Case Studies / Multimodal AI Data
A Production Multimodal Pipeline for Document and Video Understanding
Image, video, document, and text annotations for multimodal model training and evaluation — one calibrated pipeline across four modalities with cross-modal QA.
The outcome
- 4
- Modalities in one pipeline
- 250k+
- Annotations delivered
- 99.1%
- Acceptance rate at final QA
Client context
An AI product company training a vision-language model for enterprise document and video workflows needed annotation across OCR/layout, video events, grounding, and VQA — without running four disconnected vendor processes.
Challenge
Cross-modal consistency was the blocker: the same entity had to be labeled identically in a PDF table, a screen recording, and a narrated walkthrough. Separate pipelines kept drifting apart, poisoning the training signal.
Data strategy
We built a single taxonomy spanning all four modalities, then routed every asset through modality-specialist annotators followed by a cross-modal consistency review layer that checked entity and event alignment across formats.
Workflow
- Taxonomy design — one entity/event schema across document, image, video, text
- Pilot batch — 2k assets to calibrate guidelines and edge-case handling
- Calibrated production — modality-specialist teams with shared glossary
- Cross-modal QA — alignment review across asset pairs
- Iteration — monthly guideline revisions from client feedback loops
Quality controls
Gold tasks per modality, hallucination-risk labeling for generated captions, temporal consistency checks on video labels, and domain-expert review for financial and legal document content.
Outcome
The unified pipeline delivered 250k+ annotations at a 99.1% final acceptance rate. The client's model showed its largest internal gains on cross-modal retrieval tasks — the exact area the consistency layer was designed to protect.
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