Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Leverage state-of-the-art generative models to create high-fidelity synthetic datasets, bypassing data scarcity and accelerating AI development.
Launching AI initiatives often stalls due to insufficient or sensitive training data. Our Generative AI for Data Fabrication service uses advanced models like diffusion models and LLMs to create complex, structured synthetic datasets tailored to your domain, solving the cold-start problem in weeks, not months.
We deliver production-ready synthetic data that preserves statistical utility while ensuring privacy and regulatory compliance, enabling you to train robust models without real-world data constraints.
CTGAN and TVAE.GDPR, HIPAA, and internal governance.Move beyond data bottlenecks. Explore our broader capabilities in Synthetic Data Generation and Augmentation or learn how we ensure data integrity with Synthetic Data Quality Assurance and Validation.
Move beyond theoretical benefits. Our generative AI for data fabrication delivers measurable, production-ready outcomes that accelerate AI initiatives and mitigate risk.
Solve cold-start problems instantly. Generate high-fidelity, structured datasets for NLP, tabular, and multimodal applications in days, not months, bypassing lengthy real-world data collection. Launch your AI product faster.
Generate privacy-preserving synthetic data with built-in differential privacy guarantees. Our engineered datasets are statistically representative but contain no real PII, ensuring compliance with GDPR, HIPAA, and CCPA from day one.
Enhance real datasets with engineered edge cases and rare scenarios. Train more robust, generalizable models by exposing them to a wider distribution of synthetic data, reducing failure rates in production. Learn more about our approach to synthetic data for model robustness evaluation.
Eliminate the massive overhead of data acquisition, labeling, and cleansing. Our scalable synthetic data pipelines provide an on-demand, cost-effective source of high-quality training data, drastically lowering total project cost.
Share synthetic replicas of sensitive datasets with third-party developers, auditors, or global teams without security or IP concerns. Facilitate safe testing and development across organizational boundaries.
Deploy automated, production-ready synthetic data generation as part of your ML lifecycle. Ensure a continuous supply of high-quality data for model retraining and adaptation, creating a sustainable competitive advantage. Explore our synthetic data pipeline architecture services.
A structured roadmap for delivering a custom generative AI data fabrication solution, from initial scoping to production deployment and ongoing support.
| Phase & Key Activities | Timeline | Core Deliverables | Inference Systems Support |
|---|---|---|---|
Discovery & Scoping | 1-2 weeks | Technical requirements document, Data schema definition, Success metrics & KPIs | Dedicated Technical Lead, Architecture Review |
Model Selection & Prototyping | 2-3 weeks | Proof-of-concept synthetic dataset, Model performance benchmark report, Initial data quality metrics | Expert Model Tuning, Access to our Synthetic Data Platform Development tools |
Pipeline Development & Integration | 3-4 weeks | Production-ready data generation pipeline, Integration with client data systems, Automated validation suite | Full-Stack Engineering Team, CI/CD Pipeline Setup |
Validation & Quality Assurance | 1-2 weeks | Comprehensive QA report (TSTR scores, statistical fidelity), Bias & fairness audit, Adversarial testing results | Rigorous Synthetic Data Quality Assurance protocols |
Deployment & Knowledge Transfer | 1 week | Deployed solution in client environment, Complete documentation, Training sessions for client team | Deployment Support, Operational Runbooks |
Ongoing Support & Optimization | Ongoing | Performance monitoring dashboards, Quarterly optimization reviews, Access to model updates | Optional SLA with 99.9% uptime, Dedicated support channel |
Our generative AI for data fabrication delivers high-fidelity, structured synthetic datasets tailored to your domain, solving cold-start problems and accelerating time-to-market for AI products. We focus on measurable outcomes: reducing data acquisition costs by up to 70% and cutting model development timelines from months to weeks.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Get clear, specific answers to the most common questions CTOs and technical leaders ask about implementing generative AI for data fabrication.
Our methodology is a structured, four-phase engagement: 1) Discovery & Schema Mapping: We analyze your real data's statistical properties, privacy constraints, and target use case. 2) Model Selection & Training: We select and fine-tune state-of-the-art generative models (e.g., diffusion models, GANs, tabular VAEs) on your schema. 3) Generation & Validation: We produce synthetic datasets, rigorously validating them using metrics like TSTR (Train on Synthetic, Test on Real) and statistical distance measures. 4) Pipeline Integration: We deliver a containerized, automated pipeline for continuous generation and integration into your existing ML workflows. Learn more about our end-to-end approach in our guide to Synthetic Data Platform Development.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
