Transform Data Scarcity into a Competitive Advantage with Synthetic Data Consulting Services
Stop watching AI initiatives stall because your data is inaccessible, limited, or too sensitive for use. Get statistically accurate, privacy-safe synthetic datasets that your teams can use for training, testing, and deployment right now.
Australian enterprises
trust us · 4.9/5 rating
Trusted by teams at
50%
30%
$1 T
20-30%
- ◆ The Problems
Why Businesses Need Synthetic Data Consulting to Overcome Critical Data Challenges
Six costly realities holding Australian businesses back from real AI results — and why most companies don’t catch them until the budget is already gone.
Problem 01
1988
Sensitive Data Limits Testing
According to the Privacy Act of 1988, development and quality assurance teams cannot test systems using real customer data without causing significant exposure. In the absence of synthetic data, teams face significant safety risks (when utilizing unmasked production data), compliance violations, or brittle testing (because of missed edge cases and data restrictions).
Problem 02
60%
Scarce or Imbalanced Data
Insufficient or unbalanced data in AI cause serious model bias, overfitting, and bogus accuracy metrics. In the absence of synthetic data to balance datasets, models are unable to identify underrepresented groups, memorize the majority class, and have poor generalization to real-world situations.
Problem 03
63%
Privacy and Compliance Risk
63% of companies that have a security breach either don’t have AI governance policies in place or are only developing them. During the development of AI, many organizations unintentionally violate these commitments. Data privacy risk builds up in every pipeline without synthetic data consultation, and the financial and reputational costs of a single breach significantly outweigh the costs of preventing it.
Problem 04
88%
Slow Access to Real Data
Accessing production data for software development or AI model training in large organizations may require weeks of legal review, anonymization, and permission. This bottleneck irritates technical teams that could otherwise work quickly and slows delivery schedules.
Problem 05
$12.9 M
Not Enough AI Training Data
Large, varied, and representative datasets are necessary for machine learning models to perform well in generalization. The majority of organizations, particularly in regulated industries, simply lack the real-world data necessary or have poor data quality to train production-grade AI models. Organizations lose an average of $12.9 million a year as a result of poor data quality.
Problem 06
2×
Hard to Share Data Safely
Without significant data masking or anonymization, it is difficult to share real-world data safely. In the absence of synthetic data consultancy, organizations either share data in a dangerous way or don’t share it at all, which causes major issues for AI development and product delivery later on.
- ◆ Our Services
Synthetic Data Consulting Services That Unblock Your AI and Development Pipelines
Six specialist capabilities to move from AI ambition to measurable business outcomes — aligned to Australian compliance and built around your objectives.
Synthetic Data Strategy
Get a well-defined synthetic data plan based on your current data infrastructure, compliance requirements, and AI roadmap. Determine which datasets require synthetic substitutes, link generating priorities to company objectives, and create governance procedures that comply with regulatory inspection right now.
- Business-aligned AI strategy framework
- Measurable goal-setting and KPI definition
- Revenue and efficiency impact modelling
- Long-term value creation roadmap
Privacy-Safe Data Generation
Use generative AI techniques, such as GANs, variational autoencoders, and agent-based simulation, to create privacy-safe data that mimics the statistical characteristics of your real-world data. Implement data creation pipelines that allow development teams to work at full speed while adhering to your responsibilities.
- Current-state data and systems audit
- Team capability and skills gap analysis
- Infrastructure readiness evaluation
- Prioritised remediation recommendations
Test Data for Development
Deliver realistic, organized synthetic test data straight to your QA and development environments. Replace sluggish, hazardous access to production records with on-demand datasets that reflect the complexities of real data. Reduce compliance exposure at every layer of your software delivery process and remove testing bottlenecks.
- Prioritised AI initiative backlog
- Phased delivery plan with success metrics
- Budget and resource allocation guidance
- Ownership and accountability framework
Training Data Augmentation
Add synthetic records to your current datasets to address class imbalance, add rare-event scenarios, and increase diversity in operational and demographic characteristics. Implement training data augmentation pipelines to enhance model generalization, lessen overfitting, and provide your machine learning teams with quantity and diversity.
- Privacy Act 1988 and APRA alignment
- Voluntary AI Safety Standard compliance
- AI ethics and risk management protocols
- Scalable, defensible deployment design
Data Anonymisation and Masking
Use workflows for structured data masking and anonymization to lower the risk of re-identification in your analytics, AI, and reporting systems. Use tokenization, format-preserving encryption, and field-level masking techniques in conjunction with synthetic replacement procedures. Ensure that your data satisfies privacy requirements with relevant Australian law.
- LLM selection and evaluation framework
- Responsible deployment guidelines
- RAG and fine-tuning strategy
- Competitive differentiation through Gen AI
Synthetic Data Validation
Before starting any model training or system testing, validate synthetic datasets against your real-world data to ensure statistical fidelity, coverage, and usability. Establish repeatable quality standards, such as utility scoring, correlation preservation, and distributional similarity, so that your AI models inherit accuracy rather than noise.
- AI team structure and roles definition
- Governance routines and cadences
- Fundamental capability building
- Confident, consistent scale-up
- ◆ Take the first step
Stop Letting Data Constraints Block Your AI Progress
Reach out to Intelinova to connect with a specialized synthetic data consulting partner and get a partner based on your industry, compliance environment, and AI goals.
◆ How it works
How Our Synthetic Data Consulting Engagement Works
A structured three-phase process designed to move you from uncertainty to a clear, compliant, and executable AI strategy — without wasted time or budget.
Free Expert Consultation
A 30-minute senior-led call to understand your business goals, current AI maturity, and where the biggest opportunities exist. No vendor pitch — just honest, qualified assessment.
AI Strategy Development
Senior consultants build a bespoke, business-aligned AI strategy with clear objectives, measurable KPIs, and a realistic investment profile tailored to your Australian market context.
Governance Framework Design
Design a compliance-ready AI governance structure aligned to the Privacy Act 1988, Voluntary AI Safety Standard, and APRA guidelines — so every deployment is defensible from day one.
AI Roadmap Planning
A prioritised, phased AI roadmap with defined delivery milestones, success metrics, ownership assignments, and budget guidance — cutting low-value work and focusing resources where impact is highest.
Operating Model & Handover
Define your AI operating model — team structures, governance cadences, and capability-building plans. Our partners stay engaged through implementation advisory to ensure strategy becomes measurable reality.
- ◆ Industries
Industries We Serve With Synthetic Data Consulting
Healthcare and Medical Research
Synthetic datasets allow researchers to refine models and evaluate algorithms without disclosing actual health records. Synthetic data is mainly being used in silico clinical trials and AI model training. .
Financial Services
Synthetic data is used by banks and fintech firms to evaluate credit risk algorithms, train fraud detection models, and perform regulatory compliance simulations. When real transaction data cannot be exchanged between teams or jurisdictions, synthetic data is becoming widely acknowledged as a useful tool for financial model validation.
Insurance
Insurance companies are increasingly using synthetic data to safely train AI models, overcome the lack of historical data, and get rid of algorithmic bias. Insurance companies utilize it for rigorous software testing, model building, and privacy-safe research because it replicates real-world trends without using Personally Identifiable Information (PII).
Automotive and Autonomous Systems
The development of autonomous vehicles depends on massive amounts of edge-case driving data, which are situations that are risky, costly, or uncommon to record in the actual world. Automotive AI teams regularly employ synthetic data consultancy to create camera imagery, LIDAR point clouds, and simulated sensor feeds.
Government and Defence
Without disclosing classified or personally identifiable information, government agencies and defense organizations employ synthetic data to test decision-support systems, run policy simulations, and train AI models on sensitive operational situations.
Technology and SaaS
Software firms and SaaS platforms employ synthetic test data to execute load tests, speed up QA cycles, and validate product features across a variety of user situations, without using actual customer data. Synthetic datasets are used by development teams to train embedded AI features, test edge situations, and populate staging environments.
- ◆ Why choose us
Why Choose Intelinova for Synthetic Data Consulting
Five concrete reasons Australian businesses choose our partner network to deliver real AI strategy outcomes — not expensive advice that goes nowhere.
01 · Partner Network
Partner-Led Delivery Model
Intelinova does not directly provide consultancy services for synthetic data. Rather, we link Australian companies with a pre-screened network of expert synthetic data consulting partners who are evaluated for their technical proficiency, topic expertise, and delivery history.
02 · Compliance
Privacy and Compliance First
From the beginning, each partner in the Intelinova network incorporates data protection and compliance into their synthetic data engagements. Our partners adhere to the Privacy Act 1988 and the Australian Privacy Principles, making sure that the creation of synthetic data strengthens your compliance position.
03 · Execution
Multiple Generation Methods
Our network includes partners who have practical experience with all synthetic data creation approaches, including GANs, variational autoencoders, agent-based simulation, statistical modeling, and rule-based generation. Your organization will receive the solution that best suits your data type, use case, and quality requirements.
04 · Senior Talent
Statistical Quality Validation
Every synthetic dataset produced by Intelinova network partners is subjected to stringent validation procedures. Instead of datasets created and sent without any confirmation of how accurately they represent your real-world data, you receive synthetic data with documented quality assurance.
05 · Free Access
Works With Your Data
Instead of making you rebuild or replace what you already have, Intelinova partners work with your current data infrastructure, governance frameworks, and tools. The engagement is made to operate with your current stack and generate synthetic datasets that fit right into your processes
04 · Senior Talent
Australian-Based Oversight
Intelinova oversees all synthetic data consulting engagements from their Australian basis. Our team remains active from initial scoping to ensure that the engagement continues on schedule, within scope, and aligned with the business targets you outlined at the outset.
◆ What clients say
Australian Enterprises That Stopped Wasting Spend on AI.
Measurable ROI from enterprises across Australia who moved AI from stalled pilots into production-grade business systems.
Chief Executive Officer
- Australian Financial Services Group
$4.2M
Projected first-year ROI
from approved AI strategy
The Privacy Act and APRA compliance piece alone was worth the engagement. Our internal team had no idea what AI governance exposure we had. Our Intelinova partner built it into the strategy architecture from day one — not as an afterthought.
VP of Legal & Compliance
- Sydney FinTech Firm
I expected a 90-day assessment that led to nothing actionable. Instead we had a full AI roadmap with phased priorities, ownership, and success metrics in ten weeks. That kind of structured thinking with senior-level delivery is rare in this space.
Head of Digital Transformation
- Australian Mining Corporation
As a healthcare organisation we have strict data requirements. Every AI strategy vendor we'd spoken to glossed over compliance. Our Intelinova partner built the Voluntary AI Safety Standard requirements into the framework before we touched a single system.
Chief Medical Information Officer
- Melbourne Hospital Network
We're a 120-person manufacturing business — not a tech giant. Intelinova scoped the engagement right for our size, delivered senior expertise without enterprise pricing, and the AI operating model is saving us 35 hours of management time every week.
Chief Operations Officer
- Brisbane Manufacturing Group
- ◆ Take the lead
Your Data Challenges Have a Solution. Let's Build It.
Tell us about your data difficulty, and Intelinova will connect you with the ideal synthetic data consulting partner for your industry, infrastructure, and privacy needs.
- Comprehensive AI roadmap
- Australian compliance advice
- Senior CTO/CXO consultants
- Free · No obligation · 24hr response
◆ Questions
Frequently Asked Questions About Synthetic Data Consulting
Common questions from Australian business leaders before their first strategy call.
What is synthetic data consulting actually used for in real business contexts?
One of the most frequent obstacles to AI development and software delivery is the discrepancy between the data that organizations require and the data that they can securely access. This is addressed by synthetic data consulting. In practice, this entails producing statistically representative training datasets in situations where real-world data is too limited, too sensitive, or too unbalanced to be used directly. Generating privacy-safe patient records for healthcare AI training, building edge-case test data for software quality assurance, and developing synthetic transaction datasets for fraud model development. Additionally, companies employ synthetic data to facilitate cross-team cooperation on AI projects in situations where sharing real data is prohibited by contracts or privacy laws.
Is synthetic data genuinely private, and does it satisfy Australian privacy obligations?
Properly generated synthetic data is made by studying the statistical characteristics of a genuine dataset and creating new records that mimic those patterns without relating to any real person. It does not contain any information about any real human. When properly created, synthetic data removes the possibility of re-identification and does not qualify as personal information under the Australian Privacy Principles or the Privacy Act of 1988. But procedure and quality are very important. Identifiable patterns from the original dataset may be preserved in poorly produced synthetic data. For this reason, any credible synthetic data consulting engagement must include validation and expert monitoring.
How realistic is synthetic data compared to real-world data?
The statistical qualities, distributional features, and feature correlations of real-world data can be closely replicated in datasets created using modern synthetic data generation techniques, especially GANs and variational autoencoders. High-quality synthetic data performs similarly to actual data for the majority of AI training and software testing applications. This is explicitly measured by validation frameworks using downstream model performance benchmarks, correlation matrix comparisons, and distributional similarity scores. As part of the engagement, a professional synthetic data consulting partner oversees the generation process, the quality of the source data used to train the generator, and the rigor of post-generation validation, all of which have a significant impact on the realism of synthetic data.
Can synthetic data improve our machine learning model training outcomes?
Yes, and in a number of significant ways. By producing more instances of underrepresented events, such as fraud cases, equipment malfunctions, or uncommon diagnoses, training data augmentation with synthetic datasets corrects class imbalance. Additionally, it improves model generalization by introducing variety across operational and demographic variables that could be underrepresented in real-world data. Additionally, it enables teams to evaluate model behavior against stress situations and edge instances that are uncommon in real data. Increased model robustness and more dependable performance when applied to real-world inputs are regularly reported by organizations that employ training data augmentation through synthetic datasets.
How do you validate synthetic data quality before it is used in production?
Before any synthetic dataset is utilized for system testing or model training, reputable synthetic data consulting partners implement multi-layered validation frameworks. In addition to correlation preservation, feature coverage, and utility testing, which evaluates how well models trained on synthetic data perform against real-world benchmarks, validation usually includes distributional similarity, which compares statistical distributions between real and synthetic datasets. To ensure that no actual records can be retrieved or deduced from the synthetic output, partners also conduct privacy audits. Your team can utilize the written quality assurance report as proof of data fitness prior to any subsequent use.
What does a typical synthetic data consulting engagement cost in Australia?
The complexity of your data environment, the generation techniques needed, and the quantity of synthetic datasets required determine the scope and cost of synthetic data consulting engagements. The normal price range for scoping and strategy engagements, in which a partner evaluates your data infrastructure and establishes a generation approach, is between $15,000 to $30,000. Depending on scale, end-to-end synthetic data creation projects, which include pipeline building, validation, and connection with your current systems, often cost between $40,000 to $150,000. Retainers for ongoing synthetic data creation are scoped uniquely for companies with ongoing development or AI training requirements. Before making any commitments, Intelinova’s matching process incorporates an initial scoping discussion to assist you understand what an engagement actually entails.
- ◆ Ready to move
Stop Waiting and Take the Lead With AI Strategy.
Speak with a professional AI strategy consultant right now. Get a comprehensive AI roadmap, useful compliance advice, and a strategy created especially for Australian companies.
- Privacy Act 1988
- AU AI Safety Standard
- ISO 42001 Ready
Get matched with the right partner
Free 30-minute scoping call. No vendor pitch. Just honest guidance on where AI fits your Australian business.
We'd burned 18 months evaluating AI vendors who couldn't tell us what ROI looked like. Intelinova matched us with a partner who had direct experience in our vertical. Eight weeks later we had a working strategy, a compliance framework, and an execution roadmap that our board actually approved.