natural Connector for AI agents.

It simplifies your text by converting words to their base forms. For example, it turns 'walking', 'walked', and 'walks' into 'walk'. This helps group similar meanings together.

It improves search recall. By normalizing words before they enter your database, you ensure that a search for 'run' will find results containing 'running' or 'ran'.

Use Porter for standard normalization where you want to keep most of the word's integrity. Use Lancaster if you need to aggressively shrink your vocabulary to save on storage or costs.

Yes. By cleaning and normalizing your text locally before sending it to an LLM, you reduce the number of unique tokens the model has to process, which lowers your total API spend.

Absolutely. It's perfect for taking thousands of messy reviews and turning them into a clean list of keywords for clustering or sentiment analysis.

This capability is deterministic and local. An LLM might give different results each time or hallucinate meanings, whereas this engine uses math-proven algorithms to give you the exact same result every time.

Porter is gentler and more common. Lancaster is aggressive and creates much shorter stems (sometimes stripping prefixes/suffixes completely).

Yes! Stemming documents before embedding them reduces vector dimensionality and increases recall for different word variations.

Yes, it automatically tokenizes the string, stems each word, and rejoins them for your convenience.