bull-ai.engine
    Engineered AEO

    The engineering layer for AI Search visibility.

    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.

    Free · No signup · 49 signals scanned

    AI engines tracked
    9
    Engineering signals
    49
    Prompts scanned per cycle
    1,200
    Two-fetch diff: raw HTML shows zero indexable elements while the rendered DOM is fully populated. The crawlability gap most AI engines never close.
    What Googlebot sees vs. what ChatGPT sees · live two-fetch diff
    Confidence Udegbue, VP of Product at Freeletics
    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.
    Confidence UdegbueVP of Product · Freeletics

    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.

    curl raw…
    EMPTY
    $ curl --user-agent
    "GPTBot"
    <div id="root"></div>
    <script src="/app.js">
    </script>
    3 words indexed
    render --js
    OK
    $ bullai render "/"
    <h1>Engineered AEO</h1>
    <p>49 signals, 9 engines…
    </p>
    <section id="pricing">
    </section>
    1,284 words indexed
    Open

    Engine × Signal Matrix

    49 signals. 9 engines. One operating picture.

    Every cell is a citation opportunity, scored continuously.

    CGPTCLDPPLGEMAIOCOPGRKMETYOU
    S1
    S2
    S3
    S4
    S5
    S6
    S7
    CRIT HIGH MED N/A
    Open

    Sprint board

    Engineering tasks pushed as PRs.

    Bi-weekly cadence, scoped to revenue impact, reviewable as pull requests.

    TODO 2
    In Review 2
    Shipped 3
    PR-126
    Patch robots.txt → allowlist ClaudeBot, PerplexityBot
    PR-127
    Add llms.txt with capability declarations
    PR-123
    SSR pre-render /pricing and /features for bot access
    PR-128
    Ground entity to Wikidata Q-node via sameAs
    PR-129
    Migrate React SPA → Next.js App Router (SSR)
    PR-130
    Expose .well-known/mcp.json catalog endpoint
    PR-131
    Fix hydration mismatch blocking
    Open

    Citation map

    Track citation share across every engine.

    ChatGPT, Claude, Perplexity, Gemini, AI Overviews, Copilot. Per-prompt, per-engine, weekly.

    Citation Share / 12 WK+48% WK/WK
    58%51%44%32%
    70%35%0
    W01W06W12
    ChatGPT
    Perplexity
    Claude
    Gemini
    Open

    Frontier signals

    The signals that will define 2027.

    MCP-readiness, agentic commerce surface, .well-known endpoints. Tracked before competitors know they exist.

    Frontier Scan
    2027 Readiness
    3/5
    /.well-known/ai-plugin.jsonDETECTED
    /.well-known/mcp.jsonMISSING
    /robots.txtDETECTED
    /agent-surfaceMISSING
    /openapi.jsonDETECTED
    Frontier Readiness3 of 5 Detected
    Open

    The platform

    From audit to citation in one bi-weekly cycle. Bull AI is the engineering layer between your technical surface and every AI engine that decides what gets cited.

    01Your technical surface
    MCP EndpointMCP
    Agents.mdAGENTS.MD
    .well-known.WELL-KNOWN/
    robots.txtROBOTS.TXT
    RenderingSSR / RSC / SSG
    Bot AccessROBOTS / UA
    Entity GraphWIKIDATA / KG
    Content ArchCHUNKS / BLUF

    Bull AI audits and engineers across every protocol surface that determines AI citation — from MCP endpoints to chunk architecture. 49 signals, measured continuously.

    02Bull AI engineers it
    BULL AIengineering layer
    01 / 03
    49 signals
    Diagnosed

    Across every surface above — rendering, entity, edge, community, reviews, agents.

    02 / 03
    Bi-weekly
    Engineering PRs

    Shipped as code, not slide decks. Two-week cycles, measured outcomes.

    03 / 03
    9 engines
    Measured

    Citation share tracked weekly across every major AI surface in the market.

    03AI engines cite you
    ChatGPT
    Claude
    Perplexity
    Gemini
    AI Overviews
    Copilot

    Your brand becomes the cited choice across every major engine — measured, not assumed.

    Freeletics

    Freeletics captured 58% AI search visibility in 12 weeks. Past Nike, Apple, and Peloton.

    A 12-week deployment of the Engineered AEO platform across nine AI engines.

    58%
    AI visibility shareacross 135 fixed prompts
    +43 pts
    Lift from baselinemeasured, not modeled
    9
    Engines wonout of 9 tracked
    $1.4M
    Attributed pipelinesourced from AI citations
    Freeletics · 12-week deployment
    Deployment log

    What the platform shipped

    • Eliminated React SPA crawl gaps

      Next.js App Router migration so GPTBot, ClaudeBot, and PerplexityBot see rendered HTML, not an empty root div.

    • Engineered entity graph

      Connected the brand across Wikidata, G2, and the Apple App Store so engines can resolve "Freeletics" as a single entity.

    • Restructured robots.txt for per-bot crawl access

      Explicit allow rules for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. No more accidental blocks.

    • Built MCP-ready catalog surface

      Programs, workouts, and coaches exposed as a structured catalog ahead of the agentic commerce shift.

    ChatGPT response ranking Freeletics ahead of Nike, Apple, and Peloton for a fitness-app recommendation prompt.
    What ChatGPT returns when buyers ask for fitness app recommendations.
    Confidence Udegbue, VP of Product at Freeletics
    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.
    Confidence UdegbueVP of Product · Freeletics
    The engineering thesis

    AI Search is engineering, not content.

    The gap between brands that get cited and brands that do not is engineering: rendering, entity resolution, and per-engine pipelines. Here is why.

    01 / Argument

    AI engines do not crawl your marketing site. They crawl your rendered HTML.

    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.

    ~939MMonthly GPTBot + ClaudeBot requests · Vercel/MERJ (2024)
    02 / Argument

    Citations follow entity resolution, not page count.

    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.

    −4.6%AI Overviews citations from adding schema alone · Ahrefs (2025)
    03 / Argument

    Each engine has its own citation diet.

    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.

    9Engine pipelines we measure and optimize independently

    AEO platforms ship dashboards because dashboards scale. Code execution does not. We chose the harder business.

    The Bull AI methodology

    49 engineering signals. 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.

    SIGNAL
    ChatGPTChatGPT
    ClaudeClaude
    PerplexityPerplexity
    GeminiAI Overviews
    GeminiGemini
    ChatGPTCopilot
    IMPACT
    PER-ENGINE STATEBLOCKEDBLOCKEDBLOCKEDBLOCKEDBLOCKEDBLOCKED
    CSR/SPA shipping empty HTML to bots
    CSR_SPA_EMPTY_BODY
    Hydration > 2.5s (content invisible at crawl)
    HYDRATION_TIMING_OVER_2_5S
    Wikipedia page absent (ChatGPT corpus gap)
    WIKIPEDIA_ABSENT_CHATGPT
    Reddit presence absent (Perplexity citation gap)
    REDDIT_ABSENT_PERPLEXITY
    Wikidata Q-node absent (entity not resolvable)
    WIKIDATA_QNODE_ABSENT
    Organization schema missing
    ORGANIZATION_SCHEMA_MISSING
    sitemap.xml missing or invalid
    SITEMAP_MISSING_OR_INVALID
    Alt text missing in bulk (image cite gap)
    ALT_TEXT_MISSING_BULK
    BLUF answer missing above the fold
    BLUF_ANSWER_MISSING
    Comparison table absent (citation magnet)
    COMPARISON_TABLE_ABSENT
    CRITICAL HIGH MEDIUM DOWNGRADED N/A
    How the engines actually cite

    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.

    Different engine, different pipeline, different optimization. Most AEO firms apply one playbook to all engines. That is why they do not move citation share.

    How it works

    Four stages. Bi-weekly cycles. Measured, not promised.

    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.

    Day 0

    Diagnose

    Two-fetch crawlability diff, entity graph probe, and engine-by-engine citation scan across your prompt set.

    • Two-fetch diff report
    • Engine x signal matrix
    • Sprint 0 task board
    Bi-weekly

    Engineer

    Rendering, robots, schema, and entity-graph fixes generated and deployed in bi-weekly cycles. Tracked, not theorized.

    • Bi-weekly fix plan
    • Per-task LLM validation
    • Crawlability score lift
    Bi-weekly

    Measure

    1,200 real measurements per cycle. Citation share, prompt visibility, and competitor capture across nine engines.

    • Visibility deltas per engine
    • Competitor capture map
    • Prompt-level intel
    Continuous

    Compound

    Each cycle's wins feed the next. Sources to own, prompts newly answered, and the next fix queue generated automatically.

    • Sources you could own
    • Cycle retrospective
    • Next-cycle backlog
    The Console

    Inside the console that turns invisibility into pipeline.

    Four tabs. One operating system for AI-search revenue. Click any tab - the dashboard and the story update together.

    The exact change. The exact file. The exact lift.

    One-click actions with the diff, the impacted page, and the projected score gain. Reviewable as a pull request, not a PDF deck.

    • Schema, render, crawler, and content fixes
    • Projected delta in score points before you ship
    • Approve, route to engineering, or auto-apply
    67

    Sprint 2 · Week 3

    +25 ptssince baseline

    Live
    Engineering

    Add SSR pre-rendering for React SPA routes

    +4.8

    Inject FAQPage + HowTo JSON-LD via middleware

    +3.2

    Fix hydration mismatch blocking Googlebot JS

    +2.9
    Marketing

    Rewrite /pricing with AI-first comparison copy

    +3.5

    Publish expert roundup targeting 12 discovery prompts

    +2.1
    Total sprint impact+16.5 pts projected
    The transformation

    3 months from invisible to recommended.

    Content-based AEO is a volume game. Volume games are won by giants. Engineered AEO is how everyone else wins.

    Before · buyers ask, you're invisible
    ChatGPT
    ChatGPT

    > "vivobarefoot vs xero shoes"

    8/100
    Claude
    Claude

    > "barefoot shoes for the gym"

    9/100
    Gemini
    Gemini

    > "vivobarefoot sizing reviews"

    11/100
    Perplexity
    Perplexity

    > "merrell vapor glove vs vivobarefoot primus"

    7/100

    Engineered AEO in motion

    Diagnosis
    from /audit
    Client-render shellMissing schemaCrawler blocked
    Sprint 02 · Pipeline
    LIVE
    SPR-12

    SPA blocker → SSR /pricing, /features

    MERGED+8.4 pts
    SPR-13

    Inject Product + FAQ JSON-LD schema

    IN REVIEW+6.1 pts
    SPR-14

    robots.txt + llms.txt → allow GPTBot, ClaudeBot, PerplexityBot

    SHIPPED+4.7 pts
    bi-weekly cadence
    Outcome · 90 days
    measured

    AEO Score

    +25 pts

    Revenue Recovered

    $1.2 – 1.8M

    After · recommended, then bought
    +$175
    Geo Court III
    Mike · New York

    Geo Court III

    Claude recommended → Purchased

    +$210
    Magna Trail II
    Emily · Sydney

    Magna Trail II

    Perplexity recommended → Purchased

    +$180
    Stealth III
    Chris · Toronto

    Stealth III

    Gemini recommended → Purchased

    Customer proof

    Engineered AEO, measured in citations.

    Each tile is a real customer outcome, scored against a fixed prompt set across nine AI engines.

    15%58%+43 pts
    AI visibility lift across 135 prompts

    12 weeks on the Engineered AEO platform. Share of voice across ChatGPT, Perplexity, AI Overviews, Claude, Gemini, and Copilot on a fixed prompt set.

    Freeletics · VP of Product
    9%47%+38 pts
    Citation share across 120 newsletter-discovery prompts

    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.

    Beehiiv · Head of Growth
    11%52%+41 pts
    AI Search share across 180 restaurant-ordering prompts

    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.

    Lunchbox · Director of Brand
    Reclaim your share of AI Search

    See the engineering gap between you and the brand AI is citing instead.

    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

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