In 2025, AI is moving fast, but it still hits a wall when it comes to data.
Real-world data is hard to find, expensive, and rooted in privacy regulations. That’s where synthetic data comes in. It’s artificially generated data that looks and behaves like the real data.
It fills gaps, protects privacy, and saves tons of time and money. But here’s the catch: traditional ways of creating synthetic data can be slow, rigid, and manual.
Solution?
Implementing an agentic infrastructure. It uses autonomous AI agents that plan, learn, and adapt on their own. These agents can generate synthetic data, structure it, improve it, and make sure it meets goals. All of this happens without constant human input.
In this blog, let’s explore
The limitations of traditional synthetic data workflows
Agentic infrastructure and how it benefits data workflows
Benefits of implementing agentic infra for synthetic data generation
What the future of synthetic data generation looks like.
Let’s go!
The Problem with Traditional Synthetic Data Workflows
Most synthetic data generation today still relies on static, rule-based scripts or one-off machine learning models. These pipelines often use popular techniques like
GANs (Generative Adversarial Networks)
VAEs (Variational Autoencoders)
LLM
But while math is powerful, the process around them is far from flexible; it’s hectic and complex.
First, there’s a lot of manual work involved. Data engineers spend a lot of time
Setting up the data schema
Defining transformation rules
Fine-tuning model parameters
Performing post-generation validation.
Traditional ways of synthetic data generation are not plug-and-play. They are more like building a custom toolchain for every new use case. Even a small change in a target domain (like switching from banking transactions to insurance claims) can mean starting from scratch.
These traditional methods also struggle when data evolves
For example, if your downstream machine learning model needs new fields, updated formats, or better edge-case handling, most synthetic data generators can’t adjust automatically. You have to go back to the drawing board, tweak parameters manually, or write new scripts.
Scalability becomes a problem
If you want to expand from tabular data to time-series data or add synthetic logs for an LLM training pipeline, you will hit a roadblock. Now, you’ll need more engineers, new models, and additional validation logic.
Traditional pipelines don’t easily generalize across data types or domains without significant reengineering.
And then there’s quality control
How do you know if your synthetic data is good? Most traditional pipelines don’t include feedback loops. They generate data once and stop. Unless you manually inspect the outputs, run diagnostics, or compare downstream model performance, poor data can quietly make your data unusable for models.
While each of these processes has its own value, doing them manually wastes time and resources. This slows down model training. There’s a growing need for automation.
What Is Agentic Infrastructure?
93% of business leaders think companies that use AI agents well in the next year will get ahead of their competitors. (Source: Capgemini)
Agentic infrastructure flips the script on how synthetic data is created and managed.
Instead of relying on rigid scripts or static workflows, it uses a network of AI agents where each agent has a specialized role, like generating samples, validating quality, or adapting schemas. These agents continuously gather feedback, evaluate the usefulness of the data they generate, and improve their methods over time.
Unlike traditional pipelines, which follow fixed instructions, agentic systems adapt to context. For instance, if a downstream model struggles with rare events, an agent can detect that gap and generate new synthetic examples to fill it. Another agent might adjust data formats or balance class distributions. All this happens without human supervision.
Features of Agentic infrastructure in synthetic data generation:
Context awareness: Agents monitor logs, performance metrics, and usage patterns to understand what kind of synthetic data is most needed.
Autonomous decision-making: Agents act independently to update data generation strategies, select models, or fine-tune parameters.
Continuous learning: As they receive feedback from model performance or data validation layers, agents adjust their behavior to produce more relevant and higher-quality data.
Collaboration: Many AU agents can work at the same time. For example, one agent focuses on data structure while another focuses on privacy compliance.
In short, agentic infrastructure turns synthetic data generation into a living, self-improving ecosystem that is more responsive, scalable, and intelligent than ever before. Synthetic data generation platforms like Sycnora.ai make use of this infrastructure.
How Agentic Systems Improve Synthetic Data Generation
1. Adaptive Agents
These agents generate data, test how useful it is, and refine their approach. They use feedback from models or evaluation tools to make the next batch better. Over time, they learn to produce more realistic and useful examples.
2. Simulated Environments
Multi-agent simulations let you create synthetic datasets based on real-world interactions. You can simulate traffic, financial transactions, social behavior, and more. The result is data that reflects complex patterns that would be hard to model otherwise.
3. Cross-domain Collaboration
One agent generates text, another makes matching images, and a third agent stimulates sensor data for the same scenario at the same time. This is possible with agentic AI. These systems can coordinate these outputs so they align, creating rich, multi-modal datasets that work together.
4. End-to-end Pipelines
Instead of stitching together a bunch of tools, agentic infrastructure handles the entire synthetic data lifecycle. From ingesting raw inputs to validating final outputs, agents can automate and optimize every step.
5. Dynamic Structuring
Agents can automatically choose or change data formats depending on the use case. If a model performs poorly on certain inputs, agents can reformat the data or add new metadata. This keeps your synthetic data aligned with real needs.
What’s Next: Agentic AI + Synthetic Data Generation
Syncora.ai is a next-generation synthetic data platform that fully embraces agentic AI.
Instead of relying on rigid workflows, this synthetic data generation tool deploys AI agents to generate, structure, and continuously refine synthetic datasets. All this happens while protecting privacy and staying compliant with GDPR, HIPAA, and other norms.
These agents learn from feedback and adapt to changing model needs. Your data stays accurate, diverse, and production-ready. With built-in privacy controls and tokenized rewards for data contributors, Syncora.ai makes it easy to scale data generation fast and safely.
As per a report, the global AI agents’ market is expected to grow from $5.29 billion today to $216.8 billion by 2035. That’s a massive jump, growing at around 40% every year.
Synthetic data is essential for the future of AI, but it’s agentic infrastructure that will make it fast, flexible, and scalable. Instead of manually curating and engineering data, we can build systems that do it for us.
These systems don’t just generate synthetic data; they understand the purpose behind it and adapt to meet that need. As more teams adopt agentic approaches, we’ll see AI models trained on smarter, more diverse, and more ethical datasets.
Data is the goldmine for AI models, and synthetic data is the key that opens it — safely, quickly, and at scale.
Synthetic data is privacy-safe, scalable, and increasingly used to train machine learning models without exposing real user information. But here’s the catch: even synthetic data needs to be trusted.
How do you know if synthetic data:
Was generated correctly?
Is privacy-safe?
Can be proven where it came from?
To answer this, blockchain enters the picture.
No, blockchain is not only about crypto and mining, but rather it holds a true value: transparency and security. By combining synthetic data generation with blockchain, we get a powerful foundation for trust, transparency, and automation in synthetic data workflows.
Synthetic data is fake data, but in a good way. It mimics real data so it can be used to train AI models, without containing any actual personal or sensitive information.
But with traditional synthetic data tools, there’s a trust gap. You’re never fully sure how the data was generated, what logic was used, or whether it still carries hidden risks. Most tools operate like black boxes, offering little or no transparency or traceability. That makes it hard for teams to confidently use the data in high-stakes environments like healthcare or finance.
There’s another problem with this. When synthetic data is bought, sold, or shared, people still ask:
How was this data created?
Can I trust its quality?
Is it really privacy-compliant?
Who owns it?
If you’re a data scientist, a compliance officer, or even a contributor sharing data, trust is everything. But with traditional systems, this trust is often based on promises and paperwork, not provable facts. That’s where blockchain makes a big difference.
Blockchain in Synthetic Data Generation
Blockchain is a transparent, tamper-proof ledger that records every action permanently. In synthetic data generation, this means every transformation, privacy step, and data output can be verified and traced. Here’s how it helps synthetic data workflows:
1. Transparency
With blockchain, every step, whether it’s generating synthetic data, validating it, or licensing it, is recorded on a public ledger. That means anyone, from developers to regulators, can independently verify what happened and when.
Blockchain ensures that there are no hidden processes or missing logs. During synthetic data generation, it gives a clear and open trail of actions that anyone can trust and audit.
2. Auditability
Blockchain creates a tamper-proof, timestamped audit trail. You can trace every synthetic dataset’s life cycle from the past to the present. This includes raw data ingestion to how it was anonymized, validated, and eventually licensed or shared.
The blockchain provides complete visibility for enterprises and regulators. This helps prove compliance and reduce legal risks.
3. Decentralized Validation
One of the best things about blockchain is decentralization — and it can be applied to synthetic data generation! Instead of relying on a single party to review data, blockchain enables peer review.
In this scenario, subject-matter experts or approved validators can assess the quality of synthetic datasets, and their reviews are transparently recorded. This crowdsourced feedback ensures data is trustworthy and accurate, with no hidden manipulation.
4. Smart Contracts for Licensing
Smart contracts are automated agreements on the blockchain. They can handle dataset licensing, payments, and permissions without the need for legal paperwork or manual intervention.
Everything runs instantly, securely, and with predefined rules. This saves time and ensures fair usage terms.
Syncora.ai: Where Blockchain Meets Synthetic Data
Syncora.ai is a platform that combines agentic synthetic data generation with the Solana blockchain to create a decentralized, transparent data marketplace.
Why Solana?
High throughput: Can handle thousands of transactions per second
Low fees: Makes microtransactions (like per-dataset licensing) feasible
Fast finality: No lag between licensing and access
Scalable ecosystem: Easily integrates with other Solana-based tools and wallets
With Solana, it becomes practical to log every action on-chain (whether small or big). Here’s how Syncora.ai uses blockchain in synthetic data generation.
1. Every Step is Logged On-chain
From the moment you feed raw data into the system, Syncora.ai’s AI agents go to work. They
Structure the data
Apply privacy transformations
Generate synthetic records
Run validations
Now, each of these steps is logged on the Solana blockchain. That means:
Contributors can prove how their data was used
Consumers can trace a dataset’s origins
Regulators can verify compliance with privacy laws
Blockchain ensures traceability & transparency at every step.
2. Smart Contracts Handle Licensing
Traditionally, data licensing involves NDAs, legal teams, and a lot of communication back and forth. With Syncora.ai , this is replaced by ephemeral smart contracts.
Here’s how it works:
A buyer picks a synthetic dataset from Syncora.ai’s marketplace
A smart contract checks if they have enough $SYNKO tokens (Syncora.ai’s utility token)
The contract automatically splits the payment between the dataset contributor, validators, and the platform in real time.
The contract then issues a cryptographic license proof and logs the transaction permanently on-chain.
Ephemeral smart contracting happens in seconds and saves time as opposed to traditional methods of licensing.
3. Validators Keep Data Honest
Just like how online platforms rely on user reviews, the synthetic data uploaded in Syncora.ai’s marketplace relies on peer validators. This is to ensure data quality and fairness.
Here, validators are domain experts (like healthcare or finance analysts) who:
Review samples of synthetic data
Run statistical checks
Rate quality and flag issues
Their reviews are recorded on-chain, so they’re public and verifiable. This builds a reputation system where high-quality datasets and validators rise to the top.
Validators also stake $SYNKO tokens, which they can lose if they validate low-quality data dishonestly. That keeps everyone accountable.
4. Transparent Token Rewards
By using blockchain in Syncora.ai’s ecosystem, data contributors and validators can earn tokens every time their work is used or validated.
For example:
Alyssa uploads transaction logs → synthetic dataset is generated → someone licenses it → Alyssa earns $SYNKO.
Bryan validates a medical dataset → it gets approved → Bryan earns a reward from the validator pool.
These payments happen automatically via smart contracts, and there are no delays or middlemen. And the entire token flow is visible in Solana’s ledger.
5. Compliance, Baked In
As per a report, over 80% of GDPR fines in 2024 were due to insufficient security measures leading to data leaks.
Privacy laws like GDPR, HIPAA, and others are strict and demand proof. You can’t just say “we anonymized this” or “we followed policy.” You need evidence.
With blockchain, Syncora.ai makes this a reality:
Immutable logs of every privacy transformation
Proof that no raw data ever left secure environments
Auditable validation and licensing records
To Sum This Up
Synthetic data is one of the most promising solutions for privacy-safe AI training. But to truly scale its use across industries, countries, and ecosystems, we need more than just good algorithms. We need trust, traceability, and transparency. That’s what blockchain brings to the table, and platforms like Syncora.ai are leading the way. They are combining AI agents with blockchain-backed infrastructure to deliver privacy-safe, auditable, and incentivized synthetic data at scale.
A major roadblock for data scientists? They waste over 60% of their time on data cleanup and organization.
Artificial intelligence (AI) models heavily rely on data for training. But, they don’t need just any data. They need clean, structured, diverse, and privacy-safe data.
But here’s the reality check: getting that kind of data is hard. Real-world data is costly, time-consuming, biased, and burdened by compliance regulations that can make it impractical or unusable for AI applications.
Even when the AI teams get their hands on real-world data, new sets of challenges arise: messy logs, strict privacy laws, labor-intensive cleaning, and more.
Data scientists and engineers often spend more time prepping data than building models! That’s where synthetic data can help, and more importantly, agentic AI that speeds up the whole process.
Synthetic data is artificially generated data that mimics the structure, patterns, and statistical properties of real-world data without containing any actual personal or sensitive information.
Consider that you work for a healthcare startup. You want to train a machine learning model to predict disease risk based on patient records that you have. But you can’t use real patient data since it’s protected under laws like HIPAA or GDPR.
So instead, you now generate synthetic patient records that look and behave like real data you have, but they contain no identifiable details.
This lets your AI models train on data without breaching anyone’s privacy. It’s the best of both worlds: realistic, usable, and safe. But here comes the pain of generating synthetic data with traditional approaches.
Traditional Synthetic Data Generation is Powerful but Painful
Synthetic data is robust, but generating it using traditional methods isn’t easy.
Usually, data teams have to go through a lot of processes, like:
Cleaning and structuring raw data manually.
Anonymizing or masking sensitive fields.
Choosing a generative model (like GANs or Bayesian networks).
Training and tuning it, often over multiple iterations.
Manually evaluating quality and fixing errors.
Packaging the data for model use or sharing.
This process is not only time-consuming but also prone to risks. If teams make one mistake in anonymization or schema design, it can compromise privacy. If they are dealing with time series, financial logs, or healthcare records, the process of generating synthetic data gets more complex.
In short, traditional synthetic data generation:
Takes days
Requires deep domain expertise
Can’t easily scale across multiple datasets
Struggles with privacy compliance
Can result in biased models
So, what’s the solution for this?
Agentic AI for Synthetic Data Generation
Agentic AI is a system that performs tasks on its own without human intervention. It plans its workflow, chooses the right tools, and completes goals independently, acting on behalf of a user or another system.
Agentic AI can be a nectar for data and AI teams, and it can make synthetic data generation fast and easy.
Instead of data teams doing everything manually, autonomous agents can take over repetitive, structured tasks like:
Detecting and cleaning messy data
Structuring data into schemas
Applying privacy transformations
Generating synthetic data in multiple formats
Validating output quality
Logging all activity for audit and feedback
And all of this can be done in minutes, saving data teams weeks.
Agentic AI in synthetic data generation is similar to having a team of assistants that know how to prep data, follow compliance rules, and learn from their mistakes.
How Agentic Pipelines Speed up Synthetic Data Generation
There are 2 steps of synthetic data generation with AI agents.
1. Agentic Structuring
The first step is where raw or semi-structured data is automatically analyzed and turned into usable schemas. You feed the data to an agentic synthetic data generation tool. Then:
AI agents detect field types, relationships, and patterns in the data (like recognizing a column as “date of birth” or “transaction ID”).
They apply privacy rules (anonymize names, generalize zip codes, etc.).
They build a data blueprint that downstream agents can use to generate synthetic data.
Here, no human is needed to define the schema, scrub the data, or guess what’s sensitive. The agents do it all within minutes.
2. Agentic Synthetic Data Generation
Once the data is structured, a new set of AI agents gets to work.
They generate synthetic data depending on the domain (e.g., tabular, image, JSON, time-series).
They make sure the synthetic data keeps statistical fidelity. This means it “looks like” the real data in behavior.
They include privacy checks so no real-world info leaks through.
The best part is that the feedback from validators and real-world usage is fed back to improve the model automatically. Within minutes, data & AI teams get scalable synthetic data that’s safe, structured, and ready for machine learning.
Syncora.ai for Agentic Synthetic Data Generation
Syncora.ai is a platform that brings all of this to life. It employs AI agents that structure and generate synthetic data that is safe, privacy-compliant, and robust.
Here’s what makes Syncora.ai different than traditional synthetic data generation methods.
1. Fully Automated Agentic Pipeline
From schema generation to synthetic data creation, Syncora.ai uses a modular architecture and lets AI agents organize the entire workflow. This process happens in minutes.
2. Built-in Privacy and Compliance
Syncora.ai uses built-in privacy techniques to protect your data:
Anonymization removes things like names or exact locations.
Generalization turns specific details (like age 27) into broader groups (like 25–30).
Differential Privacy adds a bit of “noise” so no single person’s info can be traced.
These protections are applied automatically during data structuring. And every step is recorded on the Solana blockchain, giving you a secure, tamper-proof audit trail.
3. Multi-modal Data Support
Whether it’s tabular logs, time-series data, images, or JSONL files, Syncora’s agents know how to handle and synthesize them with domain-specific accuracy.
4. Peer Validation and Feedback Loop
Synthetic datasets are peer-reviewed by domain validators. Their feedback improves data quality over time. It uses an organic, community-driven QA system.
5. Token Incentives for Contributors
Syncora.ai rewards data contributors and validators with its native $SYNKO token. It’s a win-win situation for all. Contributors earn, and consumers get verified, high-quality synthetic datasets.
How Syncora.ai Helps: A Real-world Example
A hospital wants to enable researchers to study trends in patient outcomes, but can’t share raw EHR data.
With Traditional Synthetic Data Generation Approach:
The hospital manually cleans and anonymizes the data, which is a slow, error-prone process.
They rely on basic rules or GANs to generate synthetic samples, often missing rare or important medical patterns.
There’s no easy way to check data quality, and the process needs constant human oversight.
Sharing is done manually too, with legal back-and-forth for licensing and compliance.
With Syncora.ai:
The hospital uploads its raw data to Syncora’s secure environment.
Structuring agents detect fields like patient ID, diagnosis, treatment, etc.
Privacy agents anonymize or generalize sensitive fields.
Synthetic data agents generate statistically accurate patient records in minutes.
Validators (e.g., medical data experts) review and rate the data quality.
Researchers license the synthetic data via Syncora’s marketplace, paying in $SYNKO.
In a nutshell, what used to be a months-long legal and technical process is now fully automated and audit-ready in a few minutes. This happens without exposing a single real patient’s information.
In a Nutshell
Synthetic data is no longer a “nice-to-have” in AI… It’s becoming a must. But to keep up with the growing demands for privacy, scale, and quality, the way we generate that data has to evolve. Agentic AI changes the game. By automating everything from data structuring to synthesis and validation, it speeds up how we produce usable, safe, and scalable datasets. Platforms like Syncora.ai are proving this isn’t just theory. So, if you’re tired of wrestling with raw data, stuck in compliance issues, or just want to launch AI faster. It is the right time to let the AI agents take the lead.
The numbers say it all. People want to use agentic AI, whether it’s for automation or other tasks. When the world of AI and data is considered, agentic synthetic data can be of help.
Synthetic data is needed for creating artificial datasets that look and behave like real data.
But now, newer systems called “agentic” synthetic data generation are taking the stage. These agent-based synthetic data generation tools not only generate synthetic data but also understand the context, learn from patterns, and autonomously refine the data to meet specific needs.
Synthetic data is artificially generated data that acts as real-world data. The traditional way of creating it includes using software algorithms. You can also use simple statistical models or complex neural networks like GANs.
These tools produce datasets with the same patterns and relationships as real data; but they do not expose any personal or sensitive details. This makes them useful for training AI, testing systems, and preserving privacy.
What Is Agentic Synthetic Data?
Agentic synthetic data takes the idea of generating synthetic data to the next level. Instead of just generating datasets, this approach uses autonomous agents (AI systems) that can make decisions, plan tasks, and learn from outcomes.
While synthetic data can just give you a new data set, agentic synthetic data tools offer much more.
These agents can sense gaps in the data
They can decide what new samples are needed.
Based on the information, they can create new samples and test them
They can run this cycle repeatedly to generate new datasets for various scenarios.
Agentic data generation tools like Syncora.ai are already doing these without constant human control.
Comparison: Synthetic vs. Agentic Synthetic Data
Feature
Synthetic Data
Agentic Synthetic Data
Creation Method
Fixed algorithms or generative models (GANs, VAEs)
Autonomous agents simulate, learn, plan, and iterate to generate data
Human Involvement
Manual setup and guidance
Minimal (agents decide what data is needed)
Adaptability
Can’t adjust once set (limited)
Self‑adjusting based on feedback and performance
Goal Orientation
Generates data based on static instructions
Agents pursue clear goals (e.g., fill data gaps, support a diagnosis model)
Feedback Loop
No ongoing evaluation
Continually tests and improves the data it creates
Handling Complex Scenarios
Can generate edge cases if specified, but needs manual work
Simulates complex interactions and rare events automatically
Privacy & Compliance Awareness
No intelligence; the risk depends on the setup
Agents can enforce ethical and privacy constraints during generation
Use Cases of Synthetic and Agentic Synthetic Data Generation
Here are two simple examples showing how synthetic data and agentic synthetic data work in different scenarios.
1. Healthcare
Requirements: A hospital research team wants to train an AI model to detect early signs of a rare heart condition.
Synthetic Data Generation Approach Since real patient records are limited and protected under privacy laws, the team uses a generative model (like a GAN) to create 10,000 synthetic patient records. These records mimic the structure and patterns of real electronic health records like blood pressure readings, heart rate trends, family history, etc. However, they still need to manually check if these generated records cover all disease stages. Doctors and data scientists review them to ensure rare variations of the heart condition are included. If not, they go back, tweak the model, and regenerate the data.
Agentic Synthetic Data Generation Approach An agentic AI system is given the goal: “Improve early detection for rare heart conditions.” The agent first analyzes the real data available and spots missing patterns. It autonomously generates synthetic patient records to fill this gap, using simulation and clinical logic. After creating these new samples, the agent immediately tests the model’s performance, sees where it still fails, and iterates by adding more edge cases (e.g., patients with comorbidities or unusual symptoms). All this happens without human intervention. The agent even ensures the synthetic data complies with medical privacy standards.
2. Automobile Industry
Requirements: A self-driving car company needs nighttime driving images to train their AI for automated driving during nighttime.
Synthetic Data Generation Approach The team uses a generative model like a GAN to create 10,000 dark street scenes. But the team has to set up the inputs manually — like where to place cars or pedestrians. After generating the images, they check them to remove unrealistic ones, and then label each image with boxes around objects. This takes a lot of time and might still miss rare situations like a pedestrian crossing in heavy fog or sudden movements.
Agentic Synthetic Data Generation Approach With agentic synthetic data, an intelligent agent simulates full driving environments on its own. It sets the lighting, weather, traffic, and pedestrian behavior without help. If it notices the car model performs poorly in foggy conditions, it creates new scenes focusing on fog and tricky pedestrian crossings. It automatically labels all objects and keeps testing the model after each round of new data.
In short, traditional synthetic data needs a lot of manual work and still has blind spots. On the other hand, agentic synthetic data adapts automatically, fills in the gaps, and keeps improving the model without human effort.
Agentic Synthetic Data is The Future
Traditional synthetic data generation relies on pre-set models and manual inputs. While it helps fill data gaps, it often needs human effort to set up, tune, and validate results. Agentic synthetic data employs AI agents that do all this without the need for human command.
These systems don’t just follow instructions; they actively generate data by simulating environments, adjusting their outputs, and improving as they learn. They not only know what data you need but also figure out how to create it in the best way possible.
Agentic models also adapt to privacy rules, making sure synthetic data doesn’t reveal sensitive info. They can simulate complex real-world situations, like traffic or financial markets, with multiple agents interacting naturally — something traditional methods struggle with.
By being goal-driven, self-improving, and privacy-aware, agentic systems make synthetic data generation faster, safer, and more useful.
In short, agentic behavior brings intelligence to synthetic data creation. And that makes it a game-changer for the future of AI and synthetic data.
Agentic Synthetic Data Tool: Syncora.ai
Syncora.ai is a synthetic data generation tool that uses agentic AI to make real, practical datasets that are as good as real datasets.
Syncora.ai’s AI agents structure your raw data, spot missing parts in the data landscape, and fill gaps — all with minimal setup.
Data is production-ready in minutes, cutting weeks of prep and 60% of costs.
Every dataset generated is logged on the blockchain and meets HIPAA, GDPR, and other privacy standards.
A built-in feedback loop reduces bias and boosts accuracy (up to 20% better in early tests).
Agents validate the data they generate, so accuracy improves cycle by cycle.
If your team needs synthetic datasets beyond what traditional synthetic tools offer, Syncora.ai’s agentic platform is all you need.
To Sum It Up
While traditional synthetic data helps create useful training datasets, it still relies heavily on manual setup and static models. Moving to Agentic synthetic data, you can automate most of the work and get a high-quality, diverse dataset that is privacy-compliant. AI agents can understand the data needs, fill gaps, and adapt on their own. This makes the process faster, more accurate, and scalable. So, if you’re looking to future-proof your AI models, choosing an agentic synthetic data generation approach is the better choice.
Over 80% of developers say they’d choose synthetic data over real data, mainly because it’s safer and easier to access. (Source: IBM research)
Synthetic data is artificially generated data that is similar to real-world data and has zero privacy risk. In 2025, it’s the best solution for AI teams, developers, and data scientists who need high-quality, bias-free data. This is needed when real data is limited, sensitive, or too expensive to use.
We will also check a revolutionary synthetic data generation tool that makes generating synthetic data reliable and rewarding.
What is Synthetic Data?
In fields like AI and machine learning, a huge volume of high-quality data is needed to train the models, but there’s one big problem: real-world data can be hard to find, expensive, and heavily regulated. This makes accessing the data difficult; and this is where synthetic data can tackle this challenge.
Synthetic data is artificially generated datasets that mimic the statistical properties of real data. It is based on real data but is created by algorithms that simulate real-world events. Synthetic data can be created whenever you need it and in large amounts.
It can be used as a safe replacement for real data in testing and training AI models. With synthetic data, teams can build faster, keep privacy intact, and follow data rules without using real sensitive info. This is especially useful in industries like healthcare, finance, the public sector, and defence.
History of Synthetic Data and How it is Evolving
Stats: As per a study, the global synthetic data market is expected to grow from $215 million in 2023 to over $1 billion by 2030, with a rapid 25.1% annual growth rate.
Synthetic data may look like a new term — but it is not entirely new.
It started in the 1970s
During the early days of computing (1970s and 1980s), researchers and engineers used computer simulations to generate data for physics, engineering, and other scientific domains where real measurements were difficult or costly.
One notable example: flight simulators and audio synthesizers produced realistic outputs from algorithms.
The 1990s paved the way ahead
The modern concept of synthetic data (generating data for privacy and machine learning) started around the 1990s. In 1993, Harvard statistician Donald Rubin suggested a new idea: create fake data that looks real to protect people’s privacy.
He proposed that the U.S. Census could use a model trained on real data to generate new, similar data (with no personal details of the public included).
In 2010, it grew roots around AI
As AI started to grow fast, synthetic data became more important in the 2010s. To train deep learning models, huge amounts of data were needed — but collecting and labeling real images was expensive. So, teams began creating fake images using tools like 3D models to help train their AI.
2015 and the Present
Synthetic data generation is evolving because of modern generative AI.
Transformer-based models and GANs can produce convincing synthetic text, images, and even video.
Hybrid approaches are used to generate synthetic data to boost the diversity of datasets.
The legal rules around synthetic data are still evolving and they vary a lot from country to country. There’s no single global law focused only on synthetic data yet. Instead, companies must follow existing data protection laws (like GDPR in Europe or PDPA in Singapore), based on where the data comes from. These laws cover how data is collected, used, and stored. If synthetic data is created from personal information, privacy safeguards like anonymization or differential privacy must be used.
Since rules differ across regions, it’s important to:
Understand which country’s laws apply
Use privacy-safe techniques
Stay up-to-date with new AI and data regulations
Benefits of Generating Synthetic Data
If you’re wondering, “what is the main benefit of generating synthetic data?” then understand that it has many. Generating synthetic data offers many practical advantages over real data. Here are a few notable ones:
1. Get Unlimited & Customizable Data
You can generate synthetic data at any scale that fits your needs. Instead of waiting to collect new real-world examples, you can instantly generate as much data as needed. This speeds up AI model development and lets organizations experiment with new scenarios without delay.
2. More Privacy and Compliance
Since synthetic data contains no real personal information, it can be used without exposing privacy. Industries with strict data laws (healthcare, finance, public sector, and others) can use synthetic data as it provides the same statistical insights as real data while checking all regulatory requirements. In sensitive fields like genomics or healthcare, synthetic data copies the patterns of real data but uses fake identities. This lets teams safely share and test data without risking anyone’s privacy.
3. Save Costs and Time
Collecting and producing real data is expensive and takes a lot of time. With synthetic data generation, the costs and timeline can be cut down by eliminating the need for data collection and manual labeling. For example, manually labeling an image can cost a few dollars and take some time; while generating a similar synthetic image costs just a few cents and can be generated in seconds.
4. More Data Diversity and Bias Reduction
One of the major benefits of synthetic data is that it can include rare cases or underrepresented groups that may be missing from real datasets. This helps reduce bias and allows AI models to handle unusual or unexpected inputs better—something that’s often not possible with real data alone. As a result, the AI performs more accurately in real-world situations. Since diversity is a built-in feature of synthetic data generation, you can balance classes or create rare scenarios. Example: In Banking, synthetic data can identify unusual fraud patterns to reduce bias in your AI models.
5. Better Control Over Quality and Safer
Since synthetic data is created in a controlled way, it can be made cleaner and more accurate than real data. You can add rare cases or special situations on purpose — like extreme weather for sensors or unusual medical conditions. This helps companies test systems safely, without real-world risks. In security areas, they can even simulate cyberattacks or fraud without exposing real networks. Overall, synthetic data makes testing safer and more reliable.
Types of Synthetic Data
Don’t confuse — synthetic data is not mock data.
Before AI became popular, synthetic data mostly meant random or rule-based mock data. Even today, many people confuse AI-generated synthetic data with basic mock data, but they’re very different. Synthetic data made by AI is more realistic and far more useful.
Synthetic data comes in different forms depending on what kind of AI or system you’re training. Usually, there are two main types:
a) Partial Synthetic Data
Only sensitive parts of a real dataset (like names or contact info) are replaced with fake values. The rest of the data stays real. This helps protect privacy while keeping the dataset useful.
b) Full Synthetic Data
The entire dataset is generated from scratch, using patterns and stats learned from real data. It looks and behaves like the original but contains no real-world records. This makes it safe to use without privacy risks.
Other types of synthetic data include
Tabular Data: These are similar to spreadsheet elements (rows and columns). It helps train models for predictions, fraud detection, and analysis — without using real customer records.
Text Data: Used to train chatbots, translation tools, and language models. AI generates realistic messages, reviews, or support queries to improve systems like ChatGPT or virtual assistants.
Audio Data: Synthetic voices, sounds, or speech are created to train voice assistants and speech recognition tools. For example, Alexa uses synthetic speech data to improve understanding in different accents and tones.
Image & Video Data (Media): AI-generated visuals train systems in face recognition, self-driving cars, or product detection. For example, Waymo uses synthetic road scenarios to test vehicle safety.
Unstructured Data: This includes complex combinations like video + audio + text (e.g., a news clip with captions). It’s useful in advanced fields like surveillance, autonomous systems, and mixed-media AI tasks.
What Are Synthetic Data Generation Tools and Technologies?
There are many tools and techniques for generating synthetic data. The right choice depends on your use case, the type of data you need (text, images, tables, etc.), and how sensitive your real data is. Here are a few tools & technologies used for generating synthetic data:
Large Language Models (LLMs): Used to create synthetic text, conversations, or structured data based on training inputs.
Generative Adversarial Networks (GANs): Two neural networks work together to generate data that looks real. Commonly used for images, videos, and tabular data.
Variational Autoencoders (VAEs): This model compresses real data and recreates new versions that keep the same patterns and structure.
Statistical Sampling: You can create data manually using known patterns or distributions from real-world datasets.
Rule-based Simulations: Generate data by defining business logic or event-based rules.
Syncora.ai’s Agentic AI: This platform uses intelligent agents to generate, structure, and validate synthetic data across multiple formats. It is faster, safer, and privacy-friendly.
Some tools are better for privacy, while others are designed for high realism or specific formats. Whether you’re building AI for healthcare, finance, or retail, picking the right generation method is important to create safe, high-quality, and useful synthetic datasets.
Who can Use Synthetic Data? — Use Cases
Practically any organization that relies on data can benefit from synthetic data. Check the table below for the application for each industry.
Industry
Use Cases (Applications)
Autonomous Vehicles & Robotics
Car makers generate massive synthetic driving scenes to train self-driving AI. They can test systems safely in simulation before real-world trials.
Finance & Insurance
Banks and insurance agencies can use synthetic data to model risk, detect fraud, and meet rules. They can create fake transactions and customer behaviors to mimic real data without using confidential information.
Healthcare
Using synthetic patient data can speed up drug discovery by simulating clinical trials. AI for medical imaging is trained on artificial X-rays and MRIs to improve disease detection while protecting patient privacy.
Manufacturing & Industrial
Factories can use synthetic sensor and visual data to improve quality control. This helps AI spot product defects and predict equipment failures.
Retail
Retailers can use synthetic data to simulate customer behavior, test pricing strategies, and improve recommendation engines..
Government
Governments can use synthetic population data to model public services, forecast policy outcomes, and run simulations without risking citizen privacy.
Others
Synthetic data also helps in marketing (simulating customer behavior), cybersecurity (simulating attacks), and other areas.
Who can use it in a Company?
Synthetic data can be used by
Data scientists & ML engineers to train AI models & prototype quickly when real data is scarce
QA & development teams can test apps and systems under various scenarios. They can also use synthetic data to detect bugs early.
HR & business teams can simulate employee data for planning and run what-if scenarios without exposing real people.
Marketing & product teams to model customer segments or run A/B test campaigns without using real user data
How to Generate Synthetic Data?
Synthetic data can be generated by using statistical models or simulations that mimic real-world data. This involves training algorithms like GANs or rule-based engines on real datasets. This way, they can learn patterns, then produce new, similar data that doesn’t expose any actual records.
You can use tools like
Scikit-learn
SDV (Synthetic Data Vault)
Faker (Python package)
PySynthGen
Although this way of generating synthetic data is effective, this process often requires heavy manual setup, deep domain knowledge, and can be time-consuming.
There is a new approach to this.
What is Syncora.ai? How Does it Help with Synthetic Data Generation?
Syncora.ai is an advanced AI platform that automatically creates realistic synthetic data. It uses AI agents to understand what you need, then generates various types of data like tables, text, or images. You just tell it what data you want, and Syncora.ai creates it for you.
Core capabilities:
Self-generating & highly realistic: AI agents create and improve data without manual coding. You just give raw data, and it will restructure and create synthetic data that has 97% fidelity.
Fast & saves money: No ETL backlogs, and the data is generated within minutes (saves weeks of manual work) with the help of agentic AI. This helps you to launch AI faster and cuts labeling and prep costs by 60%
Trackable and compliant: Every piece of data is logged on a secure blockchain for transparency, and the process complies with HIPAA, GDPR, and other norms.
Fixes data gaps: Uses hidden or hard-to-access data without revealing personal info, giving edge to the AI model for training edge cases.
Better accuracy: The built-in feedback loop helps reduce bias and improves model performance, up to 20% better in early tests.
Syncora.ai lets you generate synthetic data without risk of privacy concerns and scaling issues. It provides secure, on-demand synthetic data and lets you accelerate your AI projects and innovate faster.
Synthetic data is changing how AI teams, data scientists, and companies access and use data. It solves problems like privacy, bias, and high data costs and makes it easier to train, test, and deploy smarter AI systems. From healthcare to finance, it’s already helping teams move faster while staying compliant. And now, with agentic AI tools like Syncora.ai, generating high-quality, privacy-safe synthetic data takes just minutes, not weeks. If you’re building AI in 2025, synthetic data isn’t just helpful, it’s essential.
FAQs
1. What is synthetic data generation software?
Synthetic data generation software creates artificial data that mimics real data. It is used to train and test AI models without using private real data. There are many software you can use, with Syncora.ai being one of the best. Syncora.ai uses agentic AI to generate high-fidelity, privacy-safe data quickly and at scale.
2. What is synthetic data in machine learning?
In ML, synthetic data is artificially created data. It is used to train, test, and improve AI/ML models. It helps fill gaps, simulate rare scenarios, and improve model performance, and is useful when real data is limited or sensitive.
3. What is synthetic test data generation?
Synthetic test data is fake data created for testing software or systems. It simulates real-world inputs to check how applications would behave, without risking real customer or sensitive data.
4. What is synthetic proxy data?
Synthetic proxy data is fake data and is used when real data isn’t available or can’t be shared. It copies the patterns of real data, so teams can test and analyze systems safely.
5. What is synthetic panel data?
Synthetic panel data mixes real and fake information to show how people or groups might change over time. It’s helpful for studies in economics or policy when long-term real data isn’t available.
Synthetic data generation is the process of generating an artificial dataset that is similar to real-world data, but it has no privacy risks.
It lets you tap into new possibilities for AI, analytics, and research. If you’ve ever felt stuck waiting for real data, or worried about privacy issues, you’re in the right place: generating synthetic data is simpler and far more practical than you might think.
In this blog, we will show you 5 simple steps to generate practical synthetic datasets. Let’s go!
Step 1: Decide What You Need Your Synthetic Data To Do
Before you start generating anything, take a moment to think about why you want synthetic data in the first place. Answer these questions:
What problem do you need to solve?
Are you training a machine learning model for fraud detection, running simulations for healthcare, or building a dashboard for developer productivity?
When you know your purpose, it will help you outline the schema, variable types, and volume of data you need. You also need to:
Define your use case: e.g., image generation for computer vision, tabular data for boosting AI model accuracy, or time-series data for predictive analytics.
List important features: What columns, fields, or events do you need? You should focus on what truly drives your analysis or model.
Set a target size: Will you need 1,000 samples or 1,000,000? Synthetic data is scalable to fit any project.
Pro tip: Write down at least 4–6 must-have variables you want in your dataset. This will help keep your process focused and efficient.
Step 2: Gather Reference Data or Use Domain Knowledge
Synthetic data will be useless if the data that you feed is not proper.
Remember that quality synthetic data generation works best when it’s based on reality. If you have access to real data (even a small sample), you can use it to analyze distributions, correlations, and edge cases. If not, rely on your domain knowledge or research to mimic realistic scenarios. Here’s how you can go around it:
Analyze real data: Look at averages, ranges, missing values, and typical feature relationships.
Use domain expertise: If real data isn’t available, talk to field experts and review published studies to capture authentic patterns.
Identify constraints and business rules: These could be things like “age must be a positive integer” or “credit limit shouldn’t exceed $50,000 for student accounts.”
Step 3: Choose Your Synthetic Data Generation Method
Now, turn your schema and research into a synthetic data generation strategy. There’s no “one size fits all”.So, you have to choose a method that matches your technical skill, purpose, and available tools. There are many options available for Synthetic data generation:
1. Rule-based synthesis
This is the simplest way to generate synthetic data. You basically define a set of “if-then” rules or even use a spreadsheet to simulate the behavior you want.
For example: If age < 18, set occupation as ‘student’. It works well for small, straightforward tasks where you want complete control and transparency.
2. Statistical modeling
Here, you go a step further. Instead of fixed rules, you generate values by sampling from probability distributions (normal, uniform, binomial, etc.).
This makes your dataset look and feel more realistic because of the natural variance it introduces. It’s useful when you already have a reference dataset and want your synthetic version to match its patterns and spread.
3. Generative AI models
This is where things get powerful. With tools like advanced models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), you can generate huge, diverse, and complex datasets.
These models actually learn from real data and then create new samples automatically. If you’re working with multimodal data (text, images, or structured + unstructured combined), this is the way to go.
4. Dedicated synthetic data platforms
This is where things get interesting. Platforms like Syncora.ai offer a complete solution for small to enterprise-level dataset generation. Syncora’s agentic workflow automates everything: schema detection, rule-building, distribution fitting, and even compliance checks.
The result? You get high-fidelity, privacy-safe data with just one click and under 2 minutes! This is perfect for teams that need scalability, speed, and want to meet strict regulatory compliance without doing all the manual heavy lifting.
It’s time to synthesize your dataset! Depending upon the data generation method you chose, you may have to follow certain processes and steps. When you’re in that process, remember that you don’t just “generate” and walk away. You need to dig in to understand what’s being created.
Run the generation: Use your code or platform to make the dataset. Whether that’s 1,000 developer productivity records, 10,000 credit card transactions, or 1M customer profiles.
Visual inspection: Check basic statistics like means, standard deviations, histograms, and missing data rates to make sure your dataset feels natural.
Advanced validation: Use tools like pandas-profiling, Great Expectations, or Syncora.ai’s automated validator to catch issues, spot outliers, and ensure realistic relationships between features.
Privacy assurance: Confirm that your dataset contains no actual personal information, is fully synthetic, and complies with privacy requirements (GDPR, HIPAA, etc.).
You can also plot a few graphs or run summary tables to spot odd patterns (e.g., negative ages, duplicate records, unrealistic values).
Step 5: Deploy or Tune And Keep Improving
You’re almost done. Now you can put your synthetic data to work.
Integrate into your workflow: Use the dataset for model training, benchmarking, dashboard development, or software testing.
Collect feedback: If you’re working with collaborators, let them review the data. Check if the features and distributions are correct and if it is truly privacy-safe. If you used Syncora for data generation, the AI agents will automatically validate your data for accuracy and edge cases. Plus, if you license your dataset on the marketplace, real validators will also validate your data.
Tune your generator: Based on feedback or test results, adjust constraints, distributions, or generation logic to fix any problems.
Document everything: Log your process, parameters, and purpose. This builds trust and repeatability for auditors, regulators, or future team members.
Why Synthetic Data Generation Matters
Synthetic data generation is a practical and ethical solution that addresses challenges such as bias, compliance requirements, privacy risks, and data access restrictions. Whether you’re concerned about privacy, struggling with data scarcity, or want to test AI models for edge cases, synthetic data puts you (and your project) in control.
Syncora.ai leads this space, making the process frictionless for everyone.
How Sycnora.ai makes the Difference
Syncora.ai is a powerful synthetic data generation tool that gives you lightning-fast data generation with automated schema structuring, gap-filling, and even edge-case simulation in minutes. With Synora.ai, your models can train on every scenario that matters.
The entire process is handled by AI agents. It includes everything from cleaning raw data to creating high-fidelity, privacy-safe datasets. Plus, with the Syncora.ai Marketplace, you can share or access curated datasets across industries. Also, you can earn $SYNKO tokens if you contribute to or validate the existing dataset.
FAQs
What is synthetic data generation, and why should I use it?
Synthetic data generation is the process of developing artificial datasets that mirror real-world patterns while protecting actual people’s privacy. You can use it to accelerate AI development, mitigate privacy issues, test edge situations, and scale trials when real data is limited.
How do I choose the right synthetic data generation method?
You can choose a synthetic data generation method as per your goals and data type:
Rule-based: if you want full control and transparency.
Statistical sampling: if you have target distributions or a small reference sample.
Generative models (GANs/VAEs/LLMs): if you need high fidelity and complex relationships.
If you want to bring all these together and need datasets that are compliant, fast, and production-ready, you can use synthetic data generation platforms like Syncora.ai.
How do I validate that my synthetic data is “good enough”?
Confirm there’s no personally identifiable information.
Perform simple sanity checks (no negative ages, realistic ranges)
You can also do peer review with domain experts.
What are common mistakes to avoid in synthetic data generation?
Do not:
Generate data without a clear use case.
Skip schema and constraints (types, ranges, business rules).
Ignore correlations (e.g., income vs. spend).
Under‑validate privacy (accidental leakage) or utility (model performance).
Forget to document parameters and versions for repeatability.
Let’s Recap
Synthetic Data Generation can be done in 5 simple steps
Decide your goals and features
Gather reference data or domain insights
Choose the right synthetic data generation method
Generate and rigorously validate
Deploy, get feedback, and refine
With these steps, you can confidently generate synthetic data, whether you’re a solo developer or part of an enterprise team. With synthetic data generation tools like Syncora.ai, you can generate synthetic data in minutes. So start your next project ethically and efficiently.
Understanding AI developer productivity metrics is important for organizations that want to optimize workflows, improve team performance, and prevent burnout.
As AI is being used more in developer analytics and team management, it’s more important than ever to work with datasets that capture focus hours, task completion, and burnout signals. But the old-age question still remains:
Where do you get real-world developer productivity data when it raises privacy concerns and ethical issues around employee monitoring?
The answer is synthetic data: it is privacy-safe, realistic, and free from compliance risks. You can generate synthetic data with tools like Syncora.ai or download a synthetic AI developer productivity dataset from GitHub below.
What is the Synthetic AI Developer Productivity Dataset About?
The dataset simulates realistic developer behaviors around
Focus hours
Coding output
Meetings
Reported burnout
It has zero risk of exposing individual identities (zero PII leaks). This makes it a privacy-safe developer analytics data source and is suitable for a wide variety of purposes, such as machine learning and behavioral research.
Each record has daily work habits and productivity markers. This will help teams and researchers understand how developers allocate their time, how burnout signs manifest, and how overall efficiency trends evolve under different workloads.
Get Synthetic Developer Productivity Dataset
The privacy-safe developer analytics data is a carefully generated collection of 5,000 high-fidelity synthetic records created with Syncora.ai’s advanced synthetic data engine.
Size: 5,000 synthetic records simulating daily developer productivity across various dimensions.
Format: Ready-to-use CSV files compatible with Python, R, Excel, and other data analysis tools.
Data Privacy: Fully synthetic with no real user data, offering zero privacy liability.
Utility: Preserves realistic relationships among variables while supporting complex modeling and analytics tasks.
Applications of This Dataset in AI and Workflow Analytics
The synthetic AI developer productivity dataset has diverse research and practical use cases:
Productivity Prediction: You can train machine learning models that forecast developer output based on task load and behavioral cues.
Burnout Detection: Build early warning classifiers for detecting developers at risk of burnout from work patterns.
Feature Engineering Practice: Improve skills in handling mixed data types and missing values through real-world-like task data.
Analytics Dashboards: Create functional productivity visualization tools for team leads and engineering managers.
AI Team Simulation: Model and test HR, time tracking, and project planning tools in simulated yet realistic environments.
In short, this dataset offers a risk-free playground for innovation in developer workflow management and well-being analytics.
How to Generate Synthetic Developer Productivity Data in 2025?
There are two approaches to generating synthetic productivity datasets:
A) Manual Method:
Start with anonymizing real-world productivity data. Next, define the key productivity and behavioral features to be included in the dataset. Carefully structure the schema, paying attention to variable types and their relationships. To generate the data, apply methods such as rule-based synthesis, statistical sampling, or generative AI models (e.g., GANs or VAEs). Follow certain processes and generate synthetic data while tuning/testing it. Finally, validate the synthetic dataset to ensure it reflects accuracy, balance, and realism.
B) Using Synthetic Data Generation Platform
An alternative and more efficient approach is to use platforms such as Syncora.ai. Start by uploading raw or schematic developer productivity data. The platform’s AI agents automatically clean, structure, and synthesize high-quality synthetic datasets within minutes. Researchers and practitioners can then download ready-to-use, privacy-compliant data to accelerate both model training and analysis.
FAQs
1) Is this dataset really privacy-safe, and can I share results publicly?
Yes. A synthetic dataset does not contain PII or real-user records, so you can analyze, publish charts, and share insights openly.
2) Can I build accurate models with a synthetic developer productivity data source?
You can build strong baseline models if the synthetic developer productivity data preserves realistic distributions and correlations (e.g., focus hours vs. task completion rate, meetings vs. productivity score). You should validate on any available real data later to fine-tune thresholds and improve generalization.
To Sum it Up
The synthetic AI developer productivity dataset offers a privacy-safe, high-realism resource for analyzing AI developer behaviors and workflow dynamics. It lets researchers, team leads, and AI developers build analytic solutions to enhance productivity, detect burnout early, and optimize team performance without legal or ethical concerns. With tools like Syncora.ai, you can generate or access such datasets quickly, or you can download a readily available privacy-safe developer analytics dataset.
Synthetic data is at the forefront of solving data-related problems, and generating synthetic data is easier than you think…
In banking and finance, credit card default prediction datasets are important. They’re used to train AI models that assess the risk of clients missing their payments, for building credit risk models, underwriting loans, and improving financial decision-making.
If you’re developing a credit default prediction model, you’ll need diverse, high-quality data; but as you might be aware that real financial data often comes with privacy risks and regulatory restrictions. That’s where synthetic data generation helps.
How to Generate Synthetic Data for Credit Card Default Datasets?
If you want a privacy-safe credit risk modeling synthetic dataset, you have two main options in 2025:
A) Traditional Synthetic Data Generation Method
Step 1: Start with real or sample data (if available). First, analyze existing credit default datasets to understand features such as demographics, credit limits, repayment histories, and default patterns. This will give you insight into realistic data distributions.
Step 2: Now, define features. Identify attributes to model by including client age, sex, education level, marriage status, past payment statuses, bill amounts, repayment amounts, and the default label.
Step 3: Next, choose a generation method. Here are a few options:
Statistical sampling that mimics real data distributions
Rules-based methods encoding domain knowledge
Generative AI models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), or GPT-based models that learn patterns from real data and create realistic synthetic samples
Step 4: Now, set up the process and start generating synthetic data. Validate it by checking statistical properties (mean, variance, etc) and ensure the appropriate balance between default and non-default cases.
Step 5: Finally, test & deploy. Use the dataset to train, evaluate, and benchmark credit risk prediction models.
B) Using Synthetic Data Generation Tool
You can generate synthetic data in 2 minutes with platforms like Syncora.ai
Upload raw or existing credit data (structured or unstructured)
AI agents clean, structure, and synthesize data patterns rapidly while preserving statistical properties and applying privacy measures.
Download ready-to-use synthetic credit card default datasets in formats like CSV or JSON. That’s it!
Get a Privacy-safe Synthetic Dataset for Credit Card Default
Our synthetic credit card default dataset is available on GitHub and offers a comprehensive collection of over 50,000 fully synthetic records from Taiwan, and is designed for credit risk modeling and AI development. It simulates real-world credit card client behavior while preserving privacy and removing any sensitive information. You can download it below
Demographics: Age, gender, education, and marital status of clients.
Payment History: On-time or delayed payments over the past 7 months.
Billing Amounts: Monthly charges for the last 6 months.
Payment Amounts: Amounts paid over the previous 6 months.
Default Status: Indicates whether the client will default next month (1 = yes, 0 = no).
What are the Applications of Synthetic Financial Datasets for AI Use?
AI teams can train machine learning models to predict if a client will miss their next payment.
Analysts can explore data to find trends in client demographics and payment behavior.
Data scientists can create new features from repayment patterns and credit usage to improve models.
AI developers can use tools like SHAP or LIME to explain what drives default risk predictions.
Teams can compare different algorithms like logistic regression or neural networks to find the best model.
Risk managers can simulate different financial scenarios to see how models perform under stress.
Educators can use this dataset to teach machine learning and credit risk concepts safely.
Developers can build and test credit risk models while keeping client data private and compliant with regulations.
FAQs
Why should I use synthetic data instead of real credit card default data?
Synthetic data doesn’t have privacy risks and regulatory compliance issues since it contains no real client information. It allows safe experimentation, AI model training, and validation without exposing PII.
Can models trained on synthetic data perform well on real-world credit default prediction?
Yes, only if the synthetic data is generated accurately and preserves statistical properties and feature relationships. When models are trained on such data, they can achieve comparable performance to those trained on real data.
Is synthetic data legal and ethical to use in financial AI applications?
Yes, synthetic data complies with privacy laws such as GDPR because it contains no real personal identifiers, making it a legal and ethical choice for developing credit risk models.
In a Nutshell
Synthetic datasets make credit card default prediction safer, faster, and more accessible. They remove privacy risks while keeping the realism needed for accurate AI models. Whether you generate them manually or use tools like Syncora.ai, you can create high-quality, ready-to-use data for training, testing, and teaching credit risk models.
Synthetic data is the way to tackle data privacy and scarcity challenges in 2025 and beyond.
In the tech industry, developer productivity metrics like focus hours, task completion rates, and burnout indicators are needed to improve team performance and well-being.
If you want to analyze AI developer workflows and burnout, the first step is getting real-world data. It can be a tough challenge as you don’t want to risk any personal data exposure. The solution is to generate synthetic data.
If you don’t want to spend time searching for real data, you can download a readily available synthetic AI developer productivity dataset from GitHub. This privacy-safe developer analytics data simulates real developer behaviors, letting you train your AI model safely.
If you want to generate synthetic data for developer productivity analysis, here are the steps.
How to Generate an AI Developer Productivity Metrics Dataset?
There are two common ways to create synthetic developer productivity datasets:
A) Traditional Synthetic Data Generation Method
Step 1: Start with real or sample data Analyze existing datasets or surveys capturing developer focus hours, daily task completions, meeting frequencies, and burnout incidence. Understanding these features will help you create realistic synthetic samples.
Step 2: Define your features. Select relevant metrics like:
Daily hours of uninterrupted deep work (focus hours)
Number of meetings per day
Lines of code written daily
Code commits and debugging time
Self-reported burnout level
Complexity of tech stack
Pair programming activity
Composite productivity score
Step 3: Choose your synthetic data generation method. Here are a few options:
Statistical sampling
Rules-based synthesis
Generative AI models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs)
Step 4: Generate synthetic records and validate quality. Using your preferred choice, start generating synthetic data. Make sure to set up the method properly and refine and tune as and when needed. You should make sure that the synthetic data matches the real data’s statistical properties, such as mean values, correlations, and variability. Also, it should not have any PII leaks.
Step 5: Test and refine your dataset. Use synthetic data to build machine learning models for productivity forecasting or burnout detection. Compare synthetic-trained models against any real data benchmarks to assess fidelity. Adjust generation parameters as needed for improved accuracy.
B) Using Synthetic Data Generation Platforms
The fastest and efficient way to generate synthetic developer productivity data is use tools like Syncora.ai. All you have to do is:
The AI agents will clean, structure, and synthesize synthetic datasets automatically.
Receive ready-to-use, privacy-safe developer analytics data in minutes. (Download in CSV or JSON formats.)
Get an AI developer productivity metrics dataset
Instantly download 5,000 privacy-safe synthetic records capturing focus hours, task completion, burnout signals, and more. It has features to predict productivity, detect burnout early, and optimize workflows.
More features: meetings, coding output, debugging time, tech stack complexity, and pair programming status
What are the Applications of Synthetic Data for AI Developer Productivity Analysis?
AI teams can train models to forecast developer productivity and output trends.
Researchers can detect early signs of developer burnout using behavioral patterns.
Managers can analyze focus hours, meeting loads, and coding output to optimize workflows.
Product teams can benchmark productivity tools and engineering systems using risk-free data.
HR analysts can simulate team changes and predict the impact on developer well-being.
Organizations can test time tracking and performance dashboards with synthetic datasets before live rollout.
DevOps teams can model the effects of scheduling, tech stack changes, or collaboration strategies.
FAQs
1) Is it safe and legal to use synthetic developer data in my research or app?
Yes. Since synthetic data does not contain any real personal or work-related details, it avoids all privacy risks and is safe for research, development, or demonstration purposes.
2. What makes synthetic developer productivity data useful for AI analysis?
Synthetic developer productivity data is designed to mimic real work patterns. This includes focus hours, task completions, and burnout signals. Since it doesn’t use anyone’s actual personal information, this lets you train and test AI models safely and ethically.
3. How accurate are the predictions from AI models trained on synthetic developer productivity datasets?
If the synthetic dataset is well-designed and reflects real-world patterns, the AI models trained with it can give results close to those built on real data. For best results, always compare and fine-tune the models against any available real benchmarks.
To Sum It Up
Synthetic data is a smart way to study developer productivity without risking privacy. It helps you analyze focus hours, task completion, and burnout patterns. Instead of struggling with sensitive or incomplete real data, you can generate high-quality synthetic datasets or download ready-made ones. With tools like Syncora.ai, you can get privacy-safe data in minutes. This makes it easier to train AI models, improve workflows, and support developers.
As per a study carried out, global credit card defaults pose significant risks for financial institutions worldwide.
As AI is integrating into many fields, including finance and banking, it’s more important than ever to train financial models using datasets that include default patterns and risk signals.
But the question remains: where do you get a real-world credit card default dataset when such data is wrapped in complex compliance regulations?
A credit card default dataset is a collection of client records and payment histories. It is used to train machine learning models to classify whether a client will default on their next payment. These datasets typically include demographic details, credit behavior, repayment history, and a binary target indicating default or no default.
Traditionally, these datasets use real client data, which raises privacy concerns and makes it hard to comply with regulations like GDPR and other financial laws. Synthetic data generation bridges this gap by producing privacy-safe credit data that closely resembles real-world distributions without exposing sensitive information.
Where to Get the Synthetic Credit Card Default Dataset?
You can get a credit risk modeling synthetic dataset generated with Syncora.ai for free below. It is a high-fidelity synthetic financial dataset designed for AI, machine learning modeling, and credit risk assessment and is privacy-safe and compliant with GDPR and other laws.
Our synthetic financial dataset for AI is modeled after the widely used UCI Credit Card Default dataset from Taiwan, but removes all privacy risks by generating entirely synthetic records. Below are features of our free downloadable dataset:
LIMIT_BAL: Credit limit of the client (numeric).
SEX: Gender indicator (1 = male, 2 = female).
EDUCATION: Educational level.
MARRIAGE: Marital status (1 = married, 2 = single, 3 = others).
AGE: Age in years (integer).
PAY_0 to PAY_6: Past monthly repayment status indicators (categorical, -2 to 8).
BILL_AMT1 to BILL_AMT6: Historical bill amounts for the last six months (numeric).
PAY_AMT1 to PAY_AMT6: Historical repayment amounts for the last six months (numeric).
default.payment.next.month: Target variable (0 = no default, 1 = default).
All records are synthetic, but keep the real-world patterns needed to build strong credit risk models.
Dataset Characteristics and Format
This synthetic financial dataset for AI replicates realistic credit card client behavior while ensuring 100% privacy safety. Here are a few characteristics of this dataset:
Size: 50,000 fully synthetic records modeled on real-world credit risk patterns.
Variables: Includes demographics (age, sex, education, marital status), credit behavior (limits, bill amounts, repayment status), and a binary target indicating default (0 = no default, 1 = default).
Type: Privacy-safe credit data generated using advanced AI synthesis, with statistical properties aligned to real datasets.
Format: Ready-to-use CSV compatible with Python, R, Excel, and other data tools.
Data Balance: Maintains a realistic target class distribution for the dataset for classification use cases.
Utility: Preserves feature relationships for accurate machine learning model training and testing.
Compliance: 0% PII leakage.
Common Banking and Finance AI Use Cases with This Dataset
With the credit card default database, you can
Build binary classification models (logistic regression, random forests, XGBoost, or neural networks) to predict default risk.
Create new features like credit usage, payment consistency, and bill changes to improve accuracy.
Use LIME or SHAP to understand which factors influence default risk.
Compare accuracy, precision, and recall across different models.
Use it for educational purposes.
How to Generate Synthetic Credit Card Default Data in 2025?
You can create credit card default datasets in two ways:
A) Manual Method:
Start with real or sample data (if available).
Pick the features you want, like demographics, payment history, or credit usage.
Create synthetic samples using rules, statistics, or AI models like GANs.
Check the data for accuracy, balance, and realism.
AI agents instantly clean, structure, and generate synthetic data.
Download a ready-to-use, privacy-safe credit card default dataset in minutes.
FAQs
What is synthetic credit card default data, and how is it different from real credit card data?
Synthetic data is artificially generated data that mimics the patterns, distributions, and relationships found in real credit card default data but contains no actual customer information. Because of this, no privacy concerns or regulatory compliance issues arise while using data.
Can synthetic data be used to improve credit risk prediction in practical financial institutions?
Yes, synthetic data allows financial institutions to safely develop, test, and refine credit risk models without exposing sensitive customer data.
To Sum it Up
Synthetic datasets make credit card default prediction easier, safer, and fully compliant with financial regulations. They offer realistic patterns without exposing sensitive data, making them perfect for AI training, testing, and education. Whether you create one manually or use a synthetic data generation platform, synthetic data gives you the flexibility to build accurate, explainable, and reliable credit risk models. With ready-to-use credit cards default datasets like the one from Syncora.ai, financial teams can innovate confidently while meeting compliance standards.
