AI SEARCH TRIED TO RECOMMEND YOU.YOUR OWN WEBSITE FAILED YOU.
We fetch your site the way GPTBot, ClaudeBot and PerplexityBot fetch it, then show you the gap and the fix.
Rendered
AI search cannot fetch this page.
So it cannot recommend you as the answer.
# 9 words parsed. No product, price or claim text reached the model.
This diff is not a one-time read. Crawlers change, your site changes — we re-fetch and track it continuously.
Bull AI Labs rebuilt how every AI engine sees Freeletics. Within a quarter we went from being absent in answer surfaces to being the cited choice for our category.
Don't be bullish. Open your LLM lab.
Everyone else hands you a report.We ship the fix.
An agency audit lands in your inbox. The recommendations sit in an engineering backlog for months. Your visibility depends on renting mentions - Reddit threads, listicles, other people's pages.
Your first verified fix ships within 24 hours of the audit. We re-probe it on the next live fetch. Your own pages become the source AI engines read - you stop renting visibility and start owning it.
Own the source. Don't rent it.
Answer share moved from 15% to 68% on tracked high-intent queries during the engagement.
One lab. Every surface.
Every cell is a real prompt on a real engine.
Ranking on Google doesn't mean ranking in AI.
Googlebot renders your JavaScript. AI crawlers don't. Run any URL through the two-fetch diff and see exactly what ChatGPT, Claude, and Perplexity can — and can't — read on your site.
Not a tool. A stack for AI search visibility.
Six product surfaces, one operating picture. Diagnose the crawl gap, score every engine, ship the fixes, measure the lift.
Two-fetch diff
See what AI crawlers actually see.
Catch invisible content before it costs you citations.
Engine × Signal Matrix
51 signals graded. 9 engines. One operating picture.
Every cell is a citation opportunity, scored continuously.
Sprint board
Engineering tasks pushed as PRs.
Bi-weekly cadence, scoped to revenue impact, reviewable as pull requests.
Citation map
Track citation share across every engine.
ChatGPT, Claude, Perplexity, Gemini, AI Overviews, Copilot. Per-prompt, per-engine, weekly.
ChatGPTPerplexityClaudeGeminiFrontier signals
The signals that will define 2027.
MCP-readiness, agentic commerce surface, .well-known endpoints. Tracked before competitors know they exist.
The platform
From audit to citation in one bi-weekly cycle. Bull AI Labs is the engineering layer between your technical surface and every AI engine that decides what gets cited.
Bull AI Labs holds a point of view on every protocol surface that will determine AI citation — from chunk architecture to the MCP layer where agents will invoke instead of browse. 51 signals graded across the surfaces we measure today; MCP coverage is POV, not shipped probing.
Across every surface above — rendering, entity, edge, community, reviews, agents.
Shipped as code, not slide decks. Two-week cycles, measured outcomes.
Citation share tracked weekly across every major AI surface in the market.
Your brand becomes the cited choice across every major engine — measured, not assumed.
51 signals graded. 9 AI engines. Tracked continuously.
Click any cell to see what the platform measures. Filter by engine to see the signals that move citation share for that pipeline specifically.
THE ENGINE × SIGNAL MATRIX
Every signal, every engine, at a glance.
Each cell shows how that engine actually consumes this signal - match, downgrade, or N/A. Click any failing cell to jump to its detailed finding below.
Different engine, different pipeline, different citation diet.
- ChatGPT cites Wikipedia more than any other source
- Perplexity and AI Overviews lean heavily on Reddit
- AI Overviews surfaces YouTube prominently
- Claude favors documentation, .gov, and academic sources
Sources: Profound (2025), Ahrefs, Vercel/MERJ (2024). Updated quarterly.
Most AEO playbooks apply one motion to every engine. That is why citation share does not move.
AI agents don’t browse.
They invoke.
The companies that get cited in 2027 aren’t the ones with the best content. They’re the ones whose products AI agents can actually use.
Bull AI Labs audits your MCP surface across five dimensions — surface coverage, tool description quality, capability completeness, schema rigor, and error clarity — then ships the engineering work to fix every gap.
Universal scoring. Industry-specific loops. Every vertical covered.
$ bullai-probe --mcp-readiness https://acmestore.com▸ MCP DISCOVERY✓ /.well-known/mcp.json FOUND (manifest v2026-03-26)✓ /api/mcp FOUND (Shopify Storefront MCP)✗ /api/mcp/customer 404 (Customer Account MCP)✗ /api/mcp/checkout 404 (Checkout MCP preview)✓ HTML meta declaration FOUND✓ robots.txt allowlist 5/7 AI crawlers permitted▸ PROTOCOL SURFACETransport: HTTP + SSE (correct)Auth: OAuth 2.1 with PKCESpec version: 2026-03-26 (current)Capabilities: resources, tools, prompts▸ TOOL SURFACE (12 declared)✓ search_products desc: well-formed, 142 tokens✓ get_product_details desc: well-formed, 98 tokens⚠ add_to_cart desc too generic ("adds item")⚠ get_inventory pagination schema missing✗ checkout NOT EXPOSED — conversion blocker✗ create_return NOT EXPOSED — retention break✓ get_order_status well-formed⚠ apply_discount errors not agent-readable▸ CAPABILITY LOOP (ecommerce)✓ browse search_products, get_product_details✓ cart add_to_cart, get_cart✓ auth login, get_customer✗ checkout NO TOOLS — agent task fails at conversion✗ post_purchase NO TOOLS — no return, refund, or tracking surface▸ AGENT DISCOVERABILITYChatGPT crawl: ✓ ingested 2026-05-14Claude Desktop: ✓ MCP server registeredGemini Agentic Mode: ⚠ partial (Customer MCP missing)Copilot Shopping: ✗ not registered▸ MCP READINESS SCORE: 58 / 100Surface coverage 61/100 ⚠Tool description quality 72/100 ⚠Capability completeness 34/100 ✗ (no checkout, no returns)Schema rigor 68/100 ⚠Error clarity 54/100 ⚠▸ CRITICAL GAPS→ Checkout MCP not deployed. Agents browse but cannot buy.→ No returns surface. Post-purchase agentic loop terminates.→ 4 tools have generic descriptions. Agents won’t dispatch.▸ ENGINEERED SPRINT (4 PRs)PR-1 Deploy Shopify Checkout MCP preview [critical]PR-2 Rewrite tool descriptions for agent dispatch [high]PR-3 Add Customer Account MCP [high]PR-4 Idempotency keys on cart manipulation tools [medium]ESTIMATED CITATION LIFT: +23pp ChatGPT, +31pp Perplexity ShoppingESTIMATED AGENTIC CONVERSION SURFACE: $0 → addressable
/.well-known/, /api/mcp, Shopify storefront, agents.md, and more — every known agentic surface we track.
Surface coverage, tool descriptions, capability completeness, schema rigor, error clarity.
MCP, agents.md, .well-known — in scope before mainstream tooling exists.
Engineered AEO, measured in citations.
Each result is a real customer outcome, scored against a fixed prompt set across nine AI engines.
During the engagement, Freeletics' answer share on high-intent queries moved from 15% to 68% - outranking Nike, Peloton, and Apple Fitness+.
10 weeks. Rendered-HTML fixes and Reddit/G2 entity reinforcement deployed through the platform. Measured on a fixed creator-tooling prompt set across all 9 engines.
14 weeks. Schema rebuild, location-page rendering, and Perplexity-pipeline tuning deployed continuously. Share of voice across the restaurant-tech prompt set on 9 engines.
Bull AI Labs rebuilt how every AI engine sees Freeletics. Within a quarter we went from being absent in answer surfaces to being the cited choice for our category.
See the engineering gap between you and the brand AI is citing instead.
Free - domain only - no signup. First read in about two minutes.
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