> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/latest/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/latest/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy_optimised-match.md).

# FUZZY\_OPTIMISED Match

Same matching quality as FUZZY with significantly lower time. The production-scale choice when FUZZY accuracy is needed on large datasets.

### What `FUZZY_OPTIMISED` does

`FUZZY_OPTIMISED` produces the same matching results as `FUZZY` - it handles typos, abbreviations, transpositions, and real-world variation on name, addresses, and free-text fields.

The scoring pattern is exactly the same as `FUZZY` - their results are not affected. The only difference is computation time: `FUZZY_OPTIMISED` runs in roughly half the time `FUZZY` takes on the same data.

For harder abbreviation cases like "IBM" vs "International Business Machines", `FUZZY_OPTIMISED` needs the same help `FUZZY` does - the strings are too dissimilar on their own, so combine with `MAPPING_(company_names)` to map the representative form explicitly.

{% hint style="info" icon="right-long" %}
`FUZZY_OPTIMISED` is Enterprise only. The Community (open source) edition does not support this match type. Available in Enterprise Lite and above.
{% endhint %}

{% hint style="success" icon="right-long" %}
**Related Match types:**

* `FUZZY` - same quality, use in development and evaluation
* `EMAIL_OPTIMISED` - optimised version of EMAIL (Enterprise only)
* `ONLY_ALPHABETS_FUZZY_OPTIMISED` - optimised version for address fields (Enterprise only)

**Read more:** [Match Types](/latest/zingg-concepts/zingg-configuration/field-definition/match-types.md)
{% endhint %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.zingg.ai/latest/zingg-concepts/zingg-configuration/field-definition/match-types/fuzzy_optimised-match.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.