Spatial AI (base model)

While pre-trained agents provide quick answers to common tasks, Geolava also supports a flexible inference endpoint, allowing you to make unstructured or more advanced spatial queries. Instead of naming a specific pre-trained Agent (e.g., “valuation”), you can ask the system for certain insights or let it reason about combined data in near-real time.

Overview

• Prompt-Style Requests: Submit a freeform query about properties or entire regions.

• Contextual Awareness: The system leverages the underlying Spatial Embedding (including multi-sensor imagery, historical data, and location data).

• Open-Ended Output: Get a textual or JSON response describing the system’s best inference.

Like with pre-trained Agents, you can:

• Specify a single property or

• Provide city, state, bounding polygon or

• Upload your own data (multiple addresses) and ask a general question covering them all.

Endpoint

POST /v1/spatial-reasoning

(API design still subject to change.)

Example Request

{
  "prompt": "Identify blighted properties in LA with boarded windows or doors but high sales potential"
}

Example Response

{
  "insight": "Found 12 candidate properties in Los Angeles that match the description. Four are near downtown with boarded windows, yet valuations are trending upward in these neighborhoods.",
  "matched_properties": [
    {
      "propertyId": "67a771e11bcf52fcce866d6a",
      "attributes_detected": ["boarded windows"],
      "estimated_value": 310000,
      "confidence": 0.82
    },
    {
      "propertyId": "67a771e11bcf52fcce866d6b",
      "attributes_detected": ["boarded doors, broken fence"],
      "estimated_value": 425000,
      "confidence": 0.79
    }
  ],
  "total_matched": 12,
  "notes": "Neighborhood uptrend suggests strong resale demand despite disrepair."
}

Use Cases

1. Multi-factor Queries – E.g., “List properties with severe roof damage but near new developments.”

2. Regional Summaries – Provide bounding boxes or city definitions: “In these 5 ZIP codes, show me flood-prone properties with stable valuations.”

3. Hypothetical Questions – “If an ADU is added, how does that affect the valuation or compliance status?”

4. Portfolio/Custom Data – Upload your data set of addresses, then ask: “Which properties in my dataset have known code violations and might be undervalued?”

Limits & Best Practices

• Clarity: The more specific your prompt, the more targeted the analysis.

• Rate Limits: This flexible endpoint can be heavier in compute usage. For large data sets, consider region or file-upload strategies.

• Confidence Scores: Because it processes queries in a more open-ended manner, parse the included confidence or confidence_score to gauge reliability.