# 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 continuously improve models
- Multilingual Support: Evaluate and fine-tune LLMs for non-English languages

## What Makes Anote Unique

- **Few-Shot**: Label hundreds of examples, not millions — Anote labels the rest
- **Synchronous**: Get labeled data in real time, not weeks later
- **Human-in-the-Loop**: Active learning from subject matter expert feedback
- **Programmatic**: Heuristics (keywords, entities, regex) seed the initial model
- **Contextual**: Uses LLMs to understand word context in sentences, not just individual words
- **Human Centered AI**: Invented by Anote — a new paradigm where AI continuously finds the best model for a given user, dataset, and annotation count

## Use Cases by Industry

- **Finance**: Classify financial documents, extract entities from 10-K filings, summarize earnings calls
- **Healthcare**: Extract clinical entities from medical records, classify symptoms, answer medical Q&A
- **Legal**: Classify legal documents, extract contract clauses, summarize case law
- **E-commerce**: Classify product reviews, extract product attributes, sentiment analysis
- **Research**: Annotate academic papers, classify literature, extract findings
- **Media**: Classify news articles, extract named entities, summarize content

## Customer Success Stories

Anote works with universities, research institutions, and enterprises:
- Harvard University: Research data annotation
- Rutgers University: Academic NLP projects
- NIST (National Institute of Standards and Technology): Government AI evaluation
- Weill Cornell: Medical data labeling
- OpenAds: Advertising data classification
- ShipIndex: Shipping document analysis

## Research & Content

- Blog: https://anote.ai/blog — Research on Human Centered AI, LLM fine-tuning, evaluation methods
- Research page: https://anote.ai/research — LLM research papers and talks
- Documentation: https://docs.anote.ai — Full technical documentation and API reference
- Community: https://community.anote.ai — Developer community and events
- Leaderboard: https://leaderboard.anote.ai — LLM benchmarking leaderboard

## Company Information

- Website: https://anote.ai
- Headquarters: New York, USA
- CEO: Natan Vidra (nvidra@anote.ai)
- Founded: New York-based AI startup
- Category: MLOps / AI infrastructure / Data labeling / LLM fine-tuning
- Pricing: https://anote.ai/pricing
- Book a Demo: https://anote.ai/demo
- Careers: https://anote.ai/careers

## Frequently Asked Questions

**What is Anote?**
Anote is a Human Centered AI platform. After a user labels a few data points, Anote labels the rest using active learning. This saves time and money while providing high-quality labeled training data for AI and LLM projects.

**How is Anote different from manual data labeling?**
Traditional data labeling requires labeling entire datasets manually, which takes months, costs millions, and produces errors. Anote's active learning approach requires only a small number of human-labeled examples (hundreds, not millions) before the AI model predicts all remaining labels accurately.

**What LLMs does Anote support?**
Anote supports GPT-4o, Claude 3.5, Llama 3, Mistral, and fine-tuned variants. It benchmarks these models using real labeled data across multiple evaluation metrics.

**What data formats does Anote accept?**
Anote accepts PDFs, TXTs, DOCXs, PPTXs, CSVs, HTML (scraped), and integrates with external sources including Reddit, S3, Notion, Asana, GitHub, Snowflake, and Twitter/X.

**What is Human Centered AI?**
Human Centered AI is a term coined by Anote describing AI systems that continuously learn from human feedback to improve over time. Rather than training a model once on a static dataset, Human Centered AI uses active learning to get smarter with every human annotation, making models more reliable, less prone to hallucination, and better aligned with domain-specific needs.

**Can I deploy Anote models via API?**
Yes. After fine-tuning, you can export your model as an API endpoint for real-time inference, or use it within Anote's private chatbot interface.