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Anote is an applied artificial intelligence research company based in New York City. The company develops artificial intelligence systems and infrastructure focused on what it describes as human-centered AI, with work spanning data curation, data annotation, synthetic data generation, model training, model evaluation, retrieval-augmented generation (RAG), multi-agent systems, and private AI assistants.[1] [2]
The company was founded by Natan Vidra.[1]
Overview
[edit ]Anote develops software and research systems intended to support multiple stages of the artificial intelligence and machine learning lifecycle. Its stated areas of work include dataset creation, human feedback pipelines, model fine-tuning, inference, evaluation, retrieval systems, and on-premises AI deployments.[1] [2]
The company has published research and open-source software related to classification with human feedback, large language model performance, and retrieval methods for question answering over financial documents.
Products and projects
[edit ]Anote maintains a number of products and open-source repositories through its GitHub organization.[2] These include:
- OpenAnote, an end-to-end MLOps platform supporting data annotation, dataset creation, model training, fine-tuning, evaluation, and chatbot deployment.
- Research, a repository containing code related to Anote research publications and experiments in human-centered AI.
- Leaderboard, an evaluation and benchmarking platform for comparing large language models across datasets.
- Synthetic-Data, a repository for generating synthetic datasets across modalities including text, image, and audio.
- PrivateGPT, a private AI chatbot system intended for enterprise and on-premises deployment.
- Autonomous-Intelligence (also referred to as Panacea), a multi-agent AI framework for orchestration, reasoning, and task execution.
- Community (also referred to as Armor), a platform focused on AI research collaboration, events, and knowledge sharing.
The company also maintains experimental and educational repositories, including projects related to agentic chatbots, financial analysis, robotics, and AI education initiatives.[2]
Research
[edit ]Anote has published research in machine learning, human-in-the-loop systems, and retrieval-augmented generation.
Improving Classification Performance with Human Feedback
[edit ]In 2024, members of the Anote team co-authored Improving Classification Performance with Human Feedback: Label a Few, We Label the Rest, which presents a framework for improving classification accuracy using human feedback and limited labeled data.
Enhancing Large Language Model Performance to Answer Questions and Extract Information More Accurately
[edit ]In 2024, Anote-affiliated authors published Enhancing Large Language Model Performance to Answer Questions and Extract Information More Accurately, a paper focused on improving large language model performance for question answering and information extraction tasks.
Improving Retrieval for RAG-Based Question Answering Models on Financial Documents
[edit ]In 2024, members of the Anote team published Improving Retrieval for RAG-Based Question Answering Models on Financial Documents, which examines retrieval improvements for retrieval-augmented generation systems operating on financial documents.
Open-source and educational work
[edit ]Anote publishes software and educational materials through its GitHub organization.[2] Its repositories include projects related to synthetic data generation, benchmarking, private AI assistants, multi-agent systems, robotics, and machine learning training materials.
The organization also maintains educational repositories used for AI training initiatives and fellowships.[2]
Research papers
[edit ]- Improving Classification Performance with Human Feedback
- Enhancing Large Language Model Performance to Answer Questions and Extract Information More Accurately
- Improving Retrieval for RAG-Based Question Answering Models on Financial Documents