AI + Data Integration

The insight is already in your data.

Most regulated service organisations are sitting on operational data they can't act on fast enough — buried in care systems, CRMs, billing platforms, and spreadsheets. Taidotech builds the analytics and AI layer that surfaces what matters, when it matters, in a form that's interpretable and defensible.

“The organisations getting the most from AI aren't the ones with the most data — they're the ones who know which question to ask of it.”
Taidotech — on operational intelligence
AUS
All data processed and stored in Azure East Australia. No offshore transfer. Full data sovereignty by default.
100%
Interpretable outputs. Every insight traceable to its source data — designed to be defensible in audit, board, or regulator review.
Azure
Built on Microsoft Azure AI Foundry and Fabric — enterprise-grade infrastructure with the governance controls regulated industries require.
Governed
No black boxes. Every model, every output, every recommendation is explainable and subject to human review before action.

What this capability does

Turning operational data into decisions you can act on.

Regulated service organisations generate enormous volumes of structured operational data — care plans, billing records, scheduling logs, call records, compliance events, staff movements. Most of it sits in systems that aren't designed to surface patterns, flag anomalies, or produce the kind of operational intelligence that leadership and governance functions actually need.

Taidotech's AI + Data capability connects to your existing systems — whether that's AlayaCare, Dynamics 365, SharePoint, a data warehouse, or a combination — and builds the analytics and AI layer on top. The output is operational dashboards, anomaly alerts, predictive models, and integration pipelines that give your team the information they need to act, not just report.

Every solution is designed to be interpretable and auditable. In regulated environments, an AI output that can't be explained or traced to source data is a liability. We don't build black boxes.

The work typically falls into three categories: integration — connecting systems that currently don't talk to each other; analytics — dashboards and reporting that surface operational patterns; and AI models — anomaly detection, predictive modelling, and classification that go beyond what static reports can show.

For most clients, we start with integration and analytics — establishing a reliable, consistent data picture before adding model complexity. The clients who've moved to AI models have done so because the operational data foundation was solid enough to trust the outputs.

All solutions are built on Microsoft Azure, with data processed and stored in Azure East Australia. We connect to sector-specific systems — AlayaCare, HealthStream, CMS platforms — as well as standard enterprise platforms across Microsoft 365, Dynamics 365, and Power BI.

Diagram showing how Taidotech's AI + Data capability connects source systems through integration and analytics into governed AI outputs.
How AI + Data fits — from source systems through integration, analytics, and governed AI to operational decisions.

Capability areas

Four areas within AI + Data.

Each area can be engaged independently or as part of a broader programme. Most clients start with one and expand as the data foundation strengthens.

01 — Integration

System integration and data pipelines

Connect your operational systems into a consistent, reliable data layer. AlayaCare, Dynamics 365, SharePoint, billing platforms, scheduling systems, and call records — integrated so the data flows where it needs to go, when it needs to go there. Azure Data Factory, Logic Apps, and Power Automate are the primary integration tools.

02 — Analytics

Operational dashboards and reporting

Replace static spreadsheet reports with live operational dashboards built on Power BI and Azure. Revenue leakage visibility, care delivery vs billed reconciliation, staff utilisation, compliance event tracking, and referral pipeline analytics — designed for the governance and reporting contexts your organisation operates in. Every dashboard is designed to be defensible in a board or audit context.

03 — AI Models

Anomaly detection and predictive modelling

Go beyond what dashboards can show — surface patterns that static reporting can't detect. Revenue leakage anomaly detection, care plan mismatch identification, predictive demand modelling, and risk scoring for operational and compliance purposes. All models are interpretable: every flag traces back to a data signal, not a model confidence score with no explanation.

04 — Governed AI

AI governance and explainability

For organisations deploying AI in regulated contexts, governance of the AI layer is as important as the AI itself. Taidotech builds explainability, audit trails, human-in-the-loop review workflows, and model versioning into every AI deployment. Model changes follow a controlled process — tested in non-production, reviewed, and deployed with rollback capability.

Technology

Microsoft Azure, built for regulated Australian environments.

Every Taidotech AI + Data solution is built on Microsoft Azure — specifically the services that support regulated industries in Australia: data sovereignty, enterprise security, and the governance controls that regulated sector clients require.

Azure AI Foundry
Model management, evaluation, deployment, and monitoring for enterprise AI.
Azure Data Factory
Data integration and pipeline orchestration across on-premises and cloud sources.
Microsoft Fabric / Power BI
Unified analytics platform — from data lake to dashboard in a single governed environment.
Azure Machine Learning
Model training, deployment, and monitoring with experiment tracking and version control.
AlayaCare API
Direct integration with AlayaCare for aged care and NDIS operational data — read and write patterns.
Microsoft 365 / Graph
SharePoint, Teams, Excel, and the broader Microsoft 365 data estate as integration targets.

Use cases

What AI + Data looks like in practice.

Representative examples from regulated service environments. Every engagement starts with discovery — the use cases below illustrate what's possible, not what's prescribed.

Aged Care
Revenue leakage detection
Problem: Co-contribution billing complexity and home care package variations mean delivered care frequently differs from what's billed — creating revenue risk that's invisible in standard reporting.
Solution: Anomaly detection model comparing scheduled, delivered, and billed hours across care types — flagging discrepancies daily for finance and operations review.
Aged Care / NDIS
Care plan mismatch identification
Problem: Care plans in AlayaCare don't always reflect what's actually being delivered — creating clinical governance risk and compliance exposure that manual audit can't surface at scale.
Solution: Automated comparison of care plan vs delivered service records, surfacing mismatches for clinical review — replacing a manual spot-check process with systematic coverage.
Healthcare
Appointment demand forecasting
Problem: Appointment scheduling relies on experience-based rules that don't reflect seasonal patterns, referral trends, or cohort-specific demand — creating capacity mismatches and wait time blowouts.
Solution: Predictive demand model built on historical appointment and referral data — providing 4-week rolling forecasts by practitioner, service type, and cohort.
Financial Services
Claims anomaly detection
Problem: Claims patterns that indicate errors, fraud, or process failures are buried in volumes that manual review can't keep pace with — and surface too late for effective intervention.
Solution: Unsupervised anomaly detection model on claims data — flagging outliers by provider, claim type, and pattern for daily review by the compliance team.

What operational question do you most need answered?

That's usually the right starting point. Tell us what you can't currently see in your data — or what you see but can't act on fast enough. We'll tell you honestly whether we can help.

Our position
“The goal isn't more data. It's fewer decisions made without the right information at the right time.”
Taidotech — on operational intelligence