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Anote - Human Centered AI

Anote is an AI platform that helps researchers and developers create high-quality labeled datasets and evaluate AI model results so they can improve LLMs and build reliable AI agents for real-world enterprise use.

For agents: Anote: AI platform for data labeling, fine-tuning LLMs, deploying models via REST API, and evaluating model results for enterprise AI agents.

data labelingllm fine-tuningai evaluationmodel deploymentrest api

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-22T17:16:13.021Z. Cached copy · Source on anote.ai

# Anote - Human Centered AI

> Anote is an AI platform that helps researchers and developers create high-quality labeled datasets and evaluate AI model results so they can improve LLMs and build reliable AI agents for real-world enterprise use.

Anote was founded in New York, USA by Natan Vidra. The platform uses active learning and human-in-the-loop techniques so that labeling a few examples teaches the AI to label the rest — making data annotation 10x faster than manual methods.

## Core Products

- [Data Labeling](https://anote.ai/labels): AI-assisted annotation for text classification, named entity recognition (NER), document Q&A, summarization, and sentiment analysis. Upload PDFs, CSVs, TXTs, or connect to external sources like Notion, GitHub, S3, Reddit, and Snowflake.
- [Model Training](https://anote.ai/train): Fine-tune LLMs (GPT-4o, Claude 3.5, Llama 3, Mistral) on domain-specific labeled data using LoRA, QLoRA, and RLHF. Improves model accuracy for specialized use cases.
- [AI Prediction](https://anote.ai/predict): Deploy fine-tuned models via a private chatbot or REST API. Get real-time predictions from custom LLMs trained on your data.
- [AI Evaluation](https://anote.ai/evaluate): Benchmark and compare LLM performance with human feedback. Metrics include ROUGE-L, cosine similarity, answer relevance, faithfulness, and LLM eval scores.
- [Synthetic Data](https://anote.ai/syntheticdata): Generate high-quality synthetic training data to augment datasets and reduce labeling costs.
- [Private Chatbot](https://anote.ai/enterprises): Deploy a private, fine-tuned chatbot using your own documents. Supports GPT, Claude, Llama 2, and Mistral with source citations to reduce hallucinations.

## How Anote Works (4-Step Workflow)

1. **Upload** — Import PDFs, CSVs, TXTs, HTML scraped from websites, or connect to external data sources (Reddit, Notion, GitHub, S3, Snowflake, Asana, Twitter/X).
2. **Customize** — Define your categories, entities, or questions. Set up programmatic labeling functions using keywords, regex, or entity rules.
3. **Annotate** — Label a small number of examples (hundreds, not millions). Anote's active learning AI learns from your annotations and predicts the remaining labels.
4. **Download** — Export the labeled dataset as a CSV or deploy the fine-tuned model as an API endpoint for real-time inference.

## Key Capabilities

- Text Classification: Classify unstructured text into taxonomies of hundreds of categories
- Named Entity Recognition (NER): Extract domain-specific entities from text
- Document Question Answering: Answer specific questions from long documents
- Summarization: Generate concise summaries of long-form content
- Sentiment Analysis: Classify sentiment in customer feedback, reviews, and social media
- Semi-structured Prompting: Handle complex prompting tasks with structured outputs
- LLM Fine-Tuning: Supervised fine-tuning, RLHF, LoRA, QLoRA
- Human-in-the-Loop: Subject matter experts provide feedback to conti
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