Level up your AI Data Pipeline

Loop combines data annotation, dataset management and model monitoring into one solution to accelerate and maximize your AI applications.

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What is Loop?

Loop is a configurable SaaS platform that facilitates the implementation of end-to-end AI data pipelines. Combining AI data ops with human-in-the-loop process to help customers build scalable, global AI-driven experiences in three easy phases.

Loop Features

AI Pre-Release Components
  • Data Annotation Platform​
  • Data Collection Platform​
  • Data Visualization
  • Dataset Management
  • Dataset Integrations (Azure, AWS S3, Google Cloud)
  • Data Globalization
Monitoring Components
  • Connect Your Own Model (OpenAI, HuggingFace, Sagemaker)
  • Model Benchmarking
  • Hallucinations Control
  • Integrated Model Playground
  • Low-code Model Monitor Integration
  • Real-time Performance Dashboards
  • Dataset Versioning Control
AI Post-Release Components
  • Relevance and Grading Platform
  • Reinforcement Learning from Human Feedback (RLHF)
  • Ground Truth Performance Comparison
  • Semi-automated Red Teaming
  • AI UX Assessment
Data Services
  • Data Collection
  • Data Annotation
  • Data Grading and Relevance
  • Red Teaming​
  • AI Localization
  • LLM Data Creation
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Who uses Loop?

Data Scientists

Loop user-friendly components allow Data Scientists to visualize data, monitor model performances, run benchmarks, and set up data annotation and data capture experiments with just a few clicks.

CTOs

Selecting the right technology for AI initiatives is key to optimizing ROI. Loop provides agnostic benchmarking across several foundational models, and integration with providers such as Azure, AWS, OpenAI, and HuggingFace.

Program Manager​

Loop gives Program Managers the power to manage the health of their AI initiatives, with a holistic view on data collection, data annotation, model monitoring and reinforcement learning from human feedback.

Data QA Managers

Loop offers QA teams with control over data annotation and collection processes with the ability to customize task interfaces, guidelines, error matrices, and identify good annotators to ensure the right KPIs are achieved.