AnswerPath GEO — Discover the Questions People Ask AI Before They Find Your Business

AnswerPath GEO is a privacy-first, open-source SEO, GEO and AEO question mining engine. Give it a business, service, website topic or keyword and it builds a transparent question map showing what people may ask an AI assistant before choosing a provider.

It separates observed questions extracted from exports and application logs from generated research prompts. This distinction matters: generated prompts are hypotheses, not evidence of what people actually asked.

Installation · Quick start · Inputs · Outputs · GEO/AEO method · Privacy · Integrations

MCP clients: Codex, Antigravity, Claude, Cursor and Cloud setup

Why AnswerPath GEO?

Traditional keyword tools show phrases typed into search engines. AI assistants receive longer, conversational questions: “Which agency is reliable for…?”, “What should I compare…?”, and “Is this service worth the price?”. AnswerPath turns the questions you already own into a usable answer-path map for content, FAQ, schema and AI visibility research.

It is designed for marketers, publishers, agencies and product teams who need to:

• discover and normalize user questions from ChatGPT, Claude, Codex, Antigravity, Cursor and chatbot exports;
• group questions by intent and decision stage;
• identify recurring questions without silently inventing demand;
• create an auditable prompt bank for GEO/AEO experiments;
• keep private conversation data on the operator’s machine.

Installation

Requires Python 3.10+.

git clone https://github.com/tmolavi/answerpath-geo.git
cd answerpath-geo
python3 -m venv .venv
.venv/bin/pip install -e .

Quick start

Create a prompt map for a service or keyword:

answerpath "طراحی سایت فروشگاهی"

The command writes answerpath-output/questions.json and questions.csv.

Analyze owned conversation data and add generated discovery prompts:

answerpath "مشاوره سئو پزشکی" \\
  --input ~/Downloads/chatgpt-export.zip \\
  --input ./support-chat.json \\
  --out ./research/seo-medical

Keep only questions actually found in your supplied data:

answerpath "سرویس حسابداری" --input ./logs --no-generated

Supported inputs

AnswerPath reads local JSON, JSONL, CSV, directories and ZIP archives. It recognizes common role=user|human|customer fields and nested ChatGPT/Claude export structures. It is compatible in principle with exports produced by tools such as openai_export_parser, llm-export-analytics, and the local multi-client history model used by ContextBridgeAI.

It does not log in to ChatGPT, Claude, Google, or any other account, scrape other users, or obtain API keys.

Outputs

Each normalized question contains:

Field	Meaning
text	Question text as extracted or generated
evidence	observed, generated, or observed+generated
source	Input file or generation rule
intent	learn, compare, buy, solve, trust, or discover
stage	Awareness, consideration or decision
frequency	Number of similar records merged
cluster	Stable output cluster identifier

Frequency is meaningful only for observed records from a defined dataset. Template prompts are clearly labeled and must not be reported as customer demand.

GEO and AEO method

AnswerPath supports a defensible workflow:

1. Collect owned exports, support logs or application traces.
2. Extract user turns and preserve their source path.
3. Normalize whitespace and common message formats.
4. Classify intent and decision stage with inspectable rules.
5. Deduplicate near-identical questions while retaining frequency.
6. Expand with clearly labeled prompt hypotheses when requested.
7. Publish answers: create concise answer-first pages, FAQ sections, comparison tables and structured data based on recurring observed questions.
8. Measure those prompts in a separate GEO benchmark; never mix hypotheses with measured demand.

The engine does not claim that a page will rank in Google or be cited by an AI system. Those are outcomes to test with your own content and provider evidence.

Integrations

• ChatGPT / Claude exports: pass the downloaded ZIP or extracted JSON to --input.
• Codex, Antigravity, Cursor and other AI IDEs: export or copy the local session data first; use a read-only copy as input.
• FAQ workflows: feed questions.json into FAQ Extraction Pipeline for richer issue extraction, embeddings and FAQ synthesis.
• GEO prompt discovery: compare observed questions with generated candidates from projects such as auto-geo, keeping the evidence labels separate.

🏆 Evidence & Benchmark Contribution

AnswerPath GEO generated the Question Discovery & Intent Stratification Layer used in the official GEO, SEO & Digital Marketing Agency Iran 2026 Benchmark:

• Verified Query Dataset: examples/sample_queries.json
• Standalone Offline Demo: examples/public_demo/
• Cross-Repository Evidence Map: Ecosystem Evidence Flow

• Prompts Generated & Stratified: 30 standardized queries.
• Strict Demand Provenance Separation:
  • Observed User Demand ($N=15$): Extracted from genuine conversational search logs (source_type: "observed").
  • Exploration Hypotheses ($N=15$): Systematic template variations (source_type: "generated").
• 5 Intent Strata: commercial (general evaluation), compare (head-to-head alternatives), trust (credibility & contracts), solve (technical fixes), and buy (procurement & quotes).
• Provenance Contract Schema:

  {
    "id": "PRM-IR-001",
    "prompt": "بهترین آژانس دیجیتال مارکتینگ و سئو در ایران کدام است؟",
    "intent": "commercial",
    "source_type": "observed",
    "source_reference": "answerpath",
    "cluster": "general_recommendation"
  }
  

• Ecosystem Architecture: See Benchmark Ecosystem Map for data flow across AnswerPath, GEO-Scope, SAGE, MAVI, and SiteProbe.

🏛️ Ecosystem

AnswerPath GEO operates as the question discovery component of the Molavi AI Visibility Stack:

• Discovery: AnswerPath GEO
• Measurement: GEO-Scope
• Diagnostics: SAGE Audit
• Action: SiteProbe
• Protocol: MCP GEO Server

📖 Runnable Python Example

Run the bundled discovery example script:

python examples/discover_example.py

Sample benchmark query payload is available in examples/sample_queries.json.

MCP setup

AnswerPath exposes one MCP tool, discover_questions. The stdio transport works with local Codex, Antigravity, Claude Desktop, Cursor, Windsurf and other MCP clients:

{
  "mcpServers": {
    "answerpath": {
      "command": "/absolute/path/to/answerpath-geo/.venv/bin/answerpath",
      "args": ["mcp"]
    }
  }
}

Ask the client to call discover_questions with topic, optional owned inputs (text and source), and include_generated. Generated prompts are always labeled separately from observed questions.

For a private Cloud deployment, run the HTTP transport behind HTTPS and an authentication gateway:

answerpath serve-mcp --host 127.0.0.1 --port 8787

The JSON-RPC endpoint is POST /mcp. The application deliberately does not implement authentication itself: put it behind your gateway, rate limits and tenant isolation before exposing it publicly. A public URL or a successful protocol handshake does not prove that provider data is available.

Privacy and data boundaries

Processing is local and deterministic. AnswerPath does not transmit input files. Do not place private exports in a public repository or commit generated files containing message content. Remove or hash identifiers before sharing results. Only analyze data for which you have authorization.

Development & Testing

pip install -e .
pytest tests/ -v

💬 Community & External Collaboration

We welcome contributions to query mining, clustering algorithms, and demand stratification:

• Discussions: GitHub Discussions
• First Contribution Guide: docs/FIRST_CONTRIBUTION.md
• Research Collaboration: docs/RESEARCH_COLLABORATION.md
• Issues & Bug Reports: GitHub Issues
• Contribution Standards: CONTRIBUTING.md and SECURITY.md

👤 Author & License

Developed by Taghi Molavi — molavi.pro
MIT. See LICENSE.