> For the complete documentation index, see [llms.txt](https://trust.impact.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://trust.impact.com/transparency/model-card-partner-recommendation.md).

# Model Card Partner Recommendation

This model is an automated matchmaking tool that helps you discover new partners most likely to be productive for your program.

| **AI technology**  | impact.com Partner Recommendation Model            |
| ------------------ | -------------------------------------------------- |
| **Model type**     | Machine Learning (ranking & recommendation system) |
| **Provided by**    | impact.com                                         |
| **Classification** | Predictive AI                                      |

## Description

This is a proprietary machine-learning model developed by impact.com. It analyzes thousands of data points from across our partnership ecosystem to identify and recommend new partners who are most likely to be relevant and productive for your specific program.

## Intended use

**Primary use cases**

* Suggesting new traditional and influencer partners who haven't yet joined your program.
* Helping you discover opportunities filtered by country, industry, and promotional method.
* Accelerating program growth by surfacing high-potential partners.

**Out-of-scope uses**

* Treating recommendations as guarantees of future performance (they are suggestions).
* Using the tool as the sole factor in partnership decisions; human judgment remains essential.

## How we use your data

* **Training data.** The model is trained on aggregated, fully anonymized data from thousands of successful partnerships across the entire platform: non-personal signals such as partner attributes, historical performance trends, and program similarities.
* **Privacy-preserving design.** The model identifies platform-wide patterns of what makes a partnership succeed. It does not use or expose any other brand's or partner's private performance data to generate your recommendations. Your own data is used only to match you to those anonymized trends.

## Limitations & ethical considerations

* **Fairness.** Built on our Responsible-by-Design methodology, the model includes business rules to promote fairness (for example, verifying a partner's willingness to work with brands of different sizes), so partners have equitable opportunity to be discovered on their merits.
* **Feedback loop.** When you act on a recommendation (inviting, archiving, or declining a partner), you provide an implicit signal that helps refine future suggestions for your program.
* **Governance.** Use of this AI is governed by our internal AI governance framework and [responsible-by-design practices](/impact.com-ai-trust-center.md#responsible-by-design), which mandate Privacy, Fairness, Transparency, Accountability, Explainability, and Safety.

***

See also: [Model Card: Conversational AI →](/transparency/model-card-conversational-ai.md) and [Model Card: Newsletter Generation →](/transparency/model-card-newsletter-generation.md)


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