Entry Point AI is a no-code platform for applying language models to tabular data and turning the results into a task-specific fine-tuned model. Three tools sit in one workflow — running a prompt across your data, generating the training examples you lack, and fine-tuning a model — and each can be used on its own.
Transforms add an AI column to a dataset: you write a prompt, run it across thousands of rows, and watch results fill in for tasks such as tagging, extraction, and classification. Every completion is captured as you go, so the output of a transform doubles as training data.
When there are too few labelled examples to train on, synthetic generation expands the ones you have into a full training set. You review the generated rows and approve the ones that pass, and approved rows are routed automatically into training or validation splits.
Fine-tuning then trains a task-specific model on that data, and the platform compares its quality against a prompt on a general model over a held-out set before you switch over. Deployment is a base-URL change: you point your API calls at the fine-tune and keep the rest of the integration as it is.
Documented use cases include content generation, tagging and classification, structured extraction from unstructured text, triaging support issues and bug reports, product recommendation, risk and fraud scoring, content moderation, data enrichment, and reranking retrieved results in a retrieval-augmented generation workflow.
Features
- Prompt transforms: add an AI column that tags, extracts, or classifies every row, run at scale across thousands of rows
- Captured completions: every generated result is stored automatically and becomes candidate training data
- Synthetic data generation: expand a handful of examples into a full training set with a human approval step
- Automatic splits: approved rows route into training or validation without manual sorting
- Fine-tuning: train a task-specific model on your approved data and deploy it for production use
- Quality comparison: measure a fine-tune against a prompted general model on a held-out set before switching
- Drop-in deployment: point existing API calls at the fine-tuned model instead of the base model
- One workflow: transforms, synthetic data, and fine-tuning share a single project, and each stands alone if you need only one
