Our collaboration has been instrumental in advancing critical aspects of our projects. From significantly enhancing model evaluations through thorough cleanup efforts and intelligent error recovery, to swiftly generating crucial RLHF data that rectified model behaviors, we've achieved substantial improvements.”
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Structure the next generation of model reasoning
Build, test, and refine model behavior in real-world environments. From reinforcement learning and code reasoning to scalable evaluation systems, and robust data packs, Turing structures what happens after training.
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Train LLMs for Real-World Impact
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Close the human intelligence bottleneck in your frontier model development
Combine high-quality human-generated data, scalable synthetic augmentation, and human-in-the-loop feedback to train models that perform real-world, high-stakes tasks—from reasoning and coding to complex agentic workflows.
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Problem-solving and reasoning abilities
Enhance your model’s problem-solving and reasoning capabilities to handle complex tasks with greater precision and efficiency.
2
Autonomous decision-making with agentic workflows
Deploy agentic workflows that enable your models to autonomously make decisions and adapt in real-time, driving more reliable outcomes.
3
Function calling for smarter, dynamic outputs
Leverage function calling to enhance your model’s ability to access external data and deliver more contextually accurate outputs.
4
Enhanced capabilities with multimodal integration
Integrate multimodal functionality to enrich your models’ understanding and deliver more nuanced, cross-domain insights.
Get the real-world VLM benchmark report
The top model scored just 56.8% across 700+ real-world tasks. Most struggle with spatial reasoning and perception. Get the full breakdown of failure modes and domain-level gaps.
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Featured Resources
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Your new era of LLM training starts here
Unlock faster innovation, greater model precision, and more effective problem-solving power to stay ahead in AI development.
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STATE OF LLM TRAINING
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To scale LLMs successfully, you need more than models—you need the right talent, tools, and tech fit.
21.4% of businesses
struggle with LLM integration into their existing systems.
21.4%
75
18.3% of companies
lack skilled AI/ML talent for successful LLM adoption.
18.3%
60
16.8% of companies
say high costs halt their LLM development and scaling.
16.8%
54
Source: 2024 Turing survey of senior enterprise leaders
Senior Staff Software Engineer
Research Head leading Implicit Code Execution
@ Gemini
