Brainpan.AI
MULTI-ENGINE AI VISIBILITY INTELLIGENCE

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

The Brainpan.AI Visibility Engine: AI visibility measurement for regulated industries.

Brainpan.AI measures mentions, recommendations, position and supporting sources across up to eight AI platforms, including ChatGPT, Gemini, Perplexity, Microsoft Copilot, Bing Copilot, Claude, and Google AI Overviews. Then the Visibility Engine identifies what's suppressing your visibility, prioritizes the fixes for your team, and retests the same answer surface.

AI visibility is how often and how favorably a brand is retrieved, cited, and recommended by AI answer surfaces.

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

What AI visibility work has been worth, in public

  • Our own audit · Medicare category

    62,484

    citation rows recorded across our own Q3 2026 Medicare benchmark; twenty-five domains supply 52.7% of them. Google AI Overviews records the highest brand-citation rate of the seven surfaces (39.5%); the ChatGPT and Gemini API configurations record 0.0%.

    AIVI™ Q3 2026 Medicare Benchmark

    Source: Brainpan.AI primary research

  • Cybersecurity

    0.6%

    starting citation share (ranked #31) in cloud security as of October 2025, for a company with 700K+ customers. A large customer base did not automatically translate into AI citation.

    Global network security vendor

    Source: LeadWalnut case study (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: Siege Media · Mentimeter (vendor-reported)

  • Ecommerce

    +559%

    organic sales growth (plus +306% organic traffic) over 4 months. Not isolated to AI-referred traffic specifically.

    Premium cookware brand

    Source: AEO Engine (vendor-reported)

The first figure above is our own primary research. The rest are published third-party case studies, each linked to its original source above. They are 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.

  • 3 of 7

    surfaces cited the named brand's own site in 0.1% of mentions or less in our Q3 2026 Medicare capture: ChatGPT / OpenAI API (0.0%), Gemini API (0.0%) and Claude API (0.1%). These API configurations still cited official sources such as Medicare.gov (Claude API 83.6%).

  • 39.5%

    brand-citation rate on Google AI Overviews, the highest of the seven surfaces. Microsoft Copilot cites the brand in 3.9%. The surfaces do not behave alike.

  • 50.5%

    of 59,055 scored commercial mentions go to three carriers: UnitedHealthcare, Humana and Aetna. None of the three leads on QVI™, the per-mention score (recommendation scored on consumer surfaces only).

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, act, 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, act on the prioritized findings, 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 up to eight AI platforms (ChatGPT, Gemini, Perplexity, Microsoft Copilot, Bing Copilot, Claude, and Google AI Overviews) 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

Act

Your team or agency makes the schema, content, and architecture changes in priority order; the Visibility Engine then re-runs 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 AI Overviews.

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 build the dashboard

Independent category evidence from the Visibility Engine, not selective outcomes. Our published proof begins with full-category measurement, an open methodology, public data and a correction policy. Design-partner results are never presented without permission.

Last verified:

The Medicare AIVI™ Benchmark, Q3 2026

121 Medicare brands tracked across seven AI surfaces, 115 with recorded visibility. The study extracted 229,372 entity rows from 14,405 populated responses and scored 59,055 commercial mentions. UnitedHealthcare, Humana and Aetna hold 50.5% of them; Medicare.org posts the highest QVI™ (48.05) on 1.64% of mention share. QVI™ scores recommendation only on the three consumer surfaces; rows from the four API surfaces score zero on it.

Read the Q3 2026 findings · See how the score is calculated

The full index (all 115 visible brands, per-surface detail) is $3,500. One time payment. Single-organization commercial license. Buy the full index →

Public analyses: UnitedHealthcare and Kaiser Permanente

UnitedHealthcare leads on reach (18.35% of commercial mentions) and ranks ninth on QVI™ (34.91), winning 28 of 78 scored head-to-head appearances. Kaiser Permanente is the mirror case: 2.47% of mentions, 17 wins in 20 scored appearances, and essentially no owned citation share. Reach, quality, and evidence are three different advantages: a brand can lead one and trail the other two.

The UnitedHealthcare analysis · The Kaiser Permanente analysis

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

Previous edition. The Insurance AI Visibility Index, Q2 2026: 134 insurance brands, five engines; State Farm leads at 100. Read the Q2 2026 findings · The Hartford, Allstate and Nationwide analyses · Buy the Q2 Insurance index → ($3,500, one time payment, single-organization commercial license)

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 up to eight AI platforms: ChatGPT, Gemini, Perplexity, Copilot, Claude, and Google AI Overviews.

✓

Repeatable scoring

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

What arrives

The deliverables, in the form you actually receive them

Dashboard access is in development with our design partners. Today, testers receive founder-led multi-engine diagnostics; the Gen 3 Visibility Engine dashboard will deliver the same measurement weekly. Each diagnostic arrives as three artifacts 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 specification spreadsheet: the exact markup to add, by URL and by property, in a form a developer can execute without a translation layer.
  • A prioritized findings table: recommended actions in priority order, with the expected visibility effect of each one stated up front rather than discovered later.

Delivered within 5–10 business days. A 45-minute executive readout of findings is included.

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

The product

The Brainpan.AI Visibility Engine

One measurement pipeline produces every number on this site: reproducible prompts across ChatGPT, Gemini, Perplexity, Copilot and Claude, brand identification, sentiment scoring and human-validated verification. Design partners receive its output as founder-led diagnostics today; the Gen 3 weekly dashboard is in development.

▦1

Capture

Scheduled, reproducible prompts run across every engine, so the same question is asked the same way each cycle.

◈2

Identify and score

Each response is parsed for the brands it names, where it positions them, what it cites, and the sentiment it expresses.

▲3

Verify

Scores are checked against human-validated samples and kept with an audit trail, so a regulated team can defend every number it reports.

  • Achieved14,405 / 14,490Medicare observations populated of those scheduled (99.4%)
  • Achieved229,372mentions extracted, including 59,055 commercial mentions
  • Achieved93.8%human-validation agreement (209 cases)
See how the Visibility Engine works →
Gen 3 dashboard

Join the Dashboard Waitlist

Hear first when the Gen 3 dashboard opens for your industry.

⚡

You're on the waitlist

Thanks. You will hear from Brainpan.AI when the Gen 3 dashboard opens for your industry. Every message carries an unsubscribe link.

⚡

Waitlist request sent

Thanks. You will hear from Brainpan.AI when the Gen 3 dashboard opens for your industry. If nothing arrives, check your spam folder or use the contact page.

No spam. Unsubscribe anytime.

By submitting you agree to the terms and privacy notice.

Design partners

Design partner pilots

Design partners use the Visibility Engine while its Gen 3 dashboard is in development. The first diagnostic is scoped to the brand, because 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. Pilot tiers are published below: a three-month pilot, then month to month.

  • Pilot: Starter

    $3,500/month

    Measurement baseline

    Monthly citation sampling across up to eight AI 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. Includes early dashboard access when Gen 3 launches.

  • Pilot: Growth

    $7,500/month

    Measurement and monthly recommendations

    Everything in Starter, plus prioritized findings and recommended actions each month, a schema readiness review, and a full written report setting the next month’s priorities. Includes early dashboard access when Gen 3 launches.

  • Pilot: Enterprise

    $15,000+/month

    Extended pilot with weekly measurement cycles

    Everything in Growth, plus expanded platform sampling, weekly measurement cycles, a quarterly deep-dive diagnostic, AI traffic attribution review, and an executive readout of findings. Priced on scope. Includes early dashboard access when Gen 3 launches.

Good fit: enterprise brands and insurers in regulated categories with an internal owner who can act on findings. Poor fit: teams that want measurement with nobody assigned to act on what it shows.

Request Multi-Engine Diagnostic Review a sample audit (PDF)

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

Why this, and not that

Dashboards, agencies, and verified measurement solve different problems

A dashboard-only tool

Tells you your share of model moved. Without verified, version-stable scoring, you can’t tell whether the brand changed or the model did.

An SEO agency

Optimizes for a ranked list of ten links. AI answers are a selection problem: the engine names a handful of brands and explains why.

The Brainpan.AI Visibility Engine

Measures every engine with reproducible prompts, scores sentiment against human-validated samples, and keeps an audit trail regulated teams can defend. Design partners get founder-led diagnostics today and the Gen 3 dashboard as it launches.

Both comparisons in full: Multi-Engine Diagnostic 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?

Design-partner pilot tiers are published: $3,500, $7,500, and $15,000+ per month, depending on measurement frequency and scope. The first diagnostic 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 Insurance and Medicare benchmark indexes are each one fixed price: $3,500 (Q2 2026 Insurance; Q3 2026 Medicare). One-time payment. Single-organization commercial license.

How long before anything changes?

The diagnostic 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 three 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 design-partner 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 measured score. Your diagnostic reports your own measured number.

Request a Multi-Engine Diagnostic

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
90Prioritized findings and recommended actions
Request Multi-Engine Diagnostic → Join the Dashboard Waitlist Review a sample audit (PDF)

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

Kevin Walsh is the founder and lead engineer of Brainpan.AI, where he builds the Visibility Engine, the measurement platform behind the AIVI™ benchmarks, for brands that need to be retrieved, cited, and trusted by AI answer engines. Corrections: see the published correction policy.

Not ready for the full diagnostic?

Get the next AIVI™ edition and citation tips

Work email and company. That's it.

⚡

You're on the list

Thanks. New AIVI editions and research notes will arrive from Brainpan.AI. Every message carries an unsubscribe link.

⚡

Signup sent

Thanks. New AIVI editions and research notes will arrive from Brainpan.AI. If you don't hear from us when the next edition is published, check your spam folder or use the contact page.

No spam. Unsubscribe anytime. Prefer the full diagnostic?

By submitting you agree to the terms and privacy notice.