# Pre-Trained Agents

Geolava’s **Pre-Trained Agents** are specialized AI modules that leverage our unified Spatial Embedding Model to perform a range of common real estate and property analytics tasks—right out of the box. Rather than building models from scratch, you can simply call one of these Agents to get near-instant insights about a property or region.

<figure><img src="/files/UdkVbx4eUvjqKI6FQKXr" alt=""><figcaption></figcaption></figure>

### Why Pre-Trained Agents?

* **Immediate Utility**: Each Agent is tuned to handle a specific domain (e.g., valuation, condition, compliance) so you can quickly integrate Geolava’s intelligence with minimal setup.
* **Ongoing Improvements**: Our Spatial Embedding Model ingests fresh data sources (like newly available satellite imagery or updated municipal records) on a rolling basis, so Agents continuously improve over time.
* **Flexible Scope**: These Agents work at any scale—individual addresses, city-level bounding boxes, or even entire portfolios you upload.

### What’s Available?

1. **Valuation Agent**
   * Generates real-time fair-market value estimates.
2. **Condition Agent**
   * Assesses structural and environmental health (e.g., roof wear, flood damage).
3. **Compliance Agent**
   * Flags code violations or unpermitted structures based on municipal data and overhead footprint detection.
4. **Attribute Search Agent**
   * Finds properties matching specific criteria (e.g., “graffiti,” “boarded doors,” “solar panels”).
5. **Monitoring Agent**
   * Subscribes to changes or events (valuation shifts, new compliance flags, etc.), sending you alerts as soon as they occur.


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# Agent Instructions: Querying This Documentation

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

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.geolava.com/api/editor.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.