Ai Competitor Intelligence

Benchmark a company against its competitors inside AI-generated answers across ChatGPT, Gemini, Claude, and Perplexity — and learn why competitors win.

Overview

For every question, this Actor asks each AI engine and, for your company and each competitor, extracts mentions, position, sentiment, the reason, and citations. It builds a recommendation-frequency ranking, compares each competitor to you, explains why competitors win (more Reddit mentions, more review sites, more documentation, more backlinks, more comparisons), and produces actionable recommendations.

Architecture

src/
├── main.ts                 # Actor.main() entry point
├── types.ts                # Input/analysis/summary types
├── config/index.ts         # Input validation → strict config (entity universe)
├── analysis/index.ts       # per-entity stats, comparisons, recommendations
├── report/index.ts         # "why competitors win" markdown report
└── shared/                 # Reusable library (adapters, engine, parser, citations, …)

Pipeline: validate → build tasks → collect answers → analyze each entity → compare → recommend.

Input

Field	Type	Default	Description
company	string (required)	—	Your company
competitors	string[] (required)	—	Competitors to benchmark against
questions	string[] (required)	—	Questions asked on every platform
platforms	string[]	all four	ChatGPT, Gemini, Claude, Perplexity
headless	boolean	true	Headless browser
screenshotOnFailure	boolean	true	Screenshots to KV store on failure
maxRetries	integer	2	Retries per (platform, question)
askTimeoutMs	integer	90000	Max wait per answer
useApifyProxy	boolean	true	Route through Apify Proxy
cookies	array	[]	Session cookies for authenticated platforms

Input example

{
  "company": "ChatbotsHub",
  "competitors": ["Botpress", "Voiceflow", "Intercom"],
  "questions": ["best chatbot platform for businesses", "best conversational AI tools"]
}

Output

Dataset row (one per company/competitor)

{ "company": "ChatbotsHub", "isYourCompany": true, "recommendationRate": 22, "mentionRate": 40, "averagePosition": 3.5, "totalCitations": 2, "redditMentions": 0 }

Key-Value Store artifacts

• SUMMARY — entity stats, competitor comparisons (with advantages), recommendations
• REPORT — markdown: recommendation frequency + "why competitors win" + recommendations
• DETAILS — per (question, platform, entity) analyses (mention, position, sentiment, reason)
• EXPORT_CSV — flat CSV of entity stats

How "why competitors win" is computed

Each competitor is compared to you across measurable signals; a competitor "wins" a signal when it scores strictly better:

• ✓ Higher AI recommendation rate
• ✓ Mentioned more frequently / better average position
• ✓ More Reddit / community mentions
• ✓ More review-site, documentation, forum, or blog/comparison citations
• ✓ More backlinks (unique cited URLs) / more total citations

Citations are attributed per entity (only citations on the entity's own line/bullet count), so comparisons reflect each brand's real cited footprint.

Rate limits & known limitations

• Auth walls (ChatGPT/Claude/Gemini) → supply cookies. Perplexity usually works anonymously.
• Position/sentiment are heuristic (structure + lexicon), keeping runs deterministic and cheap.
• Signals depend on the engines surfacing citations for a given answer.

Deployment

npm install && npm run build && npm test && npm run lint
npx playwright install chromium   # local only; Docker image already has it
apify push

Docker base image: apify/actor-node-playwright-chrome:22.

Testing guide

npm test runs parser/citation (fixture-based), retry, validation, statistics, adapter (mock page), engine integration, and competitor-analysis tests. Fixtures live in tests/fixtures/.

Troubleshooting

Symptom	Cause	Fix
All entities 0%	Auth wall / no answers	Provide cookies, enable proxy
Missing citation signals	Engine returned no links	Prefer Perplexity; retry

Version history

• 1.0.0 — Initial production release.