Generate statistically constrained synthetic data in seconds with validation.
StatSynth AI is a statistical synthetic data generation engine built around constraint-based data simulation rather than simple random data generation. The core technology can generate structured synthetic datasets based on user-defined sample sizes, variable types, ranges, distributions, categorical proportions, correlations, regression relationships, group differences, reliability-related requirements, and other measurable statistical constraints. For common workloads, datasets can be generated and validated within seconds with very low marginal compute cost. Current capabilities include: Custom sample sizes and variable structures Continuous, categorical, and Likert-scale data Custom value ranges and distributions Correlation constraints Regression relationships Group differences Reliability and validity analysis KMO and Bartlett's test Descriptive statistics Pearson correlation analysis Regression analysis Multicollinearity testing including VIF / Tolerance XLSX, CSV, and SPSS-compatible outputs The project also includes an AI-powered questionnaire generation module. Users can describe a research topic, variables, expected relationships, sample size, and analysis requirements, and the system can generate a questionnaire structure before producing corresponding synthetic data and statistical validation outputs. Commercial applications The technology can be deployed as: A standalone synthetic data SaaS A paid data generation website An API-based data generation service A software testing data platform A statistical simulation tool A machine learning test-data generator A data analysis service backend A buyer can integrate user accounts, payment systems, subscriptions, order management, API billing, or automated file delivery to build a fully automated commercial service. What is included Full project source code Core synthetic data generation algorithms AI questionnaire generation module Existing web/backend components included in the project Configuration and environment files Deployment instructions Example generated datasets Example statistical analysis outputs The primary value of the project is the statistical constraint generation engine, not the size of the codebase or the frontend design. Current limitation: SEM, CFA, and PLS-SEM specific model-fit constraints are not currently supported. All generated datasets are synthetic/simulated data and should not be represented as real collected observations.
Project details
Next step: <p><strong>The buyer can turn the existing technology into a standalone SaaS, API service, data-generation platform, or integrate it into an existing analytics or AI product.</strong></p><p><strong>Potential expansion opportunities include:</strong></p><ol><li data-list="bullet">Add user accounts, subscriptions, payments, and automated file delivery</li><li data-list="bullet">Offer paid synthetic data generation services</li><li data-list="bullet">Launch API-based pricing based on usage or dataset size</li><li data-list="bullet">Expand support for additional statistical methods and constraints</li><li data-list="bullet">Add SEM, CFA, PLS-SEM and additional advanced statistical workflows</li><li data-list="bullet">Build enterprise testing and synthetic-data solutions</li><li data-list="bullet">Add multilingual interfaces and international payment support</li><li data-list="bullet">Integrate additional AI models for questionnaire and schema generation</li><li data-list="bullet">Create industry-specific synthetic data products for research, testing, analytics, and machine learning</li></ol><p><strong>The current core engine provides a lightweight foundation for further SaaS and API commercialization without requiring the buyer to rebuild the statistical generation logic from scratch.</strong></p>
For sale
Asking price: $45,000 USD
What's included
Source code, All related design work
The sale includes the complete project source code, the core statistical constraint generation engine, the AI-assisted questionnaire generation module, existing frontend and backend components, configuration files, example synthetic datasets, statistical validation outputs, and deployment documentation. I can also provide technical handover assistance to help the buyer understand, deploy, and continue developing the system.
Reason for selling
I am busy with other things and no longer have time to maintain this project.
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