Mobius provides logic-first, SME-validated data labeling that powers production-ready AI for BFSI and regulated enterprise use cases. Our approach ensures training data is transparent, traceable, and engineered for real-world deployment.
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This interface illustrates a data labeling environment where users select and categorize specific elements from a live website to build structured datasets. It highlights the process of mapping raw web content to predefined data tags like URLs, images, and product categories.
Power visual AI systems with pixel-level accuracy. Mobius delivers production-grade annotations, including bounding boxes, polygons, semantic segmentation, keypoints, and 3D point cloud labeling. Our workflows support demanding use cases across autonomous systems, medical imaging, and retail visual intelligence, where precision is non-negotiable.
Move beyond surface-level sentiment analysis. We design custom GenAI-enabled workflows to label complex linguistic structures such as entity relationships, policy hierarchies, and contextual metadata. Our experts annotate for intent classification, Named Entity Recognition (NER), and semantic nuance across multiple languages and domains.
Eliminate black-box risk from your training data. Unlike traditional BPO-driven annotation, Mobius provides a full Logic-Trace for every label. Powered by our XDAS framework, we document the reasoning behind AI-assisted and human-verified annotations, ensuring compliance, traceability, and bias mitigation at the dataset level.
Transform legacy and unstructured data into vector-ready assets. Mobius goes beyond labeling to chunk, tag, and semantically structure content specifically for Retrieval-Augmented Generation (RAG). The result: enterprise copilots and GenAI applications that retrieve the right context and deliver accurate, explainable responses.
Our proprietary automation engine pre-labels up to 90% of data using domain-specific logic, reducing time-to-market while preserving HITL governance.
We don't rely on generic crowdsourcing. Your data is handled by experienced Subject Matter Experts (SME) who understand domain-specific distinctions.
Our workflows integrate seamlessly with your MLOps ecosystem via robust APIs, enabling model retraining, version control, and dataset refresh at scale.
Multi-layer quality assurance, consensus scoring, and full audit trails ensure your ground truth is reliable, defensible, and truly production-ready.
Moving AI from the lab to live financial environments requires more than data volume; it demands accountability, explainability, and security.
Every label is backed by documented reasoning through XDAS, creating a defensible audit trail for internal risk teams and external regulators.
Our SOC 2-compliant infrastructure, combined with strong access controls, ensures sensitive financial data remains fully protected at scale.
Annotations are reviewed and validated by career professionals in banking, finance, and insurance, not anonymous gig-economy workers.
Our logic-driven labels reduce black-box risk and support compliance with regulatory frameworks such as the EU AI Act and transparency mandates.