Expert Multimodality Data Built for Frontier Standards
Structured datasets for audio, vision, and interface agents. Start with sample data to validate fit before scaling to a full pack.
Building robustness across modalities
Turing’s multimodality data packs address the hardest problems in audio, vision, and interface interaction. From ASR and voice cloning to GUI supervision and vision-language benchmarks, these datasets are designed to stress-test models where generic data falls short—ensuring reproducibility, traceability, and research-grade standards.
Structured datasets for audio, vision, and interface agents
Each data pack is available as a sample dataset. Samples are designed to validate scope and quality before engagement on full volumes.
ASR (noisy prompts)
Full-duplex audio to audio
Voice cloning
Text-to-speech
GUI agent process supervision
Video game gameplay data
Human critique of STEM multimodal models
Multimodal STEM VQA
Multi-document QnA RAG samples
VLM Benchmark
Standards trusted by frontier AI labs
Accelerate multimodal performance in your LLM
R&D-driven standards
Criteria and taxonomies aligned with research use.
Transparent, auditable pipelines
Trace every data point end-to-end.
Elite, domain-specific talent
PhDs, Olympiad-level specialists, and vetted SMEs.
Human-in-the-loop + AI feedback loops
Combined review to catch edge cases and ensure reproducibility.
Accelerate multimodal performance in your LLM
Talk to our experts and explore how Turing can accelerate your audio, vision, and interface-driven research.
Ready to expand your model capabilities with expert data?
Get data built for post-training improvement, from SWE-Bench-style issue sets to multimodal UI gyms.
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