Data Engineering / Synthetic Data Generation
Synthetic Data Generation Services
Real-world data is often the actual bottleneck: too sensitive to share, too rare to have enough examples of, or too imbalanced to train a reliable model on. Synthetic data — generated algorithmically to preserve the statistical properties of real data without containing actual records — solves specific versions of this problem, but the right generation method depends heavily on your data type, your fidelity needs, and your privacy requirements. We help you choose and implement the right approach, and validate that the result is actually usable for what you need it for.
This Is For You If...
You need to share or work with sensitive data (healthcare, finance, customer data) without regulatory risk
You have too few examples of a rare but important event — a fraud pattern, a rare disease, an equipment fault
Your training data is imbalanced and models keep underperforming on the minority class
You have a synthetic dataset already and aren't confident it's actually a good stand-in for the real thing
Which Type of Synthetic Data Do You Need?
Ask Yourself | Points Toward |
Do you need artificial rows of structured, spreadsheet-style data? | Synthetic Tabular Data |
Do you need artificial sequences of data ordered over time? | Synthetic Time-Series Data |
Do you need artificial images? | Synthetic Image Data |
Do you need artificial text records or documents? | Synthetic Text Data |
Do you need a full dataset specifically to train a machine learning model? | Synthetic Training Datasets |
Is your primary concern sharing or working with data without exposing real individuals? | Privacy-Preserving Synthetic Data |
Do you have too few real examples of an important but uncommon event? | Rare-Event Data Generation |
Do you want to expand an existing dataset with modified or newly generated variations? | Data Augmentation |
Do you already have synthetic data and need to confirm it's actually good enough to use? | Synthetic Data Validation |
Real synthetic data projects usually combine several of these — privacy-preserving generation of a tabular training dataset that also needs to represent a rare event well, for instance. Tell us your actual data type and goal, and we'll help scope the right approach.
Synthetic Data Services We Offer
Synthetic Tabular Data
Synthetic tabular data generation for structured, spreadsheet-style data is challenged by the need to preserve inter-column correlations (like the relationship between age and income), categorical distributions, and the statistical properties of outliers — not just each column's values in isolation. We help you apply appropriate generation methods, from statistical approaches (Bayesian networks, copulas) for simpler, more interpretable cases, to GAN-based methods like CTGAN or diffusion-based approaches for more complex, realistic tabular joint distributions.
Synthetic Time-Series Data
Synthetic time-series data generation needs to preserve temporal dependencies and patterns — trends, seasonality, autocorrelation — that a method designed for static, unordered data won't capture correctly. We help you generate time-series data using approaches suited to your data's specific temporal structure, whether that's financial data, sensor data, or another sequential domain.
Synthetic Image Data
Synthetic image data generation has advanced substantially, with diffusion models now generally surpassing GANs for image synthesis quality — used for medical image augmentation (generating examples of rare pathologies), autonomous vehicle scenario simulation (extreme weather, unusual road conditions), and manufacturing defect image generation for quality inspection training. We help you choose and apply an image generation approach suited to your specific domain and the level of realism your downstream task actually requires.
Synthetic Text Data
Synthetic text data generation increasingly uses large language models, which can generate realistic text records — including, notably, treating structured data rows as text sequences to capture semantic relationships between fields (approaches like GReaT). We help you generate synthetic text appropriate to your use case, with the guardrails needed to keep output realistic and useful, whether for document generation, dialog data, or structured-to-text synthesis.
Synthetic Training Datasets
Synthetic training datasets are built specifically to train a machine learning model — meaning the generation approach needs to prioritize what actually matters for training performance (class balance, coverage of edge cases, label accuracy) rather than surface realism alone. We help you generate training data that genuinely improves your model's performance on the task you care about, not just data that looks statistically similar to your original set.
Privacy-Preserving Synthetic Data
Privacy-preserving synthetic data generation aims to let you share or work with sensitive data — healthcare, finance, customer records — without exposing real individuals, but this requires real care: synthetic data can still leak information if the generation method memorizes or reproduces identifiable records from the training data. We help you apply generation methods with formal privacy guarantees, particularly differential privacy, and assess re-identification risk properly rather than assuming synthetic automatically means private.
Rare-Event Data Generation
Rare-event data generation addresses the common and difficult problem of having only a handful of real examples of something important — a fraud pattern, a rare disease, an industrial fault, a crash scenario — where standard model training simply doesn't have enough signal to work with. We help you generate realistic synthetic examples of the rare event to meaningfully expand your training data, while being honest about the genuine risk of "fidelity gaps" — synthetic data can still miss complex correlations present in true long-tail events.
Data Augmentation
Data augmentation creates modified versions of existing real samples — from simple approaches like rotations, crops, and noise for images, to techniques like SMOTE (Synthetic Minority Over-sampling Technique) for tabular class imbalance — as a lighter-weight complement or alternative to full synthetic data generation. We help you choose augmentation techniques appropriate to your data type and the specific weakness (imbalance, insufficient variation) you're trying to address.
Synthetic Data Validation
Synthetic data validation confirms that generated data actually preserves the statistical properties and utility you need — covering fidelity (how statistically similar the synthetic data is to real data), utility for downstream tasks, and privacy risk assessment, since none of these can simply be assumed. We help you rigorously validate synthetic data before you rely on it, using appropriate statistical tests, downstream task performance comparisons, and privacy/re-identification risk checks.
Not sure which of these fits your project, or need a combination — say, privacy-preserving synthetic tabular data validated for downstream model training? That's the normal case for a real synthetic data project. Describe your data and goal, and we'll help scope the right combination.
What We Need From You
What to Share | Why It Helps |
A sample of your real data, or a detailed description of its structure | Needed to understand the statistical properties and relationships the synthetic data needs to preserve |
The primary purpose of the synthetic data (training a model, sharing safely, testing, augmenting a rare class) | Determines which generation method and evaluation criteria actually matter most |
Your privacy requirements, if data sensitivity is a concern | Central to whether formal privacy guarantees (like differential privacy) are needed, not just general anonymization |
The scale of synthetic data you need (how many records, images, or sequences) | Affects which generation approach is computationally practical |
Any existing generation attempt and what's not working (unrealistic output, privacy concerns, poor downstream performance) | Lets us diagnose specific issues rather than starting from scratch |
Your deadline | Helps us scope the right turnaround |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Assuming synthetic data is automatically private just because it doesn't contain real records | Generation methods can memorize and reproduce identifiable patterns from training data, so privacy risk still needs formal assessment | Use methods with formal privacy guarantees (like differential privacy) and assess re-identification risk explicitly when privacy matters |
Generating tabular synthetic data column-by-column without preserving correlations between columns | Breaks realistic relationships (like age and income) that matter for downstream analysis or model training | Use generation methods (CTGAN, copulas, Bayesian networks) designed to preserve inter-column correlations, not independent column sampling |
Assuming synthetic data will automatically capture rare, long-tail patterns well | Synthetic data generation methods can miss complex correlations present in true rare events, especially with very few real examples to learn from | Validate rare-event synthetic data carefully against domain expertise, not just statistical similarity metrics |
Using synthetic data for training without validating downstream task performance | Statistical similarity to real data (fidelity) doesn't guarantee the synthetic data will actually train a model that performs well | Validate synthetic training data specifically against downstream task performance, not just distributional similarity |
Choosing a generation method based on popularity rather than fit to your data type and fidelity needs | A method well-suited to tabular data (CTGAN) isn't the right choice for images, and vice versa — mismatched methods produce lower-quality synthetic data | Match the generation approach to your specific data modality and required fidelity |
Treating basic data augmentation (rotations, flips) as equivalent to full synthetic data generation | Augmentation modifies existing samples and has real limits; it doesn't create genuinely novel examples the way generative model-based synthesis can | Use augmentation for lighter-weight expansion of existing data, and full synthetic generation when you need genuinely novel, diverse examples |
How It Works
1. Share your data and goal
Tell us your data type, its structure, and what you need the synthetic data for
2. We identify the right generation approach
We confirm which method (statistical, GAN-based, diffusion-based, LLM-based) fits your data modality, fidelity needs, and privacy requirements
3. We generate the synthetic data
We implement the chosen approach, tuned to preserve the statistical properties that matter for your use case
4. We validate rigorously
We assess fidelity, downstream utility, and privacy risk as appropriate, rather than assuming the output is good enough
5. We explain what was generated and its limitations
You get a plain-English explanation of the method, what the synthetic data does and doesn't capture well, and any risks to be aware of
Why Codersarts
Synthetic data generation only creates real value when the method matches the data type and the actual downstream need — a method great for tabular data won't work for images, and data that looks statistically realistic can still fail on the specific task or privacy requirement that actually matters. We work across tabular, time-series, image, and text synthesis, privacy-preserving generation, rare-event and imbalanced-class problems, and rigorous validation, for real teams facing data scarcity, sensitivity, or imbalance problems that a real dataset alone couldn't solve.
Turnaround Time
Well-defined synthetic data generation tasks with available real data are typically turned around within a week. Privacy-critical generation requiring formal privacy guarantees, or larger-scale image/text synthesis projects, are scoped with a clear timeline once we understand your requirements. Research and launch deadlines are prioritized accordingly — let us know your timeline upfront.
FAQ
Do you use specific tools for synthetic data generation?
Yes — we work with tools and libraries including CTGAN, SDV (Synthetic Data Vault), synthcity, diffusion model frameworks, and differential privacy libraries, depending on your data type and requirements.
How do we know if the synthetic data is actually good enough to use?
We validate it explicitly — checking statistical fidelity, and where relevant, downstream task performance and privacy risk — rather than assuming a generation method's output is automatically usable.
Is synthetic data actually private, or could it still expose real people?
It depends on the generation method — some can memorize and leak real records. We help you assess this properly and use methods with formal privacy guarantees when that matters.
Is this for coursework, or only real data scarcity/privacy problems?
Both — we support academic coursework in generative modeling and data synthesis, as well as real business and research problems involving data scarcity, imbalance, or privacy constraints.
Can you help if we only have a handful of real examples of the thing we need to generate more of?
Yes — this is a common rare-event scenario, and we'll be honest about what's achievable and validate the results carefully given the limited real data to learn from.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Need Data You Don't Have Enough of, or Can't Safely Share?
Whether it's research or a real data scarcity or privacy challenge — generate data that's actually usable for what you need.