Create the data
your AI needs.
Built for the scenarios that matter.Realistic, controllable datasets for training, testing, and improving AI. Go beyond the limits of real world data.
Book a meetingThe world is unpredictable.
Your data doesn’t have to be.
Real data is scarce, sensitive, and rarely complete. We help you engineer the scenarios, patterns, and possibilities your models need to move forward.
The thinking behind RoboSynthData with intention.
Infrastructure for imagination.
Synth Studio.
01Build data with intent.
- Define schemas and relationships
- Control distributions and classes
- Make generation repeatable
Scenario Engine.
02Make the rare repeatable.
- Specify rare and difficult events
- Represent overlooked conditions
- Build controlled evaluation sets
Fidelity Lab.
03Put usefulness to the test.
- Inspect distributions and constraints
- Evaluate downstream utility
- Review coverage before export
From “what if”
to what’s next.
A repeatable workflow.
A dataset with a purpose.
Define
Start with a schema, a scenario, or a gap in your model coverage.
Generate
Create targeted samples around your constraints and conditions.
Validate
Inspect fidelity, utility, coverage, and privacy risk.
Export
Move approved datasets into your training and testing workflow.
Iterate
Refine the data as your model and requirements evolve.
Train on what
rarely happens.
Critical failures. Unusual conditions. The classes your model barely sees. Make them part of the plan.
Explore the possibilitiesDifferent challenges.
One new way to build.
From a model experiment to an
enterprise testing workflow.
Engineering
Keep repeatable scenario sets across application and model changes.
02Customer Support
Explore uncommon questions, ambiguous language, and mixed intents.
03Sales
Model relevant accounts and workflows in a controlled demo environment.
04Operations
Explore sensor patterns associated with degradation and failure.
A little perspective.
A better starting point.
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.