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UNSTRUCTURED SYNTHETIC DATA SERVICES
Unstructured Synthetic Data, Delivered with Confidence
Evaluate your AI systems against synthetic-but-realistic unstructured data that reflects the real world without touching it. Delivered as a one-off dataset or an ongoing programme; aligned to your domain, your risk profile, and your deployment timeline.
What We Offer
Two Delivery Models. One Standard Rigour
Data Infusion delivers unstructured synthetic data through two engagement models. Both are powered by NUROSCEND™, CALTREN™, and The CALT Principle™, our proprietary data production, validation, and methodology capability. Both operate on the same non-access principle: no real customer, patient, citizen data is accessed, received, ingested, or processed at any stage of any engagement.
UNSTRUCTURED SYNTHETIC DATA SERVICES
One-off Dataset
One-off datasets scoped to a defined testing objective.
Right for you if:
Immediate access to the Executive Guide
You need a targeted dataset for a specific deployment evaluation
You want to validate model behaviour against a defined use case before committing to an ongoing programme
You are running a proof-of-concept or pre-deployment assessment
What you receive:
A single, purpose-built synthetic unstructured dataset
Scoped to your domain, industry context, and testing objectives
Constructed by NUROSCEND™ and validated within CALTREN™
Delivered securely for internal testing and evaluation
Engagement: One-off | Delivery: Single dataset
UNSTRUCTURED SYNTHETIC DATA SERVICES
Ongoing Programme
Ongoing delivery of refreshed datasets aligned to your evolving testing requirements. Most flexible.
Right for you if:
You are running a continuous AI validation or testing programme
You are managing multiple workstreams or teams in parallel
Your models are evolving and require ongoing regression and validation testing
You need consistent testing over time as risk profiles change
What you receive:
Refreshed synthetic datasets delivered at your defined cadence
Aligned to changing testing needs and risk considerations
Ongoing requirements alignment as your programme matures
Optional support and refinement through the engagement
Engagement: Ongoing | Delivery: Regular cadence
Every dataset is constructed through NUROSCEND™ and validated through CALTREN™. The same methodology and the same depth, whether you need unstructured synthetic data once or on an ongoing basis.
The service model determines the cadence of delivery. It does not determine the quality of what is delivered.
A One-Off Dataset engagement produces a dataset constructed to reflect the full linguistic and behavioural range of the population your AI system will encounter. An Ongoing Programme ensures that dataset stays current as your system, your customer base, and your regulatory environment evolve.
Both are built the same way. Both are defensible the same way.
How an Engagement Works
Both One-Off Dataset and Ongoing Programme engagements follow the same structured process. There is no ambiguity about what is required, who does what, and how data safety is maintained throughout.
What you define upfront
Getting started does not require significant preparation. The scoping process is structured and efficient, designed to extract what we need without creating work for your team. A typical engagement defines:
Industry or domain context
Type of unstructured data required (for example: emails, documents, complaint transcripts, case notes, call logs)
Intended use cases and testing objectives
Risk and compliance considerations specific to your operating environment
Delivery cadence (for Ongoing Programme engagements)
The Capability Behind Every Dataset
Every dataset, whether delivered as a One-Off Dataset or an Ongoing Programme, is produced by the same Data Infusion proprietary technology - NUROSCEND™ and CALTREN™. There is no difference in rigour or methodology between the two delivery models.
NUROSCEND™
Intelligence & orchestration layer
Controls the behavioural logic, fidelity controls, and compliance constraints that govern every dataset ensuring synthetic interactions reflect real-world variability without re-identification risk.
CALTREN™
Synthetic data environment
CALTREN™ is Data Infusion's Secure Client Platform. It is the environment through which clients place orders, submit requirements, and download completed datasets. Every dataset is validated through CALTREN™ before delivery. CALTREN™ operates entirely independently of client environments and does not process real customer data at any stage.
The CALT Principle™
Governing methodology
The foundational methodology that ensures everything produced is provably real in its behavioural characteristics and provably free of any real personal or operational data.
NUROSCEND™ constructs. CALTREN™ validates. The CALT Principle™ governs. Together, they ensure that every synthetic dataset is realistic enough to test against and safe enough to use, with no access to real data required at any stage.
Data Safety and Isolation
This is not a policy position adopted for convenience. The entire architecture of our capability, NUROSCEND™, CALTREN™, and The CALT Principle™, was built so that real data is never needed. The synthetic environment operates independently. This is the structural basis for the privacy and compliance guarantees we provide.
This is worth being direct about.
Data Infusion does not access, receive, ingest, or work with your real customer, patient, citizen, or personal data at any stage of any engagement. Unstructured synthetic datasets are produced independently, based on agreed domain characteristics and testing objectives, without using real customer data at any point. Your data never leaves your environment and is never handed over to Data Infusion at any stage.
70%
fewer privacy violation sanctions for organisations using synthetic data, by removing the need to collect, store, and expose real customer information.
Source: Garner
Data Infusion
Synthetic data does not reduce privacy risk. It eliminates it by design because there is no real personal information in the dataset to protect, breach, or notify about.
Who This Is For
Batches and subscriptions are designed for organisations that are serious about GenAI testing rigour and for the people within those organisations who carry accountability for getting it right.
CIOs and CDOs accountable for AI governance and data risk
Digital Transformation and AI leaders responsible for programme delivery
Data, analytics, and governance teams supporting AI implementation
Product and engineering teams building and testing customer-facing AI systems
Risk and compliance functions overseeing AI deployment in regulated environments
Why Organisations Work With Us
The organisations we work with are not choosing synthetic data because it is convenient. They are choosing it because every other approach they considered (using real data, masking it, manually creating it, or buying generic datasets) carried risks they were not prepared to accept, or costs they could not justify.
Reduces compliance and reputational risk associated with using real data in AI testing environments
Improves confidence before production deployment by testing against linguistically realistic, domain-specific unstructured data
Supports faster iteration without being constrained by data access and privacy review cycles
Aligns AI testing with real-world complexity, not simplified or sanitised approximations of it
Provides a documented, repeatable, and defensible methodology for AI governance records
Get Started
Whether you need a targeted dataset for a specific evaluation, or an ongoing synthetic data programme to support continuous AI validation, the starting point is a conversation about your objectives. We will help you determine which model is right for your situation and how the engagement works in practice.