Sentiment

Score how each AI model talks about a brand (positive / neutral / negative) and the verbatim phrases driving the tone.

Overview

Not just whether the models mention a brand, but how. Your agent gets a positive/neutral/negative split per model plus the phrases driving it, and an aggregate across all models queried.

Example

Ask your agent in plain English and it calls the tool for you:

# you say
How do the AI models talk about Acme CRM for startup teams? Is the sentiment positive?

The tool returns structured JSON to your agent (you never see this directly):

{
  "brand": "Acme CRM",
  "perModel": [
    { "model": "chatgpt", "positive": 0.55, "phrases": ["quick setup for small teams"] }
  ],
  "aggregate": { "positive": 0.62, "negative": 0.08 },
  "billing": { "cost": { "usd": "0.30" } }
}

Your agent reads that JSON and answers you in plain English. That reply is written by your own AI; the JSON is just what it reasons over:

# your AI replies
Mostly positive: 62% positive, just 8% negative across the models. They reach for phrases like “quick setup for small teams” and “easy to learn”. The main reservation is reporting depth for large teams, so that’s the narrative to get ahead of.

Full input and output schema: POST /v1/sentiment. For AI-model tools, pass an optional models list to narrow which models run (see per-model pricing).