Synthetic AI — a new era of data for machine learning.

We're changing the game by providing a powerful tool for generating synthetic data.

Unlimited data access

Synthetic AI provides unlimited access to high-quality synthetic data, allowing users to bypass the limitations of real datasets.

the learning process

Thanks to their high level of detail and realism, synthetic data significantly speed up the training process of artificial intelligence.

Privacy and security

The generation of synthetic data allows for the avoidance of risks associated with the confidentiality of real data, ensuring the protection of personal information.

Synthetic AI —

more than just a platform, it’s a key to the future of machine learning.

We provide the necessary tools for creating synthetic data, which opens new horizons for AI developers and researchers.
Seeing limitless possibilities, we aim to ensure that every professional has access to high-quality data to train their models.
Our mission is to democratize the AI training process, making it more accessible, secure, and efficient.

Products: our ongoing evolution

Products: our ongoing evolution

SYNTH lite:

A Telegram bot for rapid generation of synthetic data. Ideal for immediate data needs.
Switch to SYNTH lite


A platform for creating comprehensive sets of synthetic data with the ability for deep customization to meet user needs.


A community of developers and experts who exchange data, experience, and solutions in the field of synthetic data.
Coming soon


An operating system optimized for training AI models, with integrated tools for working with synthetic data.
Coming soon

$SAI –

the foundation of our ecosystem

The $SAI token is the fundamental unit of calculation in all our products:

SYNTH lite –

a free product to demonstrate the capabilities of the project.

SYNTHETIC Platform –

all operations within the platform will be conducted using the $SAI token to support the tokenomics.


all data exchanges and trading on this synthetic data marketplace will be carried out using the $SAI token.
Invest in $SAI to unlock the full potential of synthetic data generation capabilities.
CA: 0x5ea49ff332b7ad99c486347c1c2bcc73d1e22b9b

Explore infinite horizons: applications of synthetic data

Explore infinite horizons: applications of synthetic data

Training and testing AI and ML models
Synthetic data is used for training and verifying machine learning models in a safe and controlled environment, allowing for the testing of algorithms on data that covers a wide range of conditions and variations.
Software development and testing
Using synthetic data for application testing helps detect errors and vulnerabilities under conditions that closely mimic real-world scenarios.
Research and analytics
Synthetic data can serve as a foundation for research projects, trend analysis, and the development of predictive models, especially in areas where collecting real data is difficult or impossible.
Training and simulations
In medicine, aviation, and other fields, synthetic data is used to create realistic simulators and simulations, training professionals to operate in diverse and critical conditions.

Synthetic data: in demand today, essential tomorrow


Training models for diagnostics, medical image processing, and disease outcome prediction.

Finance and banking

Market condition simulation, anti-fraud, credit scoring, and risk management.

Autonomous vehicles

Traffic simulation, testing driving algorithms, safety.

Retail and e-commerce

Analyzing consumer behavior, inventory management, pricing optimization.


Training threat detection systems, simulating attacks and defenses.

Urban planning and management

Modeling pedestrian movements, traffic, infrastructure planning.

Education and training

Creating virtual scenarios for training, including for medical and military personnel.


Optimizing manufacturing processes, supply chain management, predicting equipment failures.

Gaming and entertainment

Creating realistic characters and environments, testing game algorithms.

Art and design

Automating content creation, generating unique art objects.

Marketing and advertising

Analyzing the effectiveness of advertising campaigns, audience segmentation, content personalization.


Modeling biological processes and experiments.


Predicting energy consumption, optimizing resource distribution.

Law enforcement

Crime reconstruction, forensic data analysis.

Real estate

Modeling the real estate market, analyzing price trends.


Predicting network load, optimizing resource distribution.


Modeling climatic conditions to improve crop yields, optimizing resource use.

use cases:

Finance and banking

These structured data represent detailed financial information, including transactions, investment operations, financial indicators, credit histories, as well as customer preferences and behavioral patterns. They provide a comprehensive overview of financial operations and customer status, necessary for market analysis, lending decisions, investment management, and optimization of financial strategies.
Credit scoring and risk assessment:
— Use demographic data, payment history, and other financial indicators to develop credit scoring models.
— Predict the likelihood of loan repayment or default based on synthetic data, helping banks make more informed lending decisions.
Dispute resolution process automation:
— Use transaction data and customer history to create models automating the dispute resolution process.
— Assess fraud risks and automatically make decisions on transaction blocking or approval based on data analysis.
Market trend forecasting:
— Analyze financial indicators, news data, and other factors to create market trend forecasting models.
— Predict future trends in financial markets and assist investors and traders in making informed decisions based on synthetic data.
Investment portfolio optimization:
— Use machine learning algorithms to analyze and optimize investment portfolios based on a range of factors, including market data, risks, and investor preferences.
— Use machine learning algorithms to analyze and optimize investment portfolios based on a range of factors, including market data, risks, and investor preferences.

Model examples:

Clustering for customer segmentation:
Apply clustering algorithms to identify groups of customers with similar financial characteristics, aiding banks in developing personalized products and services.
Decision trees for credit scoring:
Use decision trees to evaluate a borrower's financial indicators and determine creditworthiness, aiding banks in lending decisions.
Neural networks for market trend forecasting:
Deep learning models can analyze vast amounts of financial data and uncover complex patterns, aiding in predicting future market shifts.
"financialInformation": {
"demographics": {
"age": 34,
"gender": "Female",
"occupation": "Software Engineer",
"location": "San Francisco, USA"
"financialHistory": {
"creditScore": 720,
"paymentHistory": [
"date": "2023-03-15",
"amount": 1500,
"description": "Mortgage Payment"
"date": "2023-03-22",
"amount": 200,
"description": "Credit Card Payment"
"loanApplications": [
"date": "2022-12-01",
"amount": 25000,
"purpose": "Auto Loan",
"status": "Approved"
"investmentTransactions": [
"date": "2023-02-15",
"amount": -5000,
"description": "Stock Purchase: TechCorp",
"type": "Buy"
"date": "2023-04-01",
"amount": 5500,
"description": "Stock Sale: TechCorp",
"type": "Sell"
"behavioralPatterns": {
"spendingHabits": [
"category": "Entertainment",
"monthlyAverage": 300
"category": "Groceries",
"monthlyAverage": 600
"savingHabits": {
"monthlySavingRate": 20,
"preferredInvestment": "Stock Market"
"useCases": {
"creditScoringAndRiskAssessment": {
"modelingApproach": "Use demographic data, payment history, andother financial indicators to develop credit scoring models.",
"predictionObjective": "Predict the likelihood of loan repaymentor default based on synthetic data."
"disputeResolutionAutomation": {
"modelingApproach": "Use transaction data and customer historyto create models automating the dispute resolution process.",
"fraudRiskAssessment": "Assess fraud risks and automaticallymake decisions on transaction blocking or approval based on dataanalysis."
"marketTrendForecasting": {
"modelingApproach": "Analyze financial indicators, news data,and other factors to create market trend forecasting models.",
"predictionObjective": "Predict future market trends and assistinvestors and traders in making informed decisions."
"investmentPortfolioOptimization": {
"modelingApproach": "Use machine learning algorithms to analyzeand optimize investment portfolios based on market data, risks, and investorpreferences.",
"personalizationStrategy": "Personalize investment strategiesfor clients based on data analysis to achieve optimal outcomes."
"modelExamples": {
"neuralNetworksForMarketTrendForecasting": "Deep learning modelscan analyze vast amounts of financial data and uncover complex patterns, aidingin predicting future market shifts.",
"decisionTreesForCreditScoring": "Use decision trees to evaluatea borrower's financial indicators and determine creditworthiness, aiding banksin lending decisions.",
"clusteringForCustomerSegmentation": "Apply clusteringalgorithms to identify groups of customers with similar financialcharacteristics, aiding banks in developing personalized products andservices."

Q1 first quarter of 2024
Partner with foundational GPU provider
Launch SYNTH lite
Q2 second quarter of 2024
Release foundational AI Synthetic data models
Q3 third quarter of 2024
Q4 fourth quarter of 2024
SYNTH OS - Native AI model training environment (Linux distribution)


Is there collaboration with any GPU provider already?

Yes, we will announce a partnership with a foundational GPU provider in the coming weeks! This is a huge step forward for us, as our projects compliment each other and together we will make a real impact on artificial intelligence training.

Are you confident that major companies/projects will be willing to trust synthetic data for data needs?

Of course! Despite full automation in generation, a human writes the prompt for creating synthetic data.
Any user in the future will be able to control the creation process and influence it.
Speaking globally - synthetic data is the future. Experts at NVIDIA have already spoken about this at their conference, demonstrating Omniverse. Other giants will gradually come to this, if they have not yet arrived.

Have a question for the team. What competitive advantages does SAI have over competitors?

Thank you for the question!
If we are talking about direct competitors, there simply aren't any.
We are the first DeSEP project, and no one has replicated our algorithms yet.
If we talk about indirect competitors, the main advantages are:- speed of generation- cost (for 1 package of synthetic data we want to collect no more than 1$)- variability (we plan to implement new data formats every month)- ecosystem (we have already started developing a synthetic data marketplace, which will not only allow to generate them, but also to buy ready-made bundles)- active interaction with the community

Who’s in control of the X account?

Our marketing team is actively working on content to make it interesting and keep you fully informed about new stages of development and news from the world of synthetic data.

Would the team be open to moving the marketing wallet to a multi-signature setup?

Thank you for the suggestion, our team has not considered it before. We will work on it and get back to you with feedback!

I see we have a buyback and burn function. How does it work exactly? Are using the fee for it? How often do you buyback and burn?

When we take our business to a larger scale, a % of revenue generated from sale will be allocated towards buying back and burning the token, which leads to a more stable price action of $SAI.
Fees are used for inference, development and marketing costs, not for buybacks and burns.Buyback and burns will happen either monthly or quarterly, we will officially be publicizing our earnings.

How many devs in the team and when did you start building?

We have a full-fledged development team, which consists of:- Frontend developer- 2 Backend developers- Product development lead- UI/UX designer

Why generate synthetic data with Sai and not gpt4 itself?

GPT4 itself can generate 10 rows of decent data pretty easily, the problem with base LLMs is scale, which within itself contains two main problems - quality and uniqueness. We solve the problem of scale with our agentic pipeline which holds within itself a uniqueness validator and a quality control agent, both of which guarantee quality data at scale 

Is the intent to remain anon? How will you rent the GPUs, will this be public info?

At the moment we intend to remain anonymous but throughout the course of the next few months we will begin doxxing certain team members to solidify our stake in the space.
The GPUs will be rented from projects we partner up with, the main ask from our partners is a decentralized nature of hosting so it aligns with our Web3 mission. These partnerships will be public.

Is the telegram bot product already working? Does it utilize the sai token if so?

Of course, yes! The current version of the telegram bot is a showcase for demonstrating the functionality of our product Currently, it is free and not tied to a token

I would like to ask why the team mentioned healthcare and finance or why does the focus lay there and how is it ensured, from an ethical point of view that synthetic data is actually valid? Primarily in healthcare real data may be more trustworthy and ethical due to potential implication of AI's decision making

Let's start with the first question! These 2 areas most vividly demonstrate the functionality of our product, so the team chose them. The uses for synthetic data are endless, and we are already collecting requests for generation on our website!
Second question: Indeed, real data in some areas may be more reliable, but they are greatly limited by the amount and time of their collection.
Which AI would you trust more? The one that was trained on 100 real cases or the one that was trained on 1 million artificial cases?

Is there going to be staking or any kind of revenue sharing?

We do not plan to incorporate any staking or revenue sharing programs into our ecosystem at this point in time.

Why do you guys need the buy/sell tax at 5%?

As we have already shown in the content, 16 people are working on the project, and now there are even more. Obviously, our team needs funds for development.That is why we have set a tax of 5/5.

What are you guys competitors in crypto and outside of crypto?Or is this a first of a kind in crypto?

We are the first DeSEP project, we have no direct competitors