Sometimes the data you need doesn't exist yet, can't be shared, or is too sensitive to use. A new product has no history to train on. Customer records can't leave a secure environment. Rare events like fraud or equipment failures appear too seldom for a model to learn from. Test environments need realistic data without exposing real people.
Synthetic data generation creates artificial data that keeps the useful statistical patterns of real data — or represents the scenarios you need — without copying real records. Our synthetic data services help you generate, validate, and use synthetic data responsibly for machine learning, testing, demos, and data sharing.
What are synthetic data generation services? Synthetic data generation services create artificial data sets that mimic the structure and statistical properties of real data, or simulate scenarios that real data lacks. Methods include rule-based generators, statistical models, generative models for tabular data, and large language models for text. Providers validate synthetic data for fidelity (how closely it matches real patterns), utility (how well models trained on it perform), and privacy (how little it reveals about real individuals).
Synthetic Data Generation at a Glance
Question | Answer |
|---|---|
What it is | Creating realistic artificial data for AI training, testing, demos, and safe sharing |
Data types | Tabular data, time series, text and conversations, structured test data for applications |
Methods | Rule-based generation, statistical models, generative models for tabular data, LLM-generated text |
Validation | Fidelity, utility, and privacy checks against the real data |
Tools | Python, Faker, SDV (Synthetic Data Vault), custom generative models, LLM APIs |
Engagement models | Synthetic data proof of concept, data set generation project, test data pipeline |
When Synthetic Data Helps
Privacy-safe development and testing — realistic data in test and staging environments without real personal data
Data sharing — give partners, vendors, or researchers data that reflects real patterns without exposing real records
Rare event augmentation — add examples of fraud, failures, or edge cases to balance training data
Cold-start AI projects — create initial training or evaluation data before real data exists
LLM training and evaluation — generate varied prompts, conversations, and question sets
Demos and sales — realistic demo environments without customer data
When Synthetic Data Is Not Enough
Synthetic data can't create information that isn't in the real data or in your design rules. Models trained only on synthetic data may miss real-world patterns, and poorly generated data can still leak information about real people. We're clear about these limits and usually recommend combining synthetic data with real data and human-labeled evaluation sets.
Our Synthetic Data Services
Use case and approach design
We agree why you need synthetic data, what it must preserve, and which risks matter, then choose the right generation method.
Test data generation
We generate realistic, rule-consistent test data for applications and databases — names, addresses, orders, transactions, and relationships that respect your schema and business rules.
Tabular synthetic data
We train statistical and generative models on real tabular data to produce synthetic data sets that preserve distributions and relationships.
Time series synthesis
We generate realistic sequences for sensors, transactions, or usage data, including rare events and seasonal patterns.
Text and conversation data
We use LLMs with careful prompting and review to generate varied prompts, dialogues, and documents for NLP and LLM projects, connected to our LLM evaluation work.
Data augmentation
We create additional examples of under-represented classes or scenarios to improve model balance and robustness.
Validation
We measure fidelity, utility, and privacy, and report how close the synthetic data is to real data and how well models trained on it perform.
Test data pipelines
We automate synthetic test data generation so environments can be refreshed on demand.
Validating Synthetic Data
Check | Question | How it's measured |
|---|---|---|
Fidelity | Does it look like the real data? | Comparing distributions, correlations, and relationships |
Utility | Is it useful for the task? | Training models on synthetic data and testing on real data |
Privacy | Does it reveal real individuals? | Checking for copied records and re-identification risk |
Rule consistency | Does it follow business rules? | Validating constraints, formats, and relationships |
How It Works
Step 1: Define the goal
We agree what the synthetic data is for and what it must preserve or avoid.
Step 2: Review real data or rules
We study the real data's structure and patterns, or define generation rules for new scenarios.
Step 3: Generate
We build the generator and produce a first data set.
Step 4: Validate
We measure fidelity, utility, and privacy, and tune the generator.
Step 5: Deliver
We deliver the data set and, where useful, the generator itself.
Step 6: Automate (optional)
We set up pipelines to regenerate data on demand.
What You Get
A synthetic data set fit for your purpose
A validation report covering fidelity, utility, and privacy
A reusable generator where needed
Documentation of methods and limits
Automated test data refresh on request
Who This Is For
Engineering and QA teams | AI and ML teams | Regulated industries | SaaS and product teams |
|---|---|---|---|
Need realistic test data without real personal data | Need more training examples or balanced classes | Need safer ways to share or use sensitive data | Need realistic demo environments |
Why Work With Codersarts
Synthetic data is only valuable if it's validated. We measure how useful and how private each data set is, explain trade-offs honestly, and combine synthetic data with real data and human review where needed. Our AI data services and data privacy teams work together so synthetic data helps rather than creates new risks.
Frequently Asked Questions
What is synthetic data?
Synthetic data is artificially generated data that reflects the structure and statistical patterns of real data, or simulates scenarios, without copying real records.
Is synthetic data private?
It can greatly reduce privacy risk, but not automatically. Poorly generated data can still reveal real individuals, so we test for copied records and re-identification risk.
Can we train AI models only on synthetic data?
Sometimes, for narrow tasks, but most projects work best combining synthetic and real data, and always evaluating on real data.
How do you measure synthetic data quality?
We measure fidelity against the real data, utility by training and testing models, privacy risk, and consistency with business rules.
Can you generate test data for our application?
Yes. We generate realistic, rule-consistent test data that respects your database schema and relationships.
How long does a synthetic data project take?
A proof of concept typically takes a couple of weeks. Larger or automated generation projects take longer.
How much do synthetic data services cost?
A proof of concept is a fixed-scope engagement. Larger projects are quoted based on data types, volume, and validation requirements.
Related Data Services
Data Labeling — human-labeled data to combine with synthetic data
Data Quality — checks for training and test data
Machine Learning — models trained on augmented data
RLHF Services — human feedback data for LLMs
Ready to Create the Data You Need?
Tell us what data you're missing and why. We'll recommend whether synthetic data fits and what it would take.
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