GEO Brand Mention Tracker

GEO Brand Mention Tracker

AGENT ROLE: Autonomous GEO-monitoring agent. Query each configured answer engine with each prompt, extract brand presence/sentiment/accuracy deterministically, and emit the JSON in references/output.schema.json. This is measurement, not manipulation — never instruct an engine to praise the brand.

OBJECTIVE

Measure, per engine and per prompt, whether the target brand appears in the generated answer, whether it is linked/cited, the sentiment of its framing, and whether stated facts about it are correct — then aggregate into presence-rate and sentiment trends.

INPUTS

• brand (REQUIRED): { name, domains: string[], aliases?: string[] }.
• prompts (REQUIRED string[]): buyer-intent questions (see references/prompt_template.json for structure).
• engines (OPTIONAL): subset of ["openai","anthropic","gemini","perplexity"]. Default all with a configured key.
• facts (OPTIONAL): array of { claim, correct: bool } ground-truths to accuracy-check.
• baseline (OPTIONAL): prior run's aggregate for trend deltas.

AUTHENTICATION (per engine)

• Read keys from env: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, PERPLEXITY_API_KEY.
• For each engine in engines: IF its key is missing THEN skip that engine and add it to skipped_engines with reason no_api_key. IF engines end up empty THEN STOP error.code="NO_ENGINE_CREDENTIALS".
• Endpoints: OpenAI POST /v1/chat/completions; Anthropic POST /v1/messages (header anthropic-version); Gemini POST /v1beta/models/{model}:generateContent; Perplexity POST /chat/completions (returns citations).

EXPECTED TOOL CALLS

• Run scripts/track_mentions.py --brand brand.json --prompts prompts.json.
• Per (engine, prompt): send the user prompt with temperature=0 for reproducibility; request citations where the engine supports them.

PROCEDURE (deterministic, per engine × prompt)

STEP 1 — SEND prompt; capture answer_text and citations[] (URLs) if provided. STEP 2 — PRESENCE: mentioned = true IF brand.name or any alias appears (case-insensitive, word-boundary). linked = true IF any brand.domains appears in answer_text or citations. STEP 3 — SENTIMENT: classify the sentence(s) mentioning the brand as positive|neutral|negative using a strict rubric (recommendation/superlative → positive; caveat/warning/negative comparative → negative; else neutral). Record the exact quoted sentence as evidence. STEP 4 — ACCURACY (if facts): for each ground-truth claim, check whether the answer asserts it correctly, incorrectly, or not at all → correct|incorrect|absent. Any incorrect about the brand = hallucination flag. STEP 5 — AGGREGATE per engine: presence_rate = mentioned_count / prompt_count; link_rate; sentiment distribution; accuracy_rate. STEP 6 — TREND (if baseline): delta each aggregate vs baseline. STEP 7 — EMIT.

RATE LIMITS & ERROR HANDLING

• Respect each provider's RPM/TPM. On 429 THEN honor Retry-After if present, else backoff 2^attempt (max 5); after that mark that (engine,prompt) status="rate_limited" and continue — never abort the whole run.
• On 5xx/timeout (60s) THEN retry ≤3; then record status="engine_error" for that cell.
• Cap concurrency at 3 per engine.

MISSING / INSUFFICIENT DATA

• IF an engine returns an empty/blocked answer THEN mentioned=false, status="empty_answer" — do not infer absence as negative sentiment.
• Sentiment/accuracy are computed ONLY when the brand is mentioned; otherwise null.
• Never claim a citation the engine did not return.

OUTPUT

One JSON object per references/output.schema.json. No prose.

FILES

• scripts/track_mentions.py — multi-engine client, presence/sentiment/accuracy extraction, aggregation.
• references/prompt_template.json — recommended buyer-intent prompt structure.
• references/output.schema.json — output contract.