Engineer the data.
Expand the possible.
Synthetic data generation, scenario design, and validation. One coherent workflow for the datasets your AI actually needs.
Book a meetingA dataset should begin
with an objective.
More records alone do not solve a data problem. RoboSynth brings control, scenario coverage, and validation into the same process, so teams can generate data with a clear purpose.
Build data with intent.
Shape datasets around the requirements of your model, rather than the limits of your source data.
- Define schemas and relationships
- Control distributions and classes
- Make generation repeatable
Make the rare repeatable.
Bring unusual conditions into your development cycle, with targeted scenarios you can inspect and refine.
- Specify rare and difficult events
- Represent overlooked conditions
- Build controlled evaluation sets
Put usefulness to the test.
Evaluate how well your synthetic dataset meets the structural and practical needs of its intended task.
- Inspect distributions and constraints
- Evaluate downstream utility
- Review coverage before export
Use patterns with care.
Make source minimization and privacy evaluation part of your data development workflow.
- Review similarity to source records
- Evaluate memorization risk
- Define sensitive field constraints
Put approved data to work.
Plan the handoff from generated datasets to the training, evaluation, and software systems that need them.
- Define export requirements
- Track dataset configurations
- Fit existing engineering workflows
Start with the model.
Work back to the data.
Bring your data type, system constraints, and acceptance criteria. We will confirm the available generation and delivery approach for your project.
- Tabular records and relationships
- Time series and event scenarios
- Text and conversation evaluation
- Vision and environmental requirements
Good questions.
Straight answers.
Start here. Bring the specifics
to our conversation.
What is RoboSynth?
RoboSynth is a synthetic data platform for AI and data teams. Define dataset requirements, generate targeted samples, validate their quality, and use approved data in training, evaluation, and testing workflows.
Does synthetic data replace real data?
Usually, it complements it. Synthetic data is useful for augmentation, rare scenarios, controlled evaluation, and test environments. The right mix depends on your application and should be validated against representative real data.
Can I generate rare events and edge cases?
Yes. Scenario generation is central to RoboSynth. Define the unusual conditions, underrepresented classes, or failure scenarios you want to cover, then evaluate those samples for your intended use.
How do I know the data is useful?
Assess schema consistency, distributions, relationships, scenario coverage, and downstream model performance. Privacy review is a separate step. A dataset should meet your acceptance criteria before it enters a training or testing pipeline.
Is synthetic data automatically private?
No. Privacy depends on the source data, generation method, evaluation, and use. Synthetic data can reduce exposure to raw records, but similarity and memorization risks still need to be evaluated.
Which data types can we discuss?
Bring your requirements for tabular records, time series, events, text, or vision scenarios. We will confirm the supported modality, validation approach, and delivery requirements for your project during the meeting.
Can we use it for software testing?
Yes. Structurally realistic datasets can support QA, staging, regression tests, analytics development, and product demonstrations while reducing dependence on production records.
How do we get started?
Book a meeting to discuss your data objective, constraints, and success criteria. RoboSynth is live. You can start the conversation without creating an account or joining a waiting list.
Build the dataset your model is missing.
Bring your toughest data problem.
Let’s work out what comes next.