Healthcare Synthetic Data Generation Services

Synthetic data is artificially generated data that mimics the statistical patterns of a real dataset without being a copy of any real record. In healthcare, synthetic data can take the form of synthetic patient records, tabular and claims data, time-series such as vital signs, and other formats, produced using statistical models, generative AI, or simulation. The appeal is significant: because synthetic data ideally contains no real protected health information, it can be used for AI training, software development and testing, research, demonstrations, and data sharing with far less privacy risk and regulatory friction than real data. It can also augment scarce data, such as rare conditions, and help balance datasets. Generation often uses advanced models, connecting to our healthcare AI development practice. But synthetic data is not automatically safe or useful: poorly generated data can inadvertently memorize real records, creating re-identification risk, or fail to preserve the patterns that make it useful. So responsible synthetic data generation always includes privacy validation and fidelity testing. Done well, it unlocks data-driven work while protecting patients.

Taction Software delivers synthetic data generation as a full engagement shaped to your purpose, whether you are building AI, testing software, enabling research, or sharing data safely, and whether you are a health system, a digital health or AI company, a research organization, or a life-sciences team. We define the use case and the fidelity it requires first, because the right generation approach depends entirely on what the data is for. We then build what fits: data profiling, generation models suited to the data type, privacy validation against re-identification, fidelity and utility testing, and pipelines to produce data on demand. Because synthetic data is often used to train models, we connect it to practices like healthcare MLOps. We are candid that privacy and fidelity trade off, and we tune to your requirements rather than promising both perfectly. Every engagement includes validation, because unvalidated synthetic data is a liability. The goal is synthetic data proven safe enough to use and faithful enough to be worth using.

Well-generated, validated synthetic data delivers value that locked-down real data cannot, because it decouples data-driven work from direct exposure of protected health information. The clearest benefit is safer, faster access: teams can develop, test, and demonstrate software using realistic data without provisioning real PHI, which reduces both risk and delay. For AI, synthetic data can augment scarce examples, such as rare conditions, and help balance datasets, potentially improving models. For collaboration, synthetic data can be shared with partners or vendors far more easily than real data. For research, it can enable exploratory work while protecting patients. Reducing the footprint of real PHI in non-production environments also shrinks the attack surface and compliance burden. The essential caveat is that these benefits depend on validation: only synthetic data proven private and faithful delivers them safely. That is why Taction Software treats validation as inseparable from generation, so the value is real rather than assumed.

Taction Software follows a validation-first process suited to a technology that is only as good as its proof. We begin with the use case, defining what the data is for and the fidelity that requires, because generation choices flow from purpose. We profile the real data securely to capture the patterns to reproduce, then select and build the generation approach that fits, from statistical methods to generative models. We validate on two fronts that are both non-negotiable: privacy, testing against re-identification and memorization of real records, and fidelity, testing that the synthetic data preserves the patterns that make it useful. We also assess bias, since generation can distort representation. We build pipelines so data can be produced repeatably, and we document the validation so the data can be trusted and defended. Throughout, we are honest about the privacy-fidelity trade-off and tune to your requirements, because overstating either the safety or the usefulness of synthetic data is a serious mistake in healthcare.

Synthetic data sits at the intersection of privacy and utility, so both must be engineered and proven. Taction Software builds on a HIPAA-aligned foundation, with encryption, access controls, audit logging, and Business Associate Agreements where applicable, and we handle the real source data with the same rigor as any PHI during profiling and generation. We use generation methods matched to the data type, from statistical models to generative AI, on healthcare-grade cloud infrastructure. Critically, we treat privacy validation as mandatory, testing synthetic data against re-identification and memorization risk, and we are clear that synthetic data reduces but does not automatically eliminate privacy obligations, so its regulatory treatment should be assessed for each use. We validate fidelity and assess bias so the data is useful and fair, and we align privacy validation with our healthcare security audit practice. We document validation thoroughly, because trustworthy synthetic data must be defensible, not merely asserted.

Taction Software is a US-based healthcare software company founded in 2013, with offices in Chicago, Cheyenne, Austin, and Sacramento. We build healthcare software exclusively, so data engineering, privacy, and compliance are part of our default process rather than afterthoughts. We have delivered more than 200 healthcare projects, including EHR and EMR platforms such as Voyant Health, FDA-registered mobile applications, and behavioral health tools. That data and privacy depth is exactly what synthetic data demands, where the whole value rests on rigorous privacy and fidelity validation. We work as a candid partner, honest about the privacy-fidelity trade-off and disciplined about validation, rather than overselling synthetic data as a privacy cure-all. Our leadership brings deep, hands-on expertise, with our CEO contributing more than 20 years of personal experience in software and healthcare technology. Building with Taction means partnering with a team that has repeatedly taken healthcare software from concept to production in regulated settings.

Synthetic data generation pricing depends on scope, data types, fidelity requirements, and validation depth. Taction Software scopes each engagement to your purpose, and typical ranges are as follows. A focused engagement or proof of concept, such as generating and validating one dataset type for a specific use, generally falls between $40,000 and $80,000. A full synthetic data capability with multiple data types, robust privacy and fidelity validation, and generation pipelines typically ranges from $80,000 to $200,000. Enterprise programs with complex data, advanced generation, and ongoing production start at $200,000 and up. Because validation is essential, it is always included in scope, not treated as optional. Final pricing follows a discovery phase that defines use case, data, and validation requirements. We provide clear, itemized estimates so you can prove value on a focused dataset first.