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Digital Transformation

ArchiTechs drives digital transformation by combining strategy, AI, and modern platforms to build scalable, future-ready capabilities, enhancing customer experience and accelerating outcomes.
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/Introduction

Digital Transformation That’s Strategic, Scalable, and Human-Centric

We drive digital transformation with an architecture-first approach—delivering intelligent platforms, GenAI-led automation, modern customer experiences, and cloud-enabled operating models. 



We bring the deep subject matter expertise, speed, and scale to turn your digital strategy into a lasting impact.

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Our Digital Transformation services are designed to future-proof your organisation—aligning technology, people, and process with strategic ambition. 



We help clients assess digital maturity, craft bold digital strategies, and redesign operating models to be agile, data-driven, and AI-enabled.

Enterprise Strategy Alignment
Enterprise Strategy Alignment
Align business goals with technology and data initiatives. Identify growth opportunities through AI and digital transformation.
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IT Strategy & Transformation
IT Strategy & Transformation
Define future-state IT capabilities to support innovation. Prioritise technology investments and develop modern IT operating models.
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Data & AI Strategy
Data & AI Strategy
Develop enterprise-wide data and AI roadmaps. Identify high-value AI use cases, assess readiness, and define governance and monetisation strategies.
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Platform Strategy
Platform Strategy
Evaluate and select modern data and AI platforms (e.g., Azure, AWS, Databricks). Define platform governance, scalability, and integration plans.
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Cloud Strategy
Cloud Strategy
Design hybrid or multi-cloud strategies. Assess infrastructure readiness for AI, high-performance computing, and edge workloads.
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Digital Operating Model
Digital Operating Model
Redesign organisational structures, roles, and capabilities for AI adoption. Define Agile, product-led, and DevOps-based delivery models.
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Innovation Strategy
Innovation Strategy
Explore opportunities with GenAI, edge AI, quantum computing, and more. Build innovation pipelines and prioritised use case portfolios.
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Capability & Skills Strategy
Capability & Skills Strategy
Identify skill gaps in data science, ML engineering, and AI ethics. Design learning pathways and talent acquisition strategies.
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Governance, Risk & Compliance Strategy
GRC Strategy
Develop Governance, Risk & Compliance (GRC), and ethical AI governance models. Align with and compliance frameworks like ISO 42001 (AI Management), GDPR, and Australian privacy laws.
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Ecosystem & Partner Strategy
Ecosystem & Partner Strategy
Build strategic partnerships with hyperscalers, AI startups, and academia. Enable co-innovation and joint go-to-market models.
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EA Strategy & Operating Model
EA Strategy & Operating Model
We define the purpose, scope, and governance of enterprise architecture based on guiding architecture principles and customized frameworks like TOGAF.
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Business Architecture
Business Architecture
We map business capabilities, value streams, and operating models to strategic business priorities, ensuring a clear link between operational execution and long-term objectives.
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Data & AI Architecture
Data & AI Architecture
We design modern, scalable, and secure data architectures that enable trusted insights and responsible innovation. This includes:
  • Enterprise data platforms and pipelines
  • Model governance and MLOps/AIOps
  • Architectures that are fast, flexible, and future-ready, unlocking enterprise data value at speed

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Solution Architecture
Solution Architecture
We design end-to-end solution blueprints aligned with enterprise, domain, and program architectures. Each solution is tailored to conform with enterprise standards and promote reuse and consistency.
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Roadmap & Portfolio Planning
Roadmap & Portfolio Planning
We develop strategic transformation roadmaps and investment portfolios based on:
  • Organisational maturity
  • Business priorities
  • Risk appetite

This phased planning approach ensures sustainable transformation and measurable value.

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EA Governance & Compliance
EA Governance & Compliance
We define target operating models (TOMs) for architecture governance, including:
  • Architecture review boards
  • Compliance and exception handling processes

This ensures architectural integrity and alignment with enterprise policies

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Integration Architecture
Integration Architecture
We design robust API, microservices, and middleware architectures to enable seamless system interoperability, ensuring flexibility, reusability, and future scalability.
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EA Tool & Repository Management
EA Tool & Repository Management
We define a customized EA metamodel, select and implement tools like LeanIX, Avolution ABACUS and Mega etc.
We also define standards and best practices for repository governance.
 Alternatively, all of the above can be delivered through our Enterprise Architecture-as-a-Service (EAaaS) model.
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Data & AI Strategy
Data & AI Strategy
We help you define clear, actionable strategies aligned with your business goals— prioritising use cases, architecture, governance, and required investments. Our approach  bridges vision and execution, accelerating your path to AI readiness with measurable outcomes.
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Innovation & AI Labs
Innovation & AI Labs
We help establish innovation pods or AI labs to continuously experiment with new AI technologies, GenAI models, and value-creation prototypes under a skunkworks model.
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Data & AI Architecture
Data & AI Architecture
Design future-proof, scalable data ecosystems including data lakes, data mesh, and unified platforms for AI and analytics workloads.

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Data Platform Modernisation
Data Platform Modernisation
Migrate from legacy systems to modern cloud-based data platforms (e.g. Azure, AWS, GCP, Snowflake, Databricks) using scalable and secure architectures.
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Customised AI/ML & GenAI Solutions
Customised AI/ML & GenAI Solutions
Design, train, and deploy AI/ML models—including GenAI solutions for customer service, automation, summarisation, and content generation.

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DataOps, MLOps & LLMOps Enablement
DataOps, MLOps & LLMOps Enablement
Implement reliable and scalable AI delivery through lifecycle management, CI/CD pipelines, automated testing, monitoring, retraining, version control, and responsible AI practices across DataOps, MLOps, and LLMOps.
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Data Engineering & Integration
Data Engineering & Integration
Develop robust ETL/ELT pipelines and enable real-time and batch processing while integrating diverse data sources across the enterprise to support analytics and AI workloads.
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Advanced Analytics & BI
Advanced Analytics & BI
Unlock insights through descriptive, diagnostic, predictive, and prescriptive analytics using modern BI tools such as Power BI, Tableau, and Looker.
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Data Governance & Compliance
Data Governance & Compliance
Implement a customised data governance framework covering policies, ownership models, data quality, and compliance with regulations including GDPR, APRA, and HIPAA.
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Data & AI Talent Enablement
Data & AI Talent Enablement
We upskill client teams through training, establish AI Centres of Excellence (CoE), and embed data literacy across business units.
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Digital Strategy
Digital Strategy
We assess your current digital maturity, identify capability gaps, benchmark against industry standards, and define a clear digital vision, strategic goals, and transformation roadmap aligned with your business objectives.
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Customer Experience Transformation
Customer Experience Transformation
Reimagine customer journeys using design thinking, data, and AI to deliver seamless, personalised, and superior omnichannel experiences across digital and physical touchpoints.
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Operating Model Redesign
Operating Model Redesign
Redesign your operating model by transforming organisational structures, roles, processes, and governance to support agility, innovation, and a phased transition to your Target Operating Model (TOM).

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Digital Technology Enablement
Digital Technology Enablement
Identify and implement emerging technologies—including GenAI, IoT, RPA, and blockchain—to accelerate your digital transformation with scalable, cloud-native solutions.
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Data Mesh & Data Product Models
Data Mesh & Data Product Models
Transition to decentralised, domain-oriented data architectures using data mesh principles and product-led delivery models for improved agility, faster time-to-value, and scalable innovation.

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Data-Driven Enterprise
Data-Driven Enterprise
Enable enterprise-wide, data-driven decision-making by building modern data platforms, analytics capabilities, and robust governance frameworks.
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AI-Enabled Automation
AI-Enabled Automation
Use our AI Opportunity Radar and design thinking frameworks to identify, prioritise, and implement AI, ML, and RPA initiatives that automate value chains, reduce operational costs, and increase productivity.
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Cloud Transformation
Cloud Transformation
Accelerate your cloud journey by modernising legacy infrastructure, adopting cloud-native architectures, and optimising cost, performance, and scalability across leading cloud platforms.
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Measurement & Value Realisation
Measurement & Value Realisation
Establish robust KPIs, track digital ROI, and implement performance measurement frameworks to ensure sustained value delivery and continuous transformation improvement.
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Talent Strategy & Capability Planning
Talent Strategy & Capability Planning
We assess current workforce capabilities, identify skill gaps, and co-develop talent strategies and workforce plans. We also enable targeted upskilling and training to build high-performing teams aligned to future needs.
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Flexible Resourcing Models
Flexible Resourcing Models
Access scalable, on-demand talent for short-term initiatives, agile sprints, or continuous delivery. We provide individuals, pods, or full teams tailored to your specific resourcing needs and delivery rhythms.
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Role-Based & Specialist Augmentation
Role-Based & Specialist Augmentation
Quickly address critical skill gaps with pre-vetted experts—including Data Scientists, AI Engineers, Enterprise Architects, DevOps Engineers, Scrum Masters, and more—ready to integrate and deliver from day one.

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Embedded & Dedicated Teams
Embedded & Dedicated Teams
Deploy dedicated teams embedded within your organisation, aligned to your goals, culture, and delivery methodologies. Ideal for long-term programmes, complex initiatives, or IP-sensitive engagements.
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Global Delivery & Nearshore Models
Global Delivery & Nearshore Models
Optimise delivery with flexible nearshore and offshore models that leverage our global talent network, delivery hubs, and robust governance for cost-efficiency and round-the-clock productivity.

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Governance, Onboarding & Performance
Governance, Onboarding & Performance
Ensure quality and accountability through comprehensive support—including structured onboarding, compliance, knowledge transfer, SLA management, and ongoing performance governance.
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Our Strategy Methodology

Our methodologies blend proven enterprise frameworks with modern agile, data-driven, and AI-enabled approaches to deliver measurable impact—faster, smarter, and at scale.
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Discover & Diagnose
/Step 6
Monitor & Refine
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Prioritise & Roadmap
/Step 1
Initiate & Align
/Step 3
Strategise & Design
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Mobilize & Enable

Our Enterprise Architecture Methodology

Our EA method integrates the architecture definition levels (from strategic architecture to detailed design). This approach aligns each lifecycle phase with the architecture granularity it addresses.
EA Value Chain
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Strategic Architecture
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Domain/Segment Architecture
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Capability Architecture
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Solution Architecture
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Detailed Design
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Our methodologies blend proven enterprise frameworks with modern agile, data-driven, and AI-enabled approaches to deliver measurable impact—faster, smarter, and at scale.
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Level
Scope
Detail
Impact
Audience
Strategic Architecture
Domain/Segment Architecture
Capability Architecture
Solution Architecture
Detailed Design
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Enterprise
Line of Business
Business Function
Process
System / Application
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Low

(Principles & Objectives)
Medium

(Capability Mapping)
Medium
(Capability Models & Roadmap)
High

(Solution & Tech Stack)
Very High

(Tech Specs & Configs)
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Strategic Outcomes
Business Outcomes
Capability Outcomes
Operational Outcomes
Implementation Outcomes
Enterprise
Business / Product Owners
Capability Owners / Enterprise Architects
Users, Solution Designers & DevOps
Technical Implementers / Testing Teams
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/Step 2
Current State
/Step 6
Solution Alignment
/Step 4
Gap & Roadmap
/Step 1
Strategy & Scope
/Step 3
Target Architecture
/Step 7
Value & Optimisation
/Step 5
Governance & Authority
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/Step 1
Strategy & Scope
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/Step 2
Current State
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/Step 4
Gap & Roadmap
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Governance & Authority
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Solution Alignment
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/Step 7
Value & Optimisation

AI Services Lifecycle

The AI lifecycle is a structured process for building, deploying, and managing AI systems, similar to SDLCor Agile frameworks. However, AI projects are data-driven, iterative, and experiment-intensive, requiring specific stages beyond traditional development.

ArchiTechs’ typical AI lifecycle incorporates the following distinct phases:
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Opportunity Radar
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • Data Discovery & Collection
  • Discover internal and external data sources
  • Collect structured & unstructured data (e.g., logs, APIs, sensors)
  • Ensure privacy, consent, and compliance (GDPR, HIPAA, etc.)
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • Data Wrangling & Engineering
  • Clean, label, and structure data
  • Engineer features and split into train/validation/test sets
  • Handle missing values, class imbalance, noise, and outliers
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • Model Development (Experimentation)
  • Select algorithms (classification, regression, etc.)
  • Train multiple models using frameworks (e.g., notebooks, MLFlow)
  • Tune hyperparameters and evaluate (accuracy, recall, F1-score)
  • Rapid iteration based on results
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • Model Deployment
  • Deploy via APIs, containers, or edge devices
  • Use CI/CD pipelines and MLOps tools (e.g., SageMaker, Azure ML)
  • Choose inference mode: batch, real-time, or streaming
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • Monitoring & Feedback Loops
  • Monitor performance: accuracy decay, drift, latency, cost
  • Detect data/model drift and anomalies
  • Collect real-world feedback for future improvements
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • Retraining & Lifecycle Management
  • Use new data to retrain models
  • Automate retraining pipelines as needed
  • Maintain model registry: version control, rollback, retirement
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
  • Define the business objective and determine if AI is the right fit.
  • Align with KPIs or outcomes (e.g., reduce churn, optimise pricing).
  • Identify constraints (ethical, legal, operational).
    • /Case Studies
    Right Arrow
    View All Case Studies
    Image of an Airplane
    /Aerospace & Defence

    Paying Down the Technology Debt

    Legacy systems were grounding progress. We streamlined architectures, modernised platforms, and brought agility back to mission-critical operations.
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    Discover How We Modernised Securely
    Read Full Case Study
    Airplane tail with Qantas kangaroo logo
    /Airlines

    Institutionalising Analytics, One Warehouse at a Time

    We unified fragmented data silos into a streamlined enterprise data warehouse, driving significant cost savings and embedding analytics into everyday operations. From insights to action—this transformation elevated decision-making across the airline.
    Discover How Analytics Took Flight
    Image of BFSI with finger
    /Financial Services

    Compliance at Speed, APRA at Bay

    We helped our client move fast without breaking the rules— automating reporting, aligning with APRA, and building compliance into the core.
    ‍
    See Compliance Become a Competitive Edge
    Read Full Case Study
    Image of NSW government
    /Government

    Resurrecting Enterprise Architecture After Multiple Failed Attempts

    We successfully re-established a high-impact Enterprise Architecture function for a NSW government agency. From governance forums to architecture boards, we built the foundations that now steer strategic technology decisions confidently and clearly.
    Explore the Full Story
    Image of a plus
    /Health

    Revolutionising Intelligence with LLMs, RAG, Agentic AI and Data Lakehouse

    We redefined healthcare intelligence by implementing a robust data lakehouse foundation and deploying customised LLMs with RAG and agentic frameworks. The result? Accelerated innovation, smarter insights, and measurable revenue and patient engagement growth.
    See How Ai is Transforming Care
    Image of doctor with patient
    /Healthcare

    Enhancing Patient Outcomes with GenAI-Driven Clinical Insights

    A leading healthcare provider in Australia sought to modernise its fragmented digital ecosystem, reduce operational inefficiencies, and unlock the potential of data and AI for improved patient outcomes. The organisation faced rising costs, siloed data systems, and compliance pressures, with limited ROI from prior technology investments.
    Explore the Full Case Study
    Image of industry 4.0
    /Manufacturing

    Simplifying Complexity Through 10+ ERPs Consolidation

    Thirteen fragmented systems, three unified platforms. A digital transformation that cut through complexity, boosted efficiency, and enabled smart manufacturing.
    Explore the Transformation
    Futuristic image of buildings
    /Real Estate & Property

    AI That Builds Value

    From asset optimisation to predictive leasing, we embedded AI across the property lifecycle—unlocking new revenue streams and tenant satisfaction.
    See How Intelligence Shapes Value
    Image of cart
    /Retail

    Personalisation at Scale

    One-size-fits-all was out. We enabled real-time customer insights, intelligent targeting, and experiences that drove loyalty and lifetime value.
    Learn How We Scaled Relevance
    Image of a space
    /Telecom

    360° customer view, Insights Delivered & Revenue Secured

    We helped a major telecom break down data silos and unlock a 360° view of its customers, powering real-time insights for 3,600+ BI users. The result? Accurate revenue assurance, smarter decision-making, and a data foundation built to scale.
    Image of a man showing Light
    /Utilities

    From BI to AI: Powering the Next Energy Frontier

    We partnered with a leading energy provider to evolve their legacy BI systems into a modern AI-driven analytics platform. This transformation unlocked predictive insights, automated operations, and laid the groundwork for a smarter, more sustainable energy future.



    See How We Turned Failure Into a Model of Success!
    Read Full Case Study
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    Our Technology
    Partners

    We leverage leading EA technology platforms to deliver structured, scalable, and insight-driven architecture across business, data, application, and technology domains.
    logo of docusignlogo of jiraLogo of trelloLogo of confluenceLogo of servicenowLogo of Microsoft teamLogo of slackLogo of greenhouseLogo of Codility_Logo of TestGorillaLogo of HipeopleLogo of HarvestLogo of deal.
    logo of docusignlogo of jiraLogo of trelloLogo of confluenceLogo of servicenowLogo of Microsoft teamLogo of slackLogo of greenhouseLogo of Codility_Logo of TestGorillaLogo of HipeopleLogo of HarvestLogo of deal.

    Our Technology
    Partners

    We leverage leading EA technology platforms to deliver structured, scalable, and insight-driven architecture across business, data, application, and technology domains.
    Image of LeanIXImage of avolutionImage of Bizzdesign Image of Mega
    Image of LeanIXImage of avolutionImage of Bizzdesign Image of Mega

    Our Tech Stack

    Battle-tested tools. Future-ready platforms. We work across the most powerful and enterprise-trusted technology ecosystems—ensuring scalability, innovation, and seamless integration at every layer of your data and AI journey.

    Cloud Platforms

    Built to scale with enterprise cloud leaders.
    Azure LogoAWS LogoGoogle Cloud Image

    AI & GenAI Ecosystem

    Leading the way in enterprise AI, LLMOps, and generative innovation.
    Icon Of ChatgptImage of google vertexImage of salseforceImage of snoflakeImage of Anthrop\cImage of logchainIamge of Hug imoji

    RAG (Retrieval-Augmented Generation) Frameworks

    Enterprise-ready knowledge retrieval and grounding.
    Image of logchainImageLogoImage of stock

     ML Frameworks & Libraries

    Model lifecycle orchestration from lab to production.
    Image of learnA logo of tensorflowLogo of mlflowImage of AirflowLogo of MLX

    Data Platforms & Pipelines

    Foundations for trusted, scalable, real-time insights.
    An Image of Data BricksImage of snoflakeAn image of Microsoft fabricAn Image of Google Nig QueryImage of Delta lakeAn Image of Airbyte

    BI & Visualisation

    Bringing data stories to life.
    An Image of LookerAn Image of thoughtshotAn image of streamlit

    Integration & Automation

    Seamless orchestration across the enterprise.
    Image of SalseforceImage of a brand logoAn image of Kafka

    Governance & Security

    Enterprise-grade trust, lineage, and compliance.
    An image if InformaticaAn icon of EyeAn image of OktaAn Image of Hippa Compliant
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