Entry Point AI

Entry Point AI provides a three-tool workflow for turning data into end-results. It offers Transforms to apply AI tagging, extraction, or classification across data rows, Synthetic Data to generate training examples from limited labeled data, and Fine-tuning to create a task-specific model. Each tool operates independently but can be used together to move from raw data to a model in production, within a single platform.

From prompt to production, in one loop, Entry Point AI describes a process where prompts are written, runs are scaled across thousands of rows, and improvements are tracked. It emphasizes that the three tools are not separate stops but parts of a single workflow used to iterate toward a deployed model.

Users can build a variety of data-related AI features, including reporting, tagging, data extraction, prioritization, and recommendations, and can apply these capabilities to diverse domains like content, fraud detection, moderation, and data enrichment.

Entry Point AI presents pricing and platform details, noting plans that scale with data volume and team size, data export options, provider costs being separate, and the ability to export results as JSONL or CSV.

Use cases

• Produce high-quality reports, blog articles, social media posts, emails, and more.
• Segment your data and tag content for search, metadata, or features.
• Extract key values from unstructured data in a consistent format.
• Suggest products based on shopping history or cart contents.
• Train a model to detect suspicious or high-risk activity.
• Detect and flag inappropriate content in inboxes, apps, and chats.
• Populate new fields for data, like industry or custom segments for business contacts.
• Rerank a set of results by relevance in a RAG workflow.