Entry Point AI

What is Entry Point AI?

Entry Point AI is a no-code platform for training, managing, and testing custom large language models (LLMs). You can upload data, adjust training settings, and evaluate model performance through a straightforward interface. It supports fine-tuning models from major providers like OpenAI, along with prompt engineering and retrieval-augmented generation (RAG).

Who’s Behind Entry Point AI?

Entry Point AI was created by Nathan Gyaneshwar, who is also the founder of PurchasePlus and has used the platform himself to manage datasets and test performance.

Key Use Cases

• Fraud Detection: Train a model to spot suspicious or high-risk activities.
• Data Extraction: Pull key information from unstructured text into a consistent format.
• Recommendations: Suggest products based on shopping cart contents or past orders.
• Data Enrichment: Add details like industry information or customer segments to existing data.
• Content Generation: Produce reports, blog articles, social media posts, emails, and more.
• Tagging & Classification: Segment and tag content for search, metadata, or specific features.
• Prioritization: Sort customer support issues, bug reports, or form leads.
• Moderation: Flag inappropriate content in emails, apps, or chat messages.
• Scoring & Ranking: Use a fine-tuned LLM in a workflow to reorder results by relevance.

Who Is It For?

Entry Point AI suits researchers, content creators, language model enthusiasts, tagging and classification specialists, data extraction experts, customer support analysts, developers building recommendation systems, fraud detection specialists, content moderators, data enrichment analysts, and professionals working on scoring and ranking tasks.

Getting Started

1. Sign Up: Create an account at https://app.entrypointai.com/.
2. Choose Your Plan: Plans are available for Individuals, Startups, and Businesses, with different numbers of training examples and user seats.
3. Upload Your Data: Import and manage structured data using the platform’s templating engine.
4. Fine-Tune Your Models: Set parameters and provide training examples — results are possible with as few as 50 examples.
5. Collaborate: Work with your team on datasets and model evaluation within the platform.