Our Mission

We're building the world's first autonomous LLM engineer to democratize AI development—starting with solving high-quality synthetic data generation for every team.

The AI frontier is shifting to data efficiency: minimal real data, maximum performance through synthetic generation and reinforcement learning. Recent breakthroughs confirm our thesis: the right minimal data consistently outperforms more low quality data, rewriting traditional scaling laws.

True democratization means empowering domain experts to engineer LLMs for their specific challenges. Imagine doctors fine-tuning models for their practices or hardware engineers training models on their exact Verilog dialect. We believe alignment with use-specific data and needs—not just larger centralized models—is the critical path to widespread AI adoption.

Building specialized models today requires an impractical assembly of AI experts, ML engineers, domain specialists, data engineers, and annotators—with extensive iteration cycles.

Our solution: automating LLM data preparation through precisely engineered synthetic data. With the right synthetic data, LLMs can essentially self-train for specific use cases—master the data engineering, and the model performance will follow. While synthetic data requirements vary across industries, we're systematically conquering these challenges for all use cases.

Our team combines Stanford researchers with experienced data operators who've built synthetic data pipelines for leading AI labs and fine-tuned models for real business applications. We understand what good data looks like and what high-performing models require.

Join Us

The organizations that engineer data most effectively will reap the greatest benefits from AI. Help us transform the industry from model-centric to data-centric AI—and ultimately to autonomous AI engineering.

We're hiring – contact sonya@phinity.ai