Langhui Technology's Multimodal LLM data services span VLM training data, image-text contrastive alignment, spatiotemporal video annotation, audio-video alignment, and structured document annotation. We deliver high-quality, fine-grained, alignment-ready multimodal training data for vision-language models, video understanding LLMs, and document AI—benchmarked against the standards of the world's leading data service providers.
Staying aligned with the multimodal research pipelines of leading global labs—including OpenAI, Google DeepMind, Anthropic, Meta, and ByteDance—Langhui's expert team continuously transforms the latest academic breakthroughs into scalable, production-grade multimodal training data.
A new paradigm for real-time visual understanding in 2026. Its native multimodal architecture supports millisecond-level streaming interactions across speech, image, and video. Langhui provides fine-grained cross-modal alignment data and real-time feedback preference data to align with GPT-4o-grade real-time multimodal interaction.
Breakthroughs in native multimodal architecture. A unified encoder processes four modalities—text, image, audio, and video. Langhui provides cross-modal alignment training data and multimodal instruction-tuning data, supporting 1M-context long-video understanding.
A new leap for open-source vision-language models. Building on improved visual encoders and high-resolution token projection, Langhui provides fine-grained visual instruction data and high-quality image-text pairs—helping the open-source LLaVA family benchmark against closed-source frontier models.
Upgraded world-model video understanding. Langhui provides long-video spatiotemporal annotation, physical-consistency labels, and scene-context data—enabling Sora-grade video generation models to precisely model the dynamics of the physical world.
Upgraded document understanding and chart parsing. Langhui provides structured document annotation, complex table recognition, handwritten formula annotation, and mixed image-text parsing data—aligned with Claude-grade fine-grained visual reasoning for document AI.
New cross-modal alignment methods in CLIP 2.0 and BLIP-3. By combining fine-grained image-text contrastive learning with instruction tuning, Langhui provides fine-grained region-level image-text pairs and multimodal contrastive preference data—elevating cross-modal representation precision.
Four core capabilities spanning the entire multimodal LLM data production lifecycle—from fine-grained VLM training data to spatiotemporal video annotation—building a productionizable, auditable, and traceable multimodal data pipeline.
Fine-grained vision-language data production covering object recognition, region description, attribute recognition, spatial relations, visual question answering, and more—with support for region-level annotation, pixel-level segmentation, and multi-turn visual dialog.
Annotation of video actions, events, and spatiotemporal relations. Supports temporal action localization, dense event description, cross-frame object tracking, and spatiotemporal reasoning—aligned with Sora-grade world-model video understanding.
Fine-grained image-text matching and contrastive learning data. Built on the CLIP 2.0 / BLIP-3 paradigms, we deliver positive-negative contrastive pairs, region-level alignment, and hard-negative mining to enhance cross-modal representation precision.
Structured annotation for tables, charts, formulas, and layouts. Supports complex table recognition, chart data extraction, handwritten formula parsing, and mixed image-text understanding—aligned with Claude-grade document AI capabilities.
Six ready-to-use standard multimodal dataset products spanning image-text, video, document, chart, dialog, and Q&A—delivered via both subscription and customization models.
High-quality region-level image-text pairs covering fine-grained visual semantics such as object recognition, attribute description, and spatial relations—supporting LLaVA, Qwen-VL, and other VLM training pipelines.
Temporal action, event description, and video caption annotation datasets. Covers short-form, long-form, and surveillance video types—supporting video QA and temporal localization training.
Structured document annotation set including tables, formulas, layouts, and page-level recognition. Supports PDF, scanned documents, and image inputs—aligned with Claude-grade document AI capabilities.
Chart data extraction and structured annotation for bar charts, line charts, pie charts, scatter plots, and more—supporting chart QA and data visualization understanding training.
Multi-turn multimodal dialog data with mixed image + text + video input instructions—supporting GPT-4o and Gemini 2.0-grade real-time multimodal dialog model training.
Visual question answering and reasoning annotation data covering counting, attributes, spatial relations, reasoning, and other task types—each item includes correct answers and distractors, supporting VQA model training and evaluation.
From intelligent document processing to visual search, from video understanding to multimodal dialog—Langhui's multimodal data services span the full spectrum of frontier VLM applications.
Structured annotation data for document AI applications such as contract review, invoice recognition, report parsing, and tender document processing—enhancing model comprehension of complex layouts, tables, and formulas.
Fine-grained image-text contrastive pair data for visual search applications such as e-commerce product search, image-to-image search, and video retrieval—boosting cross-modal retrieval recall and precision.
Temporal action annotation, event description, and spatiotemporal relation data for applications such as short video moderation, video summarization, intelligent surveillance, and video Q&A—aligned with Sora-grade world-model understanding.
Multi-turn dialog data with mixed image and video inputs for multimodal dialog applications such as intelligent customer service, educational tutoring, and medical consultation—aligned with GPT-4o and Gemini 2.0-grade real-time interaction.
Langhui has built a quality assurance system that runs across the entire multimodal data production pipeline. From annotation accuracy to image-text alignment, video coverage, and multimodal consistency—every dimension is backed by quantifiable KPIs.
Partner with Langhui's multimodal experts to inject the latest 2026 alignment data into your VLM, video understanding, and document AI models. Whether you're a foundation model team or a vertical application team, we tailor multimodal data solutions to your needs.