Great AI Needs Great Data
Great AI Needs Great Data
Don’t hire thousands of people to read PDFs, tag images, watch videos, or transcribe audio. Orbifold does it automatically. We transform unstructured video, audio, images, and documents into a single, queryable data engine — mapped to your schema and delivered via API.
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 lab integrated Orbifold’s multimodal data curation platform to transform unstructured sensor logs into synchronized, semantically aligned datasets ready for AI training. Orbifold automatically:
With clean, temporally coherent datasets, the client achieved:
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.
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 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:
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.
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 client adopted Orbifold’s multimodal data curation platform to structure and enrich its raw visual and textual fashion data. Orbifold automatically:
With Orbifold’s curated datasets, the platform achieved:
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.
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 company integrated Orbifold’s multimodal data curation platform as the foundation for its logistics AI system. Orbifold automatically:
The company achieved measurable operational transformation:
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.
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 company integrated Orbifold’s multimodal data curation platform to transform unstructured creative datasets into high-quality AI assets. Orbifold automatically:
The startup achieved measurable breakthroughs:
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.
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 company adopted Orbifold’s multimodal data curation platform to unify and structure clinical data pipelines. Orbifold automatically:
The client achieved:
By transforming unstructured healthcare data into clean, compliant AI-ready datasets, Orbifold accelerated the path to more interpretable, accurate, and scalable clinical intelligence.
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.
