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Build compliant AI systems faster with synthetic data consulting services that support AI model training, testing, privacy protection, regulatory compliance, and scalable machine learning development.
IBM states that if datasets are inaccurate or biased, this can have a big effect on model accuracy and fairness. Synthetic data consulting enables organizations to build well-rounded data sets, enhance the representation of rare instances, and minimize model bias during the creation of their machine learning systems.
By 2026, 75% of companies will rely on generative AI to create synthetic customer data, Gartner predicts, up from less than 5% in 2023. Not all organizations have access to suitable datasets that comply with privacy regulations and are suitable for training AI models.
KPMG AI survey finds that over 61% of organizations report they do not trust AI enough for adoption. Synthetic data generation allows the development and testing teams to use realistic data while avoiding the risk of sharing sensitive information.
The GDPR framework of the European Union provides for penalties of up to €20 million or 4% of total annual turnover worldwide for serious non-compliance. Using synthetic data also lowers risk to privacy from PII and PHI.
One of the main challenges to successful AI adoption is still access to quality data and security, according to Google Cloud. Synthetic data consulting can be instrumental in addressing the issue of data scarcity by creating realistic datasets for training and validation purposes.
According to Gartner, by 2030, synthetic data will completely surpass real data in AI model training datasets. There is no clear process for determining the value of synthetic data, how it should be managed, or how to measure the quality of the data.
Understand the potential for business value in AI projects, software testing, analytics, and compliance efforts using synthetic data. Identify data needs, evaluate privacy threats, and develop a data synthesis plan that meets organizational goals and regulatory needs.
Create authentic synthetic data with tabular, unstructured, and machine learning applications. Ensure that data is accurate and balanced, while also being statistically relevant and protecting privacy to facilitate reliable AI model training and business applications.
Create frameworks to ensure synthetic data programs comply with HIPAA, GDPR, CCPA, SOC 2, and industry regulations. Enhance data Governance, and minimize privacy threats related to the management of personal and health-related data.
Generate good quality machine learning training data for AI model training, generative AI projects, fraud detection and risk management, and predictive analysis. Enhance the performance of the model with minimum reliance on sensitive production data.
Create datasets for software testing, quality assurance and development that are private. Allow teams to test apps at a quicker rate without compromising on compliance and mitigating risks associated with the usage of production data.
Adopt data anonymization, de-identification and data masking methods to ensure data security while maintaining analytical value. Enable AI development and compliance in a responsible way, while not stifling innovation or operational effectiveness.
Build compliant, high-quality datasets for AI development, testing, and analytics with synthetic data consulting services designed to accelerate innovation while reducing privacy and regulatory risks.
Use a consulting partner that will suggest the top synthetic data technologies for you and not sell you a platform. This provides flexibility, scalability, and solutions based on your business goals.
All engagements from the beginning are focused on privacy and security needs, plus regulatory requirements. Solutions are built with HIPAA, GDPR, CCPA, SOC 2 and industry-specific compliance in mind.
Match with experts with experience in dealing with industry-specific data issues in healthcare, financial services, insurance, retail, manufacturing, and technology. Having industry knowledge can speed up implementation and outcomes.
Projects are linked to the measurable business goals of faster AI deployments, better model performance, better compliance, fewer testing bottlenecks, and better usage of enterprise data assets.
Each consulting partner is assessed for technical skills, delivery capabilities, effectiveness in the use of synthetic data, the training of AI models, privacy-preserving data strategies and regulatory compliance programs.
Be supported by strategy and use-case assessment, from data generation, data validation, data governance, data testing, and deployment. This provides a framework for the development to run smoothly from conception to a working product.
In the healthcare sector, synthetic data consulting plays a crucial role in training AI models, conducting clinical research, medical imaging, and healthcare analytics without compromising patient privacy. Synthetic data in the healthcare market reached US$657.92M in 2025 and is projected to reach US$5.88B by 2033, and it will improve diagnostic accuracy.
Synthetic data generation is applied by banks and financial institutions in fraud detection, risk management, anti-money laundering models and machine learning development. Financial institutions are under increasing pressure to strike a balance between innovation and privacy, and privacy-focused data strategies are becoming more critical.
Synthetic data is used by insurance companies to train their underwriting models, enhance claims analysis, and bolster their risk assessment frameworks. It helps organizations test new models safely, while minimizing exposure to personally identifiable information and regulatory concerns.
Synthetic data consulting is used by technology firms to speed up product development, software testing, AI model training, and quality assurance. Over the next few years, in many enterprise scenarios, synthetic data will be the main type of data for AI model development.
Retailers use synthetic data to enhance their customer analytics, recommendation engines, demand forecasting, and personalization algorithms. With privacy-safe datasets, businesses can explore the possibilities of AI innovation with lower risks around data handling and compliance. According to research, the global synthetic data for retail market size reached USD 608 million in 2024, which highlights its importance.
Synthetic data is leveraged by manufacturers and automotive companies as a tool to train computer vision systems, predictive maintenance models, autonomous systems, and quality control applications. It is becoming an essential resource for training AI systems when it is hard to gather enough data on edge cases.
Accelerate AI development, testing, and innovation with synthetic data strategies that protect sensitive information, support compliance requirements, and improve model performance at scale.
Long-form POVs, governance frameworks, and field benchmarks on what actually works in production healthcare AI. Hover to pause.
The structure, artifacts, and review cadence that satisfies TGA SaMD requirements without slowing deployment velocity.
How to connect AI systems to your EHR without creating data silos, compliance gaps, or HL7 translation nightmares.
The model design, data pipeline, and governance framework behind a validated predictive risk deployment at a regional hospital network.
A practitioner's reference for navigating overlapping privacy obligations when deploying AI across clinical data environments.
The five most common validation gaps that surface during post-go-live TGA audits — and how to close them before deployment.
Change management, privacy disclosure, and workflow design patterns from practices that achieved 70%+ documentation time reduction.
Why 60% of CDSS deployments are bypassed within 6 months — and the alert design and workflow integration principles that reverse it.
How one imaging network deployed AI-assisted triage across 8 sites while passing ARTG review and maintaining radiologist confidence.
The structure, artifacts, and review cadence that satisfies TGA SaMD requirements without slowing deployment velocity.
How to connect AI systems to your EHR without creating data silos, compliance gaps, or HL7 translation nightmares.
The model design, data pipeline, and governance framework behind a validated predictive risk deployment at a regional hospital network.
A practitioner's reference for navigating overlapping privacy obligations when deploying AI across clinical data environments.
The five most common validation gaps that surface during post-go-live TGA audits — and how to close them before deployment.
Change management, privacy disclosure, and workflow design patterns from practices that achieved 70%+ documentation time reduction.
Why 60% of CDSS deployments are bypassed within 6 months — and the alert design and workflow integration principles that reverse it.
How one imaging network deployed AI-assisted triage across 8 sites while passing ARTG review and maintaining radiologist confidence.
Synthetic data consulting is a forward-thinking service designed to assist organizations in understanding the appropriate use of synthetic data in an AI program, software testing, analytics, and compliance programs, and where and why to apply it. It emphasizes planning, governance, implementation strategy, validation frameworks and business outcomes. Generating synthetic data is just a part of the process. Consulting can support organizations in choosing the most appropriate generation methods, assessing the quality of the data, setting compliance controls and ensuring the synthetic data is helpful for business needs. In short, generation is the data and consulting is making sure that the whole strategy is effective, compliant, and aligned to business goals.
In sectors such as healthcare, finance, and government, where data access is restricted, monitored, or sensitive, synthetic data consulting plays a crucial role. Synthetic data is employed in healthcare to facilitate AI studies and clinical analytics without compromising patient privacy. Financial institutions employ it to identify fraud, model risks, build anti-money laundering systems and as part of machine learning projects. Synthetic data is beneficial for technology companies because it allows them to carry out software testing, train their AI models, and develop their products without endangering their customers or production data.
Compliance starts with a thorough analysis of data sources, business needs, and relevant laws. A team of experts then determines the best strategy in terms of synthetic data, anonymization, de-identification or data masking for the specific application. Projects usually contain frameworks for governance, privacy impact assessment, validation process, and reduction of re-identification risks through control.
The solution varies according to the use case. In certain cases, synthetic data can substitute for much of the real data, especially in software testing and quality assurance, the generation of edge cases, and privacy-related model development. It is particularly valuable where there are compliance requirements or a lack of data to access production data. But the best results are seen in many organizations with a blend of synthetic and real-world data. Real data may be used to provide valuable patterns in behavior and/or business context, while synthetic data can increase the variety of data sets and help mitigate class imbalances and rare events.
Typical engagement starts with a discovery phase to learn about business goals, data issues, regulatory needs, and AI projects. Consultants review the current datasets and identify potential privacy issues, data quality concerns, and opportunities for using synthetic data to provide value. The following step is usually the use case prioritization, architecture planning, review for compliance, and the creation of a synthetic data strategy. After the strategy is accepted, teams assist in the creation, validation, testing, and governance of datasets and their incorporation into AI model training or software development processes. Long-term success is tracked using performance and compliance metrics throughout the project.
There are significant variations in costs depending on the complexity of the project, regulatory requirements, volume of data, industry, and scope of implementation. A focused assessment or strategy engagement may involve a relatively small investment, and enterprise-wide synthetic data programmes that are designed to support a number of AI initiatives may have bigger budgets. The level of complexity of source data, the need for validation, compliance needs, the number of business use cases, and integration into existing AI and analytics environments are all factors that affect costs.