Case Study
Accelerating Physical AI with Multimodal Sensor Data Curation.
The Challenge
A leading robotics lab developing humanoid and autonomous manipulation systems was struggling to prepare high-quality training data for its physical AI models. The team’s raw multimodal sensor data—spanning RGB video, LiDAR, IMU, and force feedback—was fragmented, asynchronous, and inconsistently labeled. This made it nearly impossible to build models that could reliably link robot actions with their physical consequences, hampering real-world performance and sim-to-real transfer.
The Solution
The lab integrated Orbifold’s multimodal data curation platform to transform unstructured sensor logs into synchronized, semantically aligned datasets ready for AI training. Orbifold automatically:
* Aligned multimodal streams (visual, depth, tactile, proprioceptive) at the sub-frame level.
* Reconstructed interaction graphs linking agent actions to physical effects.
* Completed missing labels and harmonized inconsistent annotation schemas.
* Generated simulation-grounded augmentations to improve robustness and generalization.
The Outcome
With clean, temporally coherent datasets, the client achieved:
* 5× improvement in action-consequence prediction accuracy.
* 70% reduction in manual labeling and verification.
* 40% better sim-to-real transfer performance.
* Scaled from thousands to 2M+ curated multimodal frames.
By turning raw robotic telemetry into model-ready intelligence, Orbifold accelerated the development of embodied agents capable of perceiving, reasoning, and acting in complex real-world environments.
Read the full case study on how Orbifold powers next-generation robotic intelligence →
Case Study
Automating Insurance Claims with Multimodal AI
The Challenge
A leading property and casualty insurer planned to build a next-generation claims processing platform. Their goal was to accelerate settlement times and more accurately detect fraud. The problem was that evidence for a single claim was scattered across dozens of disconnected, multimodal files: PDF claim forms, photos of damage, adjuster notes, recorded audio statements, and CCTV video footage. Manually reviewing and connecting these files was the primary bottleneck, taking weeks per claim.
The Solution
The insurer used Orbifold as the foundational data curation engine for their new platform. When a new claim was filed, all associated files—from photos and bills to call audio and video—were sent to the Orbifold API. Orbifold automatically:
1. Ingested every file type.
2. Structured the contents, extracting policy numbers from forms, transcribing audio statements, and identifying vehicles and damage from images and videos.
3. Aligned all extracted data into a single, unified JSON record for that specific claim, complete with timestamps and cross-referenced evidence.
The Outcome
With a stream of clean, structured, and aligned data from Orbifold, the insurer’s new AI platform could function as designed. It now automatically verifies claim details against policy information, flags inconsistencies between photo evidence and audio statements, and triages claims for straight-through processing or human review. The data-bottleneck was eliminated, reducing the average claim settlement time from 2 weeks to under 48 hours.
See how Orbifold powers faster, smarter claims and financial AI →
Case Study
Powering Generative Fashion Design with Multimodal AI Data Curation
The Challenge
A leading digital fashion platform was struggling to train accurate AI models for virtual try-on and garment editing. Product data across catalogs, photoshoots, and user-generated content was inconsistent and lacked fine-grained annotations for components such as sleeves, collars, and textures. As a result, generative fashion tools often produced unrealistic edits and inconsistent styling, limiting both customer engagement and model reliability.
The Solution
The client adopted Orbifold’s multimodal data curation platform to structure and enrich its raw visual and textual fashion data. Orbifold automatically:
* Paired catalog imagery with real-world visuals through semantic and pose-consistent alignment.
* Generated detailed part-level annotations for garment components.
* Simulated fabric behavior and lighting variations to improve realism.
* Linked product metadata, visual features, and human-pose data into unified training records.
The Outcome
With Orbifold’s curated datasets, the platform achieved:
* 4× higher accuracy in fine-grained garment editing.
* 60% reduction in manual labeling effort.
* 25% improvement in visual realism for virtual garments.
* Seamless scaling from thousands to millions of curated fashion samples.
By transforming fragmented fashion data into structured, high-fidelity assets, Orbifold enabled next-generation generative design, hyper-personalized styling, and rapid AI iteration across the digital fashion ecosystem.
Read how Orbifold is transforming smarter fashion and retail experiences with multimodal data and AI →
Case Study
Streamlining Global Logistics with Multimodal AI Data Curation
The Challenge
A multinational logistics company managing millions of customer and vendor interactions across North America, Europe, and APAC struggled to process high-volume communications, optimize shipment routes, and scale marketing efficiently. Customer service teams manually handled thousands of daily emails and documents—slowing response times, increasing costs, and creating inconsistent customer experiences.
The Solution
The company integrated Orbifold’s multimodal data curation platform as the foundation for its logistics AI system. Orbifold automatically:
Ingested and structured diverse data types—emails, PDFs, images, and shipment documents—into unified records.
*Enabled AI-driven automation for quote generation, routing, and customer communication.
*Curated high-quality, domain-specific datasets for predictive analytics and continuous AI improvement.
*Ensured enterprise-grade data security and compliance with GDPR and CCPA standards.
The Outcome
The company achieved measurable operational transformation:
* 40% faster customer response times.
*20% fewer shipment delays via AI-powered route optimization.
*35% higher marketing engagement through personalized outreach.
*50% savings in AI training and compute costs.
By turning fragmented global communications into structured, AI-ready data, Orbifold empowered the client to deliver faster, smarter, and more resilient logistics operations—at global scale.
Discover how Orbifold streamlines global logistics with AI-driven data infrastructure →
Case Study
Powering Next-Gen Text-to-Video Generation with Multimodal AI Curation
The Challenge
A leading AI SaaS startup in the text-to-video space set out to create cinematic-quality video generation from natural language prompts. However, their datasets—spanning text, video, and motion data—were noisy, misaligned, and inconsistent. Models struggled to interpret camera movement descriptions and failed to produce realistic visual effects or consistent frame quality.
The Solution
The company integrated Orbifold’s multimodal data curation platform to transform unstructured creative datasets into high-quality AI assets. Orbifold automatically:
Extracted and structured cinematic motion metadata from real footage.
*Aligned text prompts with synchronized video and physics simulation data.
* Blended CGI and real-world effects for VFX training consistency.
* Optimized sampling for efficient training and lower compute costs.
The Outcome
The startup achieved measurable breakthroughs:
* 3× improvement in realistic camera motion generation.
*60% reduction in data preprocessing time.
* 40% enhancement in special effects realism.
* 50% lower compute costs through optimized training pipelines.
By curating multimodal creative data at scale, Orbifold enabled the client to deliver controllable, cinematic-quality text-to-video generation—bridging artistry and AI precision.
Read the full story on how Orbifold powers creative AI platforms with multimodal data infrastructure →
Case Study
Structuring Clinical Data for Multimodal AI in Healthcare
The Challenge
A global healthcare analytics company aimed to build AI systems that could synthesize insights across medical images, physician notes, lab results, and patient histories. However, the data was fragmented across formats and systems—unstructured text, DICOM files, PDFs, and sensor data—making it difficult to train reliable diagnostic and decision-support models while maintaining HIPAA compliance.
The Solution
The company adopted Orbifold’s multimodal data curation platform to unify and structure clinical data pipelines. Orbifold automatically:
Linked imaging data with corresponding reports and structured metadata.
*Extracted and normalized medical terminology across text and audio notes.
* Anonymized PHI while preserving data integrity for model training.
* Generated aligned, time-stamped datasets for multimodal model development.
The Outcome
The client achieved:
* 50% faster AI model development cycles.
*3× improvement in cross-modal diagnostic accuracy.
* 60% reduction in manual data cleaning and compliance checks.
* Seamless scaling to millions of curated multimodal medical records.
By transforming unstructured healthcare data into clean, compliant AI-ready datasets, Orbifold accelerated the path to more interpretable, accurate, and scalable clinical intelligence.
Learn how Orbifold structures clinical data for multimodal healthcare AI →
Don’t see your industry on the list?
No worries.
Orbifold works wherever data does. Whether you’re in energy, law, manufacturing, education, or any field swimming in unstructured information—text, images, video, or sensor data—our multimodal data platform helps you turn that complexity into clarity. Explore our Case Studies to see how leading teams across industries use Orbifold to accelerate AI development, improve decisions, and unlock new value from their data.
Or, speak with our engineers and learn how Orbifold can accelerate your AI journey.
