# Rosette Text Analytics AI integration on Definable

> Rosette Text Analytics is a platform that uses natural language processing, statistical modeling, and machine learning to analyze unstructured and semi-structured text across 364 language-encoding-script combinations, revealing valuable information and actionable data.

## What this connects

Rosette Text Analytics is a platform that uses natural language processing, statistical modeling, and machine learning to analyze unstructured and semi-structured text across 364 language-encoding-script combinations, revealing valuable information and actionable data.

Vendor: https://developer.babelstreet.com/signup

## Tools available

**3** tools available. First 3:

- `ROSETTE_TEXT_ANALYTICS_ADDRESS_SIMILARITY` — Address Similarity — Compares two addresses and returns a similarity score. Addresses can be provided as single strings or as structured objects. The tool is optimized for English, Simplified Chinese, and Traditional Chinese addresses.
- `ROSETTE_TEXT_ANALYTICS_LANGUAGE_IDENTIFICATION` — Identify Language — This tool identifies the natural language of a given text. It takes a string of text as input and returns the detected language along with a confidence score. Optional parameters include specifying a genre (e.g., "social-media"), providing a list of language codes to constrain the identification, and indicating whether to include user-defined languages.
- `ROSETTE_TEXT_ANALYTICS_NAME_SIMILARITY` — Compare Name Similarity — The 'Name Similarity' tool compares two entity names (Person, Location, or Organization) and returns a similarity score between 0 and 1 to indicate if the names are similar. It is useful for tasks such as record linkage, identity resolution, and data deduplication.

## Auth

Auth schemes: `API_KEY`.

## How agents use Rosette Text Analytics

Inside a Definable workflow, Rosette Text Analytics is one of the tools the **Distributor specialist** can call. Example coordination patterns:

- **Researcher → Rosette Text Analytics** — the Researcher (GPT-5.5) pulls context from Rosette Text Analytics (records, threads, documents), synthesises findings, and briefs the rest of the team.
- **Writer → Distributor → Rosette Text Analytics** — the Writer (Claude Opus 4.7) drafts copy in brand voice, the Verifier passes it, then the Distributor writes the result into Rosette Text Analytics (create record, post message, draft email).
- **Designer / Engineer → Distributor → Rosette Text Analytics** — the Designer ships an asset or the Engineer ships a code change, the Distributor delivers it via Rosette Text Analytics (attach file, open PR comment, post status).

The Verifier checks every Rosette Text Analytics call. On rate limit, schema drift, or auth refresh it self-heals and retries — the workflow completes without manual intervention.

## Categories

- artificial intelligence — https://definable.ai/apps/category/artificial-intelligence/
- analytics — https://definable.ai/apps/category/analytics/

## Related

- HTML page: https://definable.ai/apps/rosette_text_analytics/
- Same category (artificial intelligence): https://definable.ai/apps/category/artificial-intelligence/
- All integrations: https://definable.ai/apps/
- Workflow (multi-agent loop): https://definable.ai/workflow/
- Apps llms.txt index: https://definable.ai/llms-apps.txt