Brainpan.AI
MULTI-ENGINE AI VISIBILITY INTELLIGENCE

See Where AI Recommends Your Brand, and Why the Engines Disagree.

Brainpan.AI measures mentions, recommendations, position and supporting sources across ChatGPT, Gemini, Perplexity, Copilot, Claude and Google AI Overviews. Then we identify what's suppressing your visibility, fix it, and retest the same answer surface.

Request Multi-Engine Diagnostic Explore the Insurance Benchmark
Measured across major AI discovery systems
ChatGPT Gemini Perplexity Copilot Claude Google AIO
Our Data + Industry Outcomes

What AI visibility work has been worth, in public

  • Our own audit · Insurance category

    1,742

    total organic citations recorded across our own Q2 2026 benchmark — 1,348 of which resolved to a brand within the scored 134-brand cohort (the remaining 394 resolved to sources outside it) — and every one of them came from Perplexity or Copilot. ChatGPT, Gemini, and Claude produced brand mentions but effectively zero citations in this category.

    AIVI™ Q2 2026 Insurance Benchmark

    Source: Brainpan.AI primary research

  • Cybersecurity

    0.6%

    citation share (∼#31 rank) despite 700K+ customers and dominant traditional SEO. Legacy search authority did not transfer.

    Global network security vendor

    Source: LeadWalnut (vendor-reported)

  • B2B SaaS

    124K

    ChatGPT-referred sessions over a 6-month window, and 3,400 conversions in a separate 1-month window — different periods, not a combined conversion rate.

    Presentation software platform

    Source: SE Ranking · Mentimeter (vendor-reported)

  • Ecommerce

    +559%

    organic sales growth (plus +306% organic traffic) over a 4-month SEO/AEO program — not isolated to AI-referred traffic specifically.

    Premium cookware brand

    Source: AEO Engine (vendor-reported)

  • B2B technology services

    12.5%

    share of AI search coverage for its defined query set — ahead of larger competitors in LLM citations for target queries.

    Mid-size services firm

    Source: Chilli Fruit · Future Processing (vendor-reported)

The first figure above is our own primary research. The rest are published third-party case studies, cited above, verified against their original sources — evidence of what other AI visibility programs have produced, not a forecast of your results. Read the full business case →

The problem

Your buyers are getting answers. They are not getting to your site.

Assistants now resolve the research question inside the answer itself. The click that used to land on your page never happens, and the brands named in that answer are selected by a retrieval system nobody on your team is measuring. This is a selection problem, not a ranking problem — and the two do not have the same fix.

  • 2,549

    brand mentions produced by ChatGPT, Gemini and Claude in our Q2 2026 insurance study — with zero citations attached. Named, unlinked, unattributable.

  • 2 of 5

    engines we measured cite a source at all. Perplexity and Copilot link out; the other three answer from a model you cannot see into.

  • 60.0/100

    average AIVI score across the ten most visible carriers. Even the category leaders leave a third of the available answer surface uncontested.

The mechanics of the shift, and what it does to a traditional search program, are set out in The Post-Click Era: why your brand is disappearing from AI answers.

The operating model

Measure, diagnose, implement — in that order, every time

AI visibility is not a campaign. It is a measurement loop: establish where the models place you today, isolate the specific retrieval and entity failures behind that position, ship the fixes, then re-measure on the same prompts so the movement is attributable to the work rather than to the weather.

▦1

Measure

Sample the answer surface across five engines or seven surfaces using documented, reproducible prompts, and score share of model, recommendation strength, position weighting, and citation influence.

◈2

Diagnose

Trace every gap to a cause — missing entity signals, structure a parser cannot read, thin coverage of the questions buyers actually ask, or a competitor already holding the citation.

▲3

Implement

Ship the schema, content, and architecture changes in priority order, then re-run the identical prompt set so the delta is measured, not asserted.

◎4

AI Citation Footprint

Map where your brand is mentioned, cited, ignored, or misrepresented.

Audit your footprint →
▥5

Cross-Model Diagnostics

Track performance fluctuations across ChatGPT, Gemini, Perplexity, Copilot, Claude, and Google AIO.

Benchmark AI engines →
▧6

Entity & Knowledge Graph Readiness

Strengthens the entity, content and structured-data signals that make brand information easier for AI retrieval systems to identify, interpret and corroborate.

Verify schema readiness →
⌕7

Competitive Recommendations Gap

Isolates which competitors AI engines suggest to users before they find your brand.

Uncover competitor gaps →
▤8

Algorithmic Content Priorities

Build high-intent pages engineered specifically to feed the data structures AI engines crawl for answers.

Deploy content fixes →
↗9

Executive Attribution & Reporting

Translate raw model visibility into high-level enterprise tracking, benchmarking, and actionable growth plans.

Build executive reports →
Original research

We publish the benchmark before we sell the audit

Independent category evidence—not selective client outcomes. Our published proof begins with full-category measurement, an open methodology, public data and a correction policy. Client results are never presented without permission.

The Insurance AI Visibility Index, Q2 2026

134 carriers, 300 stratified prompts, five engines, 1,500 model responses. It recorded 4,328 organic brand mentions and 1,742 organic citations, and put the top-ten carrier average at 60.0 out of 100. State Farm leads the index at 100; the second-placed carrier sits 29 points back.

Read the Q2 2026 findings · See how the score is calculated

The full index — all 134 brands, per-engine detail — $3,500. One time payment. Single-organization commercial license. Buy the full index →

Public teardowns: The Hartford, Allstate, and Nationwide

The Hartford holds the best position rate in the entire study — 88.1% of the answers that name it place it in the top three — and still finishes eighth overall, on a 27.6% recommendation rate. Allstate is the mirror case: the third-highest share of model in the study, and sixth on the index, beaten by a carrier named 24 fewer times. Nationwide is the third case: last in the published top ten on both placement measures, first on citation rate, and fifth overall on the composite. Being retrieved, being placed well, and being recommended are separate problems. The teardowns show precisely where the second one breaks, and what it would take to fix.

The Hartford teardown · The Allstate teardown · The Nationwide teardown

Built entirely from public model outputs. No client relationship, no private data, corrections policy published alongside the method.

Measurement standard

Built for measurement, benchmarking, and executive reporting

◎

Standardized capture

Versioned prompts, declared engine settings and documented capture procedures create a consistent measurement process. Repeated observations distinguish durable visibility from model variation.

Model-level comparison

Compare across leading models: ChatGPT, Gemini, Perplexity, Copilot, Claude, and Google AIO.

✓

Repeatable scoring

Transparent scoring tracks citation share, coverage, and recommendation presence over time.

What arrives

The deliverables, in the form you actually receive them

No portal to log into, no dashboard subscription, no “insights” you have to interpret before anyone can act. Three artifacts land in your inbox, and every one of them is something a team can work straight through.

  • A written PDF report — citation footprint baseline, competitive gap analysis, schema and structured-data assessment, content architecture review, and entity clarity review, each carrying the evidence behind the finding.
  • A schema implementation spreadsheet — the exact markup to add, by URL and by property, in a form a developer can execute without a translation layer.
  • A 90-day action table — the fixes in priority order, with the expected visibility effect of each one stated up front rather than discovered later.

Delivered within 5–10 business days. An optional 45-minute walkthrough call is included.

See the section-by-section breakdown of the report →

Engagement fit

What this costs, and who it is for

The audit is scoped to the brand — category breadth, prompt volume, and the number of properties in play all move it — so it is priced after a short scoping conversation rather than off a rate card. Ongoing programs are not scoped that way: the retainer tiers are published below, month-to-month after a three-month foundation period.

  • Starter

    $3,500/month

    Monitoring and reporting

    Monthly citation sampling across five platforms, a share-of-model scorecard, a competitive gap delta, and a schema health check. Built for brands that want a measurement baseline before committing to optimization work.

  • Growth

    $7,500/month

    Monitoring and a monthly sprint

    Everything in Starter, plus a one-to-two page content optimization sprint each month, schema updates, and a full written report setting the next month’s priorities.

  • Enterprise

    $15,000+/month

    Full managed program

    Everything in Growth, plus expanded platform sampling, a quarterly deep-dive audit, analytics architecture maintenance, and direct CMO briefing support. Priced on scope.

Good fit: enterprise brands and insurers with a category worth defending and a team that can ship the fixes once they are identified. Poor fit: anyone looking for a monitoring dashboard with nobody assigned to act on what it says.

Request an AI Visibility Audit → Review a sample audit (PDF)

Full tier detail, including what changes between them: retainer tiers and pricing.

Why this, and not that

A monitoring tool, an SEO agency, and an AI visibility program are three different purchases

A monitoring tool

Tells you your share of model moved. It does not tell you which retrieval failure caused the move, and it does not ship the schema, the entity signals, or the content that reverses it. You still need somebody to read it and act.

An SEO agency

Optimizes for a ranked list of ten links. AI answers are a selection problem instead: the engine names a handful of brands and, in three engines out of five, cites none of them. Legacy authority does not transfer automatically — a global security vendor with 700K+ customers and dominant traditional SEO measured zero AI citations.

An AI visibility program

Measurement, diagnosis, and implementation under one roof, scored against a published method and a category benchmark we ran ourselves. You get the number, the reason for the number, and the work that changes it.

Both comparisons in full: AI Visibility Audit vs SEO audit and Brainpan.AI vs an internal team.

Before you ask

The four questions that come up on every first call

What does it cost?

Retainers are published: $3,500, $7,500, and $15,000+ per month, depending on how much optimization work runs alongside the measurement. The audit itself is scoped and priced per brand, because the size of the prompt set and the number of properties in play vary too much for a single posted number to be honest. The Q2 2026 Insurance Index one fixed price: $3,500. One-time payment. Single-organization commercial license.

How long before anything changes?

The audit itself lands within 5–10 business days. Movement in AI answers follows the re-crawl and re-index cycle after the fixes ship, which in practice means weeks rather than days for schema and entity work, and longer where content has to be written first. Anyone quoting you a fixed number here is guessing.

What proof do you have that it works?

None of our own yet, and we will not dress it up as something else. What exists is published third-party evidence from other firms’ programs — the five outcomes above, each cited to its source — and our own primary research, which is the part we control and the part you can check line by line. When there are client results to publish, they will be published with the same disclosure.

What happens if we do nothing?

The answer surface does not stay empty. Somebody in your category gets named in the answers your buyers are already asking for, and the association between that question and that brand strengthens every time a model reinforces it. The cost of waiting is not a flat line — it is a competitor’s position getting more expensive to displace.

Illustrative only. The 72 is a design placeholder, not a client score — your audit reports your own measured number.

Request a Brainpan.AI Visibility Audit

Get a clear, data-driven view of how AI systems see your brand — where you are visible, where you are missing, and exactly what to do next.

You receive
◎AI citation footprint
▥Competitive visibility comparison
⌕Model-by-model gap analysis
▧Content and schema priorities
9090-day AI visibility roadmap
Request Multi-Engine Diagnostic → Review a sample audit (PDF)

Prefer to talk? Reach Kevin directly at (703) 951‑7195 or kwalsh@brainpan.ai.