Synthetic Data & AI Simulation
Synthetic Data and AI Simulation for Research Workflows
A practical way to test, prototype, and extend research before or alongside live fieldwork.
We help research teams use synthetic data and AI-assisted simulation to support planning, testing, reporting preparation, and scenario exploration with greater speed and flexibility.
Synthetic data and AI-based respondent simulation can add value when used with care. They are most useful when research teams need to prototype outputs, test workflows, model scenarios, or create safe example datasets before live fieldwork is complete.
We do not position synthetic data as a replacement for real respondent data where actual measurement is required. We position it as a complementary tool that can improve preparedness, accelerate workflows, and support better decision-making around the research process.
Where Synthetic Data Adds Value
Questionnaire Design Support
Synthetic response generation can help teams evaluate survey structure before launch, revealing logic issues, weak answer frameworks, missing categories, unrealistic paths, and reporting gaps.
Early Reporting Prototypes
Synthetic datasets can be used to develop toplines, crosstabs, dashboards, and reporting structures before fieldwork is complete, allowing earlier stakeholder review and smoother downstream execution.
Scenario Exploration
AI-assisted simulation can help teams explore how different audience groups may behave under different assumptions, making it easier to review expected, optimistic, and edge-case patterns before real results are finalized.
Logic and Pipeline Testing
Synthetic datasets are highly useful for validating survey logic, export structures, derived variables, dashboard pipelines, and reporting workflows in advance of live data.
Training and Demonstration Datasets
Non-sensitive structured datasets can be created for internal training, client walkthroughs, demonstrations, and process testing without exposing live respondent data.
Planning Support During Field
When stakeholders need to think through possible patterns before field is complete, synthetic outputs can support structured planning conversations without overstating certainty.
Segment Prototyping
Synthetic respondents can help teams think through likely segment behavior, subgroup patterns, and potential reporting implications across more complex studies.
What Synthetic Data Should Not Be Used For
- representing simulated outputs as real respondent findings
- replacing real fieldwork where valid measurement is required
- making definitive claims without clear labeling
- using artificial numbers to disguise weak design or incomplete data
The most credible use of synthetic data is transparent, purposeful, and clearly bounded.
Our Position
Use synthetic data as a companion to research, not a substitute for it.
The strongest use cases are typically found:
- before fieldwork
- alongside real survey data
- in planning, testing, prototyping, and workflow acceleration
This approach allows research teams to move faster without confusing simulation with measurement.
Example Services
Synthetic Survey Datasets
We create realistic structured response files based on survey design, target audience assumptions, and anticipated segment variation.
Reporting Prototype Data
We develop synthetic datasets that allow dashboards, toplines, crosstabs, and reporting formats to be built before live results arrive.
Logic and Export Testing Datasets
We generate datasets that help validate flow behavior, variable structure, exports, and downstream processing logic.
AI-Assisted Scenario Modeling
We support teams that want to explore how different assumptions may influence survey outcomes, segment behavior, or topline movement.
How This Works Alongside Real Survey Data
Synthetic data is often most valuable as part of a broader workflow rather than as a standalone output.
A typical use case may look like this:
- the survey is designed and programmed
- synthetic datasets are generated before field
- reporting structures are built and reviewed early
- live fieldwork begins
- real data replaces prototype outputs
- synthetic scenario modeling remains available for planning and interpretation support where useful
This is why survey programming and synthetic-data workflow support can live under the same brand: one supports the instrument itself, while the other supports the preparation, testing, and reporting ecosystem around it.
Need to prototype, test, or simulate before live data is ready?
We help research teams use synthetic data carefully and effectively to improve planning, accelerate workflows, and support smarter preparation.
Talk Through a Use Case