Bull AI is the engineering platform for AI search visibility. 49 technical signals diagnosed across nine engines. Infrastructure fixed in bi-weekly cycles. Citation share measured with statistical rigor. Not content. Not guesses. Engineering.
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Bull AI 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.
Six product surfaces, one operating picture. Diagnose the crawl gap, score every engine, ship the fixes, measure the lift.
Two-fetch diff
Catch invisible content before it costs you citations.
Engine × Signal Matrix
Every cell is a citation opportunity, scored continuously.
Sprint board
Bi-weekly cadence, scoped to revenue impact, reviewable as pull requests.
Citation map
ChatGPT, Claude, Perplexity, Gemini, AI Overviews, Copilot. Per-prompt, per-engine, weekly.
ChatGPTPerplexityClaudeGeminiFrontier signals
MCP-readiness, agentic commerce surface, .well-known endpoints. Tracked before competitors know they exist.
The platform
Bull AI audits and engineers across every protocol surface that determines AI citation — from MCP endpoints to chunk architecture. 49 signals, measured continuously.
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.
A 12-week deployment of the Engineered AEO platform across nine AI engines.
Next.js App Router migration so GPTBot, ClaudeBot, and PerplexityBot see rendered HTML, not an empty root div.
Connected the brand across Wikidata, G2, and the Apple App Store so engines can resolve "Freeletics" as a single entity.
Explicit allow rules for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. No more accidental blocks.
Programs, workouts, and coaches exposed as a structured catalog ahead of the agentic commerce shift.
Bull AI 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.
The gap between brands that get cited and brands that do not is engineering: rendering, entity resolution, and per-engine pipelines. Here is why.
Most AI crawlers do not execute JavaScript. If your React or Vue app ships an empty root div, the engine indexes nothing and cites a competitor. Fixing this is an engineering job, not a copywriting one.
Engines decide who to cite by resolving your brand against Wikidata, Wikipedia, Reddit, G2, and the app stores. If the entity graph is broken, every new blog post lands in a corpus the engine cannot link back to you.
ChatGPT leans on Wikipedia. Perplexity and AI Overviews lean on Reddit. Claude favors documentation and academic sources. One playbook for all of them moves nothing. We optimize per pipeline.
AEO platforms ship dashboards because dashboards scale. Code execution does not. We chose the harder business.
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
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.
Sources: Profound (2025), Ahrefs, Vercel/MERJ (2024). Updated quarterly.
Different engine, different pipeline, different optimization. Most AEO firms apply one playbook to all engines. That is why they do not move citation share.
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 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.
Surface coverage, tool descriptions, capability completeness, schema rigor, error clarity.
MCP, agents.md, .well-known — audited before mainstream tooling exists.
Most AEO platforms sell a one-time audit. Bull AI runs a continuous loop: diagnose, engineer, measure, compound. Each cycle ships code, not slide decks.
Two-fetch crawlability diff, entity graph probe, and engine-by-engine citation scan across your prompt set.
Rendering, robots, schema, and entity-graph fixes generated and deployed in bi-weekly cycles. Tracked, not theorized.
1,200 real measurements per cycle. Citation share, prompt visibility, and competitor capture across nine engines.
Each cycle's wins feed the next. Sources to own, prompts newly answered, and the next fix queue generated automatically.
Four tabs. One operating system for AI-search revenue. Click any tab - the dashboard and the story update together.
One-click actions with the diff, the impacted page, and the projected score gain. Reviewable as a pull request, not a PDF deck.
Sprint 2 · Week 3
+25 ptssince baseline
Add SSR pre-rendering for React SPA routes
+4.8Inject FAQPage + HowTo JSON-LD via middleware
+3.2Fix hydration mismatch blocking Googlebot JS
+2.9Rewrite /pricing with AI-first comparison copy
+3.5Publish expert roundup targeting 12 discovery prompts
+2.1Content-based AEO is a volume game. Volume games are won by giants. Engineered AEO is how everyone else wins.
> "vivobarefoot vs xero shoes"
> "barefoot shoes for the gym"
> "vivobarefoot sizing reviews"
> "merrell vapor glove vs vivobarefoot primus"
Engineered AEO in motion
SPA blocker → SSR /pricing, /features
MERGED+8.4 ptsInject Product + FAQ JSON-LD schema
IN REVIEW+6.1 ptsrobots.txt + llms.txt → allow GPTBot, ClaudeBot, PerplexityBot
SHIPPED+4.7 ptsAEO Score
+25 pts
Revenue Recovered
$1.2 – 1.8M
Geo Court III
Claude recommended → Purchased
Magna Trail II
Perplexity recommended → Purchased
Stealth III
Gemini recommended → Purchased
Each tile is a real customer outcome, scored against a fixed prompt set across nine AI engines.
12 weeks on the Engineered AEO platform. Share of voice across ChatGPT, Perplexity, AI Overviews, Claude, Gemini, and Copilot on a fixed prompt set.
10 weeks. Rendered-HTML fixes and Reddit/G2 entity reinforcement deployed through the platform. Measured on a fixed creator-tooling prompt set across all 6 engines.
14 weeks. Schema rebuild, location-page rendering, and Perplexity-pipeline tuning deployed continuously. Share of voice across the restaurant-tech prompt set on 6 engines.
Run a free Engineered AEO audit in under two minutes. Get the two-fetch diff, the engine x signal matrix, and the first cycle of fixes, scored and ready.
No credit card · Audit in under 2 minutes · 9 engines scanned
Every month we pick one underdog brand and run a full $100K+ AI search audit for free. Follow along and see exactly how underdogs beat giants on AI search.
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