The most experienced foundation model training company
Train LLMs faster with high-quality synthetic data
Enhance your LLMs with high-quality, expert-validated synthetic data. Ensure greater accuracy, security, and adaptability for industry-specific applications.
Leading LLM companies and research organizations have trusted Turing
Bridge data gaps with human-validated synthetic training
LLMs require massive amounts of high-quality data, but real-world datasets are limited, expensive, and prone to bias. Turing solves this challenge by generating synthetic datasets tailored to your specific business use case. Domain experts rigorously review datasets, ensuring that your models receive accurate, diverse, and high-quality training data.
Synthetic data training specialties
Domain-specific synthetic data generation
Human-validated data for LLM fine-tuning
Evolutionary data refinement
Synthetic data for code and RAG optimization
Bias and risk mitigation
Automated dataset expansion and augmentation
Advanced synthetic data training starts here
Ready to train your LLMs with high-quality synthetic data?
Understanding your data needs
Collaborate with our experts to define synthetic data objectives, assess gaps in your dataset, and establish domain-specific requirements.
Team assembly and synthetic data generation
We assemble a team of skilled LLM professionals to generate high-fidelity synthetic datasets. Data analysts, model trainers, and domain leaders validate data quality and accuracy through expert curation, hierarchical reviews, and statistical benchmarks.
Iterative refinement and validation
Improve dataset accuracy using co-teaching, self-alignment, and multi-model reinforcement techniques, ensuring realism and bias-free outputs.
Scale on demand
Expand and customize synthetic data generation as your AI models evolve, supporting multi-industry LLM fine-tuning at scale.
Ready to train your LLMs with high-quality synthetic data?
Talk to our solution architects and explore how Turing’s expert-driven synthetic data training can enhance your AI models.
Cost-efficient R&D for LLM training and development
Empower your research teams without sacrificing your budget or business goals. Get our starter guide on strategic use, development of minimum viable models, and prompt engineering for a variety of applications.
“Turing’s ability to rapidly scale up global technical talent to help produce the training data for our LLMs has been impressive. Their operational expertise allowed us to see consistent model improvement, even with all of the bespoke data collection needs we have.”
Need high-quality synthetic data for LLM training?
Talk to our solution architects to generate scalable, bias-free, and industry-specific data for superior AI performance.
Frequently asked questions
Find answers to common questions about synthetic data training and how it can improve LLM accuracy, reduce biases, and enhance AI performance for industry-specific applications.
Why is synthetic data important for LLM training?
Synthetic data helps overcome real-world data scarcity, enabling AI models to be trained on diverse, cost-effective, scalable, and privacy-safe datasets.
How does Turing ensure synthetic data quality?
Our synthetic datasets undergo multi-tier human validation, statistical benchmarking, and iterative improvements to guarantee accuracy and realism.
Can synthetic data be used for domain-specific LLM fine-tuning?
Yes, we create tailored synthetic datasets for healthcare, finance, retail, and scientific research applications.
How does synthetic data reduce bias in AI models?
We generate balanced, diverse datasets to mitigate biases in real-world training data, improving fairness and ethical AI alignment.
What role does human expertise play in Turing’s synthetic data training?
Human experts curate, validate, and refine synthetic datasets, ensuring that AI models are trained on accurate, industry-specific knowledge.
Can Turing generate synthetic data for RAG and chatbot training?
Yes, we create synthetic Q&A datasets, domain-specific knowledge bases, and retrieval-augmented content for RAG-enhanced AI models.