. The fix isn't a better model. It's better data.
What AI-ready data looks like
We assess and improve your data on six dimensions.
Quality
Accurate, deduplicated, tested data, with problems caught before a model or a manager sees them.
Consistency
One agreed definition for every core metric, so AI answers match the finance report.
Context
Documentation and lineage, so people and models know what each field means and where it came from.
Freshness
Pipelines that deliver data as quickly as the use case needs, and alert when they fail.
Governance
Personal and sensitive data identified, access-controlled and used only where allowed.
Cost control
AI workloads monitored and budgeted, so experiments don't quietly double your cloud bill.
How we help
- Assess. An AI-readiness score across the six dimensions, plus your AI ideas ranked by feasibility, value and effort. Included in the Data Health Check.
- Fix. Close the gaps through Data Rescue, or build the missing platform with Data Foundations.
- Feed. Build the pipelines AI uses: clean retrieval data for chat and search over your documents and data, feature tables for prediction, and evaluation sets to test answers.
- Govern. Personal-data tagging, access controls and usage monitoring, aligned with the Privacy Act (Australia) and UU PDP (Indonesia).
Typical AI use cases we prepare data for
- Internal assistants that answer questions on company data
- Customer support search and chat
- Credit, churn and demand forecasting
- Automated reporting and anomaly alerts
Questions
What does AI-ready data mean?
Data that an AI system can use safely and reliably: complete and consistent, with one definition per metric, documented so people and models know what it means, fresh enough for the use case, and governed so personal and sensitive data is only used where it is allowed.
Do we need a data warehouse before using AI?
Not always. Simple AI tools can work on a few clean sources. But once AI needs to answer questions about your business, such as customers, revenue or operations, it needs the same trusted, joined-up data a warehouse provides. Otherwise it gives confident wrong answers.
Can you build our AI application?
We focus on the data layer: pipelines, data quality, governance and the retrieval data and features AI tools depend on. We work alongside your product team or AI vendor, and can advise on whether to build or buy.
What about privacy rules?
AI projects often touch personal data. We help you identify and tag it, control access, and keep usage within Australia's Privacy Act and Indonesia's Personal Data Protection Law (UU PDP). We're engineers, not lawyers, so we work alongside your legal advice.
How do we start?
With a Data Health Check, which includes an AI-readiness assessment. It takes two weeks at a fixed price, and you get a scored view of your data plus your AI ideas ranked by feasibility and effort.
Planning an AI project?
Book a free 30-minute call. We'll tell you honestly whether your data is ready and what it would take.