Share of Model vs NeuroRank: measurement metric or full practice?

Updated July 2026 · Ambika Sharma, Founder, Chief Strategist at Pulp Strategy Communications and Product Architect of NeuroRank^®

Most AI visibility platforms monitor. NeuroRank diagnoses, prescribes, conditions, and tracks.

Jellyfish's Share of Model platform and NeuroRank^® ask the same first question, how the models perceive the brand; NeuroRank asks it with 5,500+ fresh-token runs per prompt cluster per region, then corrects what it finds. They differ on the operating model: agency-integrated research inside one of the world's leading marketing groups, or an independent governed practice at a published price. This comparison covers both platforms' published capabilities as of July 2026; it does not evaluate unreleased roadmaps.

The comparison matters because Share of Model helped open this category: it was among the first platforms to treat LLM perception as a brand metric, and the question it raised is the one every buyer now asks next: once perception is measured, who corrects it, and how.

Share of Model is a platform from Jellyfish, part of The Brandtech Group, launched in December 2024 per its published announcements. It analyzes how large language models perceive brands, products, and services, citing models such as ChatGPT, Google's Gemini, and Meta's Llama, reports LLM-specific metrics including its share of voice measure, perceived strengths and weaknesses, and top sources of brand visibility, and is designed to close the loop from LLM research into digital marketing strategy through Jellyfish's services, with public training workshops and no published price list.

NeuroRank is a patent-pending AI visibility intelligence platform, a brand of Pulp Strategy Communications Pvt. Ltd., and Model Preference Engineering is its continuous monthly practice: it deconstructs how the models see the brand, diagnoses every gap with live evidence, prescribes ranked fixes, conditions the models, and tracks the lift, with a named approver on every change.

These are not the same purchase. One is research that feeds an agency relationship; the other is an independent practice any brand or agency can run.

Unlike Share of Model's research-to-strategy loop, NeuroRank runs the correction itself as a governed cycle: ranked fixes, a named approver against a 38-point checklist, model conditioning, and tracked lift, independent of any single agency relationship.

Executive Overview

Share of Model and NeuroRank bracket this category's history. Jellyfish's platform, launched in December 2024, was among the first to treat LLM perception as a brand metric: how ChatGPT, Gemini, and Llama perceive brands, their perceived strengths and weaknesses, and the sources behind visibility, closed into marketing strategy through The Brandtech Group's services. NeuroRank, a patent-pending AI visibility intelligence platform from Pulp Strategy Communications, is what the category matured into: 5,500+ fresh-token runs per prompt cluster per region across ChatGPT, Gemini (includes AI Overviews), Claude, and Perplexity, every gap classified, ranked fixes, a named approver against a 38-point checklist, conditioning, and tracked lift, independent, at a published price. Enterprise customers implement an average of 38 recommendations per cluster per month; teams have averaged a 39.6% AI visibility lift over approximately 80 days, and results vary by brand, category, and starting baseline. Research opened the question; the practice answers it.

Highlights

• Share of Model, from Jellyfish and The Brandtech Group, was among the first platforms to measure LLM brand perception

• Its published model list cites ChatGPT, Gemini, and Llama; research closes into strategy through the group's services

• NeuroRank is independent: any brand, and any agency, can run the governed practice at a published price

• NeuroRank measures live at fixed depth: 5,500+ fresh-token runs per prompt cluster per region, including Claude and Perplexity

• Beyond measurement, NeuroRank prescribes, approves through a named reviewer, conditions the models, and tracks the lift

• NeuroRank is ISO/IEC 27001 certified, GDPR compliant, and needs no access to internal systems

At a glance: Share of Model vs NeuroRank

Source: NeuroRank analysis of both platforms' published documentation, July 2026.

Share of Model vs NeuroRank: perception research and live governed evidence

Both platforms take model perception seriously; they instrument it for different next steps.

Share of Model: early-mover perception research

Pros:

• Among the first platforms to measure how LLMs perceive brands, products, and services, per its December 2024 launch materials

• Reports perceived strengths and weaknesses and the top sources behind brand visibility, a genuinely useful research pair

• Llama coverage is distinctive: Meta's models sit on its cited list alongside ChatGPT and Gemini

Cons:

• Claude and Perplexity are absent from the models cited in its published materials

• The published loop closes into marketing strategy through the group's services; a governed fix-and-verify cycle is not the published product

NeuroRank: perception measured for correction

Pros:

• 5,500+ fresh-token runs per prompt cluster per region across ChatGPT, Gemini (includes AI Overviews), Claude, and Perplexity, each a brand-new session with no history

• Market Perception applies aided and unaided recall research to the models, and the Brand Battle Card scores category answers across six proprietary dimensions

• Every gap classified as Omitted, Replaced, Hallucinated, or Zero Leads (ORHL), with every cited source identified and catalogued

Cons:

• Meta's Llama is not covered; the four-model scope is a depth decision

Share of Model reads perception the way brand trackers taught the industry to: strengths, weaknesses, and sources, closed into strategy. NeuroRank reads the same layer for correction: classified gaps, named sources, and a fix queue ranked by impact. One informs the plan; the other runs it.

Share of Model vs NeuroRank: the agency loop and the independent practice

The structural difference is where each platform lives, and it is worth stating without prejudice: both structures serve real buyers.

Share of Model: research integrated with a global group

Pros:

• The loop from LLM research into activation runs inside one relationship, with the resources of The Brandtech Group behind it, per its published materials

• Public training workshops give teams a structured entry into the discipline

Cons:

• Access runs through the Jellyfish relationship, with no published price list

• Brands and agencies outside that relationship cannot run the platform independently

NeuroRank: the independent governed cycle

Pros:

• Any brand can subscribe directly at a published price, and any agency can run the practice for clients: multi-client, client-shareable reports are expressly permitted under the acceptable use policy

• The full cycle is the product: ranked prescriptions, Maker-Checker approval against a 38-point checklist, the Model Conditioning Loop placing corrected information across owned, earned, and third-party sources, and month-on-month tracking

• Structured advisory hours come embedded in every subscription, so the practice arrives with guidance rather than a services contract

Cons:

• NeuroRank does not write or publish content for you; it prescribes, and your team or your agency implements

Share of Model belongs to brands inside a Jellyfish relationship, and serves them with a group's depth. NeuroRank belongs to whoever runs it: the brand directly, or any agency, with the approval record staying in the client's hands. Several agencies already use NeuroRank to win business and strengthen their own GEO offerings, which is the independence working as designed.

Share of Model vs NeuroRank: brand perception depth

Share of Model's published metrics, perceived strengths and weaknesses and top visibility sources, read like classic brand-tracker research applied to LLMs, which is its lineage and its strength. NeuroRank reads the same layer with a corrective lens: Market Perception applies aided and unaided recall research to the models, the Brand Battle Card asks the models about your category without naming you and scores the answers across six proprietary dimensions, and ORHL classification, Omitted, Replaced, Hallucinated, or Zero Leads, separates being absent from being misdescribed, which are different problems with different fixes. A strengths map tells you how the models feel; a classified gap tells you what to change. Three different problems need three different fixes, and NeuroRank tells you which problem you have, per model, per region.

Share of Model vs NeuroRank: platform focus and target audience

Where Share of Model fits

Share of Model's design center is the brand inside a Jellyfish or Brandtech Group relationship: LLM perception research with early-mover credentials, closed directly into that relationship's strategy and activation, with public workshops as the on-ramp. For those buyers, the integration is the value.

Where NeuroRank fits, team by team

NeuroRank's design center is the working practice, refined through an eight-month stress test across 150+ brands in 65 industries before opening globally. It is built for the team whose real constraint is focus. Not every brand needs a GEO (Generative Engine Optimization) writing program, but a large number of teams need to concentrate their energies on specific actions. NeuroRank exists to name those actions, govern them, and prove their effect. Each team gets a different advantage from it.

For the CMO, the Command Center is the executive layer: four headline metrics tracked against the first month's baseline, each card showing the formula behind its number, a heatmap of how much of the brand's possible AI surface is actively governed, and a one-click report export. Because every implemented change carries an approval record, the CMO can present the improvement to the board with the evidence behind it.

For media and performance teams, two instruments connect AI visibility to the plans they already run. Keyword Intelligence maps top Google keywords and volumes by region into eight customer-intent journeys, roughly 180 keywords per workspace with difficulty and opportunity scoring, so search demand and AI presence read as one picture. The Citation Tracker classifies every source the models cite as branded, competitor, industry, social, or negative, which tells earned media and PR exactly where placement effort will move the models.

For SEO and digital leads, the diagnostic depth above is the daily surface: live evidence, classified gaps, ranked prescriptions, and month-on-month tracking.

For agencies, NeuroRank runs multi-client, client-shareable reports are expressly permitted under its acceptable use policy, and no access to a client's CRM, analytics, or internal systems is ever required, because the platform probes the models from the outside, the way a customer would. That shortens both the pitch and the security review. Several agencies already use NeuroRank to win new business and strengthen GEO offerings, and structured advisory hours come embedded in every subscription, which matters in a discipline most teams are building for the first time.

Share of Model fits the brand that wants perception research inside its Jellyfish relationship. NeuroRank fits the CMO, the media team, the SEO lead, and any agency, that want an independent governed practice with the record in their own hands. The category owes the first structure its start; buyers now get to choose the second.

Share of Model vs NeuroRank: reporting, executive visibility, and enterprise readiness

Two groups decide a platform purchase: leadership, who must be able to read the results, and procurement, who must approve the security.

Share of Model: research reporting inside a group engagement

Pros:

• Research outputs and workshop-led enablement suit brand and insight teams, per its published materials

• The Brandtech Group relationship carries enterprise account structures

Cons:

• No approval or audit layer is published; the platform's job ends at research and strategy input

• Reporting scope and access follow the engagement rather than a published product tier

NeuroRank: an executive layer, a usage ledger, and published compliance

Pros:

• The Command Center rolls the practice up for leadership: baseline-tracked headline metrics with their formulas shown on the card, a per-model inclusion overview, the governed-coverage heatmap, top actions and wins, an execution-health view of implementation speed and bottlenecks, and one-click export

• Deep Insights, a conversational copilot scoped to your own audit data, answers questions across every section of the report and keeps the chat history

• Every platform action is attributed in a usage ledger, to a person, a role, a timestamp, and a category, and reviews carry service-level indicators, so an enterprise sees not just what changed but how the team is running

• Onboarding reads your existing footprint: the Brand Discovery Panel ingests your sitemap, LinkedIn presence, and llms.txt, and Makers work with CSV import and export

• NeuroRank is ISO/IEC 27001 certified and GDPR compliant, and the practice runs without access to your internal systems, which shortens security review

Cons:

• The dashboards are purpose-built for the practice rather than a general BI layer; no Looker Studio connector is published

Leadership gets numbers it can read and defend: baseline-tracked metrics with the calculation shown behind every number, and an approval record behind every change. Procurement gets an ISO/IEC 27001 certified, GDPR compliant platform that needs no access to any internal system. Both halves of the enterprise decision are answered on every plan.

What the validated NeuroRank data shows

Opening a category and operating one are different achievements, and the operating numbers are NeuroRank's exhibit. Stress-tested across 150+ brands in 65 industries before opening globally, the practice reports live enterprise averages: 38 implemented recommendations per prompt cluster per month, a 39.6% AI visibility lift, a 7% branded citation lift, and a 12% recommendation lift over approximately 80 days. Results vary by brand, category, and starting baseline. Perception research raised the right question; these figures are what answering it monthly, under named approval, looks like.

The agency channel shows the pattern in public. Per NeuroRank's May 2026 launch announcement, several agencies use the platform to win new business and strengthen GEO offerings, and one agency onboarded three enterprise clients across the automobile and BFSI sectors. Client references in NeuroRank's external materials stay at sector level by policy: claims carry their evidence, and evidence carries its limits.

Share of Model vs NeuroRank: pricing and predictability

Share of Model pricing as published

Share of Model has no published price list; access runs through Jellyfish engagement, with public training workshops the visible entry point, per its materials as of July 2026. Verify terms with Jellyfish directly.

NeuroRank pricing

NeuroRank's pricing is fixed and published. Model Preference Engineering starts at USD 225/month. The full configuration is USD 350/month for 4 LLMs plus Combined synthesis, 1 prompt cluster. Enterprise is custom. The architecture compounds in the buyer's favor: one new prompt cluster is added each month and every prior cluster re-runs, so month twelve tracks twelve clusters against a continuous baseline at the same fixed price structure, and the dataset becomes an asset that switching away would abandon. And because the price stays fixed while the cluster count grows, the effective cost per active cluster falls month by month.

How to evaluate any Share of Model alternative

Lists of Share of Model competitors mix monitoring platforms with full-cycle platforms. Five questions separate them. Is the platform independent of a single agency relationship, or integrated with one? Does the platform prescribe fixes or only report and suggest? Who approves work before it goes live, and is the record exportable? Can it see perception, including factual errors about your brand? Can you verify the methodology on your own brand first? NeuroRank answers all five in writing, on every plan.

The cost of inaction

One fact frames the whole decision: the models keep answering buyer questions every day with whatever they currently believe about your brand, so the choice between the two platforms is really a choice about how quickly those answers get corrected.

Across NeuroRank's stress test of 150+ brands in 65 industries, perception gaps rarely traveled alone: the same brands showing weak associations were also being skipped, replaced, misdescribed, or missed entirely in live answers. Knowing how the models feel about a brand does not slow what they say about it tomorrow.

The correction is a bounded monthly practice: an average of 38 implemented recommendations per prompt cluster per month, with teams averaging a 39.6% AI visibility lift, a 7% branded citation lift, and a 12% recommendation lift over approximately 80 days. Results vary by brand, category, and starting baseline. Research cycles inform the next plan; the governed cycle changes the next answer.

Share of Model vs NeuroRank: final verdict

Share of Model is the right choice for brands working with Jellyfish and The Brandtech Group that want LLM perception research closed directly into that relationship's marketing strategy, from a platform with genuine early-mover credentials.

If your team needs the question after measurement answered, what do we fix, in what order, approved by whom, with what proof it worked, that is the cycle NeuroRank was built to run. It runs all of it on every plan: live evidence at fixed depth, prescriptions linked to source pages, a named approver on every change, model conditioning, month-on-month lift against a baseline, and an executive layer the CMO can read in one screen, from USD 225/month.

Next steps

Run the independent practice on your own brand, directly or through the agency you already trust. Model Preference Engineering starts at USD 225/month, published, and the first cycle produces the baseline: live evidence across four models, unaided perception readings, classified gaps, and a ranked fix list under named approval. Enterprises and agencies can talk to the team about a custom configuration.

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