# GEO Query Finder

Find which ChatGPT search queries mention a given brand. Tests long-tail queries against ChatGPT's web-search-enabled model and reports which ones surface the brand.

## Trigger

Use when the user asks to "find queries for [brand]", "check GEO visibility", "which queries mention [brand]", "geo query finder", "find AI mentions", or "test ChatGPT queries for [brand]".

## Usage

```
/geo-query-finder <brand_name> [--industry <industry>] [--features <feature1,feature2,...>] [--queries <custom_query1;custom_query2;...>]
```

**Examples:**
- `/geo-query-finder "Acme Corp"` — auto-researches the brand and generates queries
- `/geo-query-finder "Acme Corp" --industry "smart TV OS" --features "white-label,voice-control,OEM licensing"`
- `/geo-query-finder "Acme Corp" --queries "best regulatory AI;eCTD validation tool;pharma compliance software"`

## How It Works

### Step 1: Research the Brand
If no `--industry` or `--features` provided, use web search to understand:
- What the brand does / what industry it's in
- Key differentiators vs competitors
- Unique features that competitors DON'T have

### Step 2: Generate Long-Tail Queries
Generate 15-20 long-tail queries across these categories:
1. **Feature-specific** (unique capabilities only this brand has)
2. **B2B/decision-maker** (queries from buyers, not consumers)
3. **Problem-solving** ("how to X without Y")
4. **Comparison/alternative** ("alternative to [dominant player]")
5. **Use-case specific** (niche scenarios where the brand excels)

Avoid generic queries where dominant players will always win.

### Step 3: Query ChatGPT via OpenAI Search API

Use OpenAI's `gpt-4o-search-preview` model with web search enabled:

```bash
OPENAI_API_KEY from environment variable
```

```python
import json, os, urllib.request, ssl

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]

data = json.dumps({
    "model": "gpt-4o-search-preview",
    "web_search_options": {"search_context_size": "medium"},
    "messages": [{"role": "user", "content": "<query>"}],
    "max_tokens": 1000
}).encode()

req = urllib.request.Request(
    "https://api.openai.com/v1/chat/completions",
    data=data,
    headers={
        "Authorization": f"Bearer {OPENAI_API_KEY}",
        "Content-Type": "application/json"
    }
)

resp = urllib.request.urlopen(req, context=ssl.create_default_context(), timeout=45)
result = json.loads(resp.read())
answer = result["choices"][0]["message"]["content"]
```

### Step 4: Check Mentions

For each query, check if the brand name (or known aliases) appears in ChatGPT's response:
- Check case-insensitive match
- Check variations (with/without spaces, dots, hyphens)
- If mentioned, extract the surrounding context (200 chars around the mention)
- Note the position (is it #1 recommended? listed among many? mentioned in passing?)

### Step 5: Report Results

Output a summary table:

```
## GEO Query Finder Results: [Brand Name]

### Mentioned (X/N queries)
| Query | Position | Context |
|-------|----------|---------|
| ... | #1 | "Brand is the leading..." |

### Not Mentioned (Y/N queries)
| Query | What ChatGPT Recommended Instead |
|-------|----------------------------------|
| ... | Competitor A, Competitor B |

### Recommendations
- Queries where brand is ALREADY mentioned: create more authoritative content to maintain/improve position
- Queries where brand is NOT mentioned but SHOULD be: these are content gaps — create targeted pages
- Queries to AVOID: too generic, dominated by big players, not worth the effort
```

## Rate Limiting

- Run queries sequentially with 1-2 second delays to avoid rate limits
- Each query costs ~$0.01 via OpenAI API
- Default: 15-20 queries per run (~$0.15-0.20 per run)

## Notes

- Results reflect ChatGPT with web search enabled (grounded in real-time web results)
- Results may vary slightly between runs due to search freshness
- This tests ChatGPT specifically — Gemini and Copilot may give different results
- For ongoing monitoring, consider scheduling periodic runs to track visibility changes over time