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Turing helped us solve long-standing pain points in speech model training—especially under noisy conditions and with hard-to-source locales. Their multimodal team was responsive, fast, and understood our research constraints.”
Advance multimodal AI with world-class audio and speech training
Scale multilingual speech, vision, and GUI-interaction models with aligned data and reinforcement learning pipelines. From noisy audio handling to state-of-the-art VLM benchmarks, we help frontier labs build faster, evaluate smarter, and generalize better.
30+
multimodal projects shipped
for frontier labs and FAANGs
for frontier labs and FAANGs
50+
Languages covered for
audio SFT and RL tasks
audio SFT and RL tasks
600+
modality-specialist trainers
across vision, video, audio, and GUI
across vision, video, audio, and GUI
TRAIN MULTIMODAL MODELS FOR REAL-WORLD IMPACT
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Close the human-intelligence bottleneck in multimodal model development
Multimodal benchmarks are revealing what pre-trained models can’t do—especially in speech, vision, and interface control. From accent variability and audio noise to diagram comprehension and GUI task completion, your model’s ceiling is gated by the quality and structure of its human-generated and labeled data. That’s where we come in.
1
Multilingual audio comprehension at scale
Curated voice data and reinforcement learning pipelines across 50+ locales accelerate automatic speech recognition (ASR) and speech-synthesis accuracy.
2
Vision & video reasoning datasets
High-fidelity image/video generation plus expert annotation drives factual captioning, scene understanding, and STEM-grade chart QA.
3
Cross-modal alignment & evaluation
Turing VLM-Bench 1.0 benchmarks image-text models on 700+ real-world tasks and surfaces hard-negative failures.
4
Interactive GUI & agent data
Generate rich computer-use demonstrations for agents that can click, type, and reason inside domain-specific apps.
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.
Featured Resources
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Tackling real-world multimodal training gaps
Explore how teams are addressing practical challenges across audio, vision, and GUI data—without overfitting to benchmarks.
STATE OF MULTIMODAL TRAINING
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Scaling multimodal models demands more than tokens—it demands cross-modal talent, tooling, and trustworthy data
Source: 2025 Turing Applied AGI Benchmark for VLM 1.0 Technical Report; internal analysis by Turing Research Council
Senior Research Lead
Head of Product & Engineering
@Fortune 50 Lab
