Aiso API Reference
Understand the input and output structure of the Aiso service. Learn how to query AI search conversations and extract valuable metadata insights.
Input
A natural language topic or query string that describes what you want to search for in AI conversations.
Examples:
- •
"best perfume for men" - •
"sustainable fashion brands" - •
"cloud hosting solutions"
A website domain to track brand visibility and mentions across AI search conversations.
Examples:
- •
"getaiso.com" - •
"example.com" - •
"brand.com"
Output
The output includes conversations from various AI search platforms including:
Each conversation includes the initial prompt and the assistant's answer, as well as any follow-up questions that come after that. The median, most common type of iteration is just one term, but some of them are in their hundreds of terms; some people seem to never start a new chat tab!
Conversation Structure:
Location
Geographic location information extracted from the conversation context and the IP address of the person who shared the conversation.
Intent
The user's intent or purpose behind the query. Categories include:
Product Mentioned
Products or services mentioned in the conversation.
Brands Mentioned
Brand names and companies referenced in the conversation.
Personas
Demographic and psychographic information about the user, including buyer/seller types and market segment.
Gender and age group:
Buyer/seller type (demand = buyer wanting to purchase, supply = seller seeking competitive intelligence):
Market segment (B2B = company purchase, B2C = individual purchase):
Summary
A brief summary of the conversation content and key points.
Topic Categories
Automated categorization into predefined topic clusters.
Example Payload
{
"conversations_content": [
{
"user": "What are the best perfumes for men?",
"assistant": "Here are some highly rated men's perfumes..."
}
],
"metadata": [
{
"country": "US",
"timestamp": "2025-01-15T10:30:00Z"
}
],
"extracted_metadata": [
{
"age_group": "25-34",
"b2b_or_b2c": "B2C",
"brand": ["Dior", "Tom Ford", "Creed"],
"country": "US",
"gender": "male",
"intent": "commercial",
"product": "men's perfume",
"summary": "User seeking recommendations for men's fragrances",
"supply_or_demand": "demand",
"topic_categories": ["fashion", "lifestyle", "personal-care"]
}
],
"similarity_scores": [0.95, 0.87, 0.82],
"rank_results": [
"getaiso.com/perfume-guide",
"example.com/mens-fragrance"
],
"status": "success"
}Fields we are working on
Sentiment Analysis
Positive, negative, or neutral sentiment detected in conversations
Language Detection
Primary language of the conversation (en, de, es, fr, etc.). We have conversations where languages switch sometimes within the same turn, so this is surprisingly not straightforward!
Query Complexity
Simple, moderate, or complex query classification
Purchase Funnel Stage
Awareness, consideration, decision, or post-purchase
Content Type Preference
Product reviews, comparisons, tutorials, or general information