Jellyfish / Share of Model

Executive Overview

Jellyfish is a global digital marketing company within The Brandtech Group. Its generative engine optimization offering combines agency services with Share of Model, a proprietary measurement platform designed to assess how brands appear in answers produced by large language models and AI search systems. The platform benchmarks visibility, competitive presence, sentiment, brand associations, recommendations, and cited information; Jellyfish then uses those findings to shape content, technical, authority-building, and measurement programs. The offering is aimed principally at large brands and marketing teams that need a structured view of AI discovery rather than isolated tests in consumer chat interfaces. Customers generally buy a consultative or managed engagement, technology access, or a combination, rather than a simple mass-market SEO tool. Headquartered in London, Jellyfish operates across the Americas, Europe, the Middle East, and Asia-Pacific. Its position is distinctive because it combines enterprise marketing execution with proprietary AI-answer benchmarking, placing it between specialist GEO software vendors, enterprise SEO platforms, and full-service agencies serving technology and other consumer-facing industries. Share of Model observes model outputs; it does not control how an AI platform generates an answer.

Jellyfish / Share of Model at a Glance

Logo
Name	Jellyfish / Share of Model
Segment	GEO and AI search agencies
Headquarters	London, United Kingdom
Organization type	Global digital marketing company with a proprietary AI-visibility measurement platform and managed GEO services
Official website	Jellyfish / Share of Model

1. What Is the GEO Strategy of Jellyfish / Share of Model?

Winning Aspiration

Jellyfish addresses a measurement gap created as consumers use generative systems for research, comparisons, and recommendations. Conventional search metrics show rankings, impressions, clicks, and backlinks, but they do not fully describe whether an AI answer mentions a brand, how it portrays the brand, which competitors it recommends, or which sources support the response. Share of Model is intended to make that emerging discovery environment measurable. Jellyfish’s broader aspiration appears to be converting those observations into an ongoing marketing discipline that connects brand, organic search, content, media, data, and communications.

Where It Plays

The offering is oriented toward established brands, multi-market businesses, and enterprise marketing organizations. It is relevant where consumers ask category, product, reputation, comparison, or purchase-consideration questions through ChatGPT, Gemini, Perplexity, Google’s AI search experiences, and other large-model systems. Jellyfish participates across measurement, strategy, content planning, technical readiness, authority development, and managed execution. Its global agency footprint supports regional and multilingual programs, although platform availability and measurement depth can vary by model and market.

How It Tries to Win

The principal differentiation is the combination of proprietary benchmarking and agency implementation. Specialist monitoring tools may stop at dashboards, while traditional agencies may audit a small sample of prompts manually. Jellyfish can use Share of Model data to identify competitive gaps and then draw on its organic search, content, creative, media, analytics, and technology teams to act on them. Its existing relationships with large advertisers also create a route for GEO to become part of broader marketing programs.

Required Capabilities

Execution requires reliable prompt design, repeatable model querying, entity and sentiment analysis, source extraction, data storage, comparative benchmarking, and interpretation by category specialists. It also requires SEO, editorial, digital PR, analytics, and technical implementation skills. Because model products change quickly, Jellyfish must continually update collection methods, platform coverage, and the meaning of its metrics.

Management Systems

A credible operating model depends on quality controls for prompt sets, model versions, geographies, response classification, and historical comparisons. Enterprise customers also require access controls, data governance, clear reporting definitions, and account management. Public information explains the offering at a strategic level but does not disclose every internal sampling, validation, or governance procedure.

2. What Are the Strategic Priorities of Jellyfish / Share of Model?

Jellyfish does not publish a fixed, exhaustive list of GEO priorities. Its product development, research, and agency positioning nevertheless indicate several durable themes:

• Establish AI-answer measurement as a recurring brand metric. Share of Model is positioned as an ongoing benchmark rather than a one-time prompt audit. Historical tracking is important because outputs, competitors, retrieval sources, and underlying models change.
• Move from diagnosis to implementation. Jellyfish uses visibility and perception findings to support content, organic search, technical, media, and authority-building work. This expands the relationship from analytics into managed execution.
• Connect GEO with existing marketing disciplines. The company treats AI discovery as an addition to search, brand, content, and communications rather than a wholly isolated channel. That positioning fits enterprise teams whose budgets and responsibilities remain distributed across existing functions.
• Broaden model, market, and language coverage. Enterprise usefulness depends on monitoring the AI experiences customers actually use in different regions. Maintaining comparable data while platforms introduce new models and interfaces is therefore strategically important.
• Develop category-level intelligence. Aggregated Share of Model research gives Jellyfish benchmarks for how brands and sectors appear in generative answers. Such datasets can strengthen sales, planning, and recommendations, provided category and prompt definitions remain transparent.
• Demonstrate commercial relevance. The long-term challenge is to connect answer visibility with qualified traffic, consideration, leads, or sales. Moving beyond mention counts toward defensible business outcomes would increase the offering’s value to senior marketers.

These themes are analytical interpretations of Jellyfish’s market activity, not a claim that the company formally labels them as its corporate priorities.

3. What Is the Business Model of Jellyfish / Share of Model?

Jellyfish operates a hybrid technology-and-services model. Customers can buy access to proprietary Share of Model analysis, strategic work based on that data, and agency execution intended to improve the underlying signals. Public-facing materials emphasize consultation and managed programs rather than a standardized self-service subscription with fully published pricing.

Typical economic buyers are chief marketing officers and leaders of search, brand, digital, content, media, communications, or analytics. Daily users may include SEO specialists, content strategists, brand managers, analysts, and agency teams. Engagements can include an initial benchmark or audit, followed by recurring monitoring and a project or retainer covering implementation. Packaging is likely to vary with the number of brands, competitors, markets, languages, categories, platforms, prompts, and service work involved.

Value is created in three stages:

1. Share of Model converts a changing collection of AI outputs into comparative brand intelligence.
2. Jellyfish interprets the findings and identifies gaps in owned content, technical accessibility, entity clarity, third-party authority, or competitive positioning.
3. Agency teams can implement and monitor a program over time.

Repeat use is driven by answer volatility, model updates, new competitors, product launches, and the need to assess whether interventions coincide with improved outcomes. Switching costs can grow when customers accumulate historical benchmarks, customized prompt taxonomies, regional datasets, workflows, and agency knowledge. Those costs are practical rather than absolute: brands can also query models directly or use competing platforms.

The model benefits Jellyfish because measurement can open broader consulting and execution work. For customers, the advantage is a single provider across analysis and activation; the trade-off is that platform methodology and agency recommendations should be evaluated independently to avoid treating a proprietary score as the sole definition of success.

4. What GEO Products and Services Does Jellyfish / Share of Model Offer?

Share of Model Measurement

Share of Model is the core proprietary capability. It repeatedly asks category-relevant questions across selected generative systems and analyzes the resulting answers. The platform is designed to show whether a brand appears, how often it appears relative to competitors, what language and associations surround it, when it is recommended, and which sources or domains are visible in cited responses. Results can be segmented by topic, market, model, competitor, or other dimensions configured for an engagement.

GEO Assessment and Strategy

Jellyfish offers discovery work that establishes a baseline and turns output data into a prioritized plan. An assessment can examine prompt families, competitive presence, content gaps, brand narratives, technical readiness, cited-source patterns, and differences among AI platforms. Strategy work may define target audiences, high-value conversational journeys, measurement rules, governance, and the division of responsibilities among SEO, brand, content, PR, and technology teams.

Content and Authority Programs

The company can use findings to brief new content or refresh existing material. Relevant work includes explanatory pages, product and category information, comparison content, frequently asked questions, expert material, original research, and clearer brand or entity descriptions. Because generative answers may rely on third-party sources, Jellyfish can also apply communications, media, creator, or digital PR capabilities to improve the availability of credible external information. Publication does not guarantee retrieval, citation, or favorable treatment.

Technical and Organic Search Support

Adjacent services include organic search, site architecture, crawlability, structured information, analytics, and broader digital experience work. These capabilities can help a site become easier for search and retrieval systems to access and interpret. Structured data and crawler access are enabling conditions, not mechanisms that compel an AI platform to cite a brand.

Managed Optimization and Reporting

For larger clients, Jellyfish can combine monitoring, recommendations, implementation, and periodic reporting. This is a material difference from software-only competitors. The company’s service model can coordinate teams and markets, but customers should clarify which work is included, which metrics determine progress, and which dependencies remain outside Jellyfish’s control.

5. Which Information Routes and AI Platforms Does Jellyfish / Share of Model Address?

Jellyfish’s approach spans several routes through which information can reach an AI-generated answer. The routes are related but should not be treated as interchangeable.

• Observed model responses: Share of Model queries selected AI systems and records their outputs. This measures what appeared for the sampled questions and collection conditions; it does not reveal every user interaction or the model’s internal reasoning.
• Live web retrieval and citations: Some answer engines search or retrieve current web documents and display citations. Jellyfish can analyze which domains recur and identify opportunities in owned or third-party sources. A citation shows source use or attribution, not necessarily endorsement.
• Owned website content: Product pages, service descriptions, editorial resources, corporate facts, and support content can supply explicit information about a brand. Jellyfish can influence this route through content and technical work, subject to crawler, indexing, retrieval, and platform choices.
• Entity and structured information: Consistent names, attributes, relationships, and structured markup may make information easier for machines to interpret. They can reduce ambiguity but do not guarantee inclusion in an answer.
• Third-party authority: Publishers, review sites, trade sources, communities, creator content, and other independent pages can shape retrieval results and model representations. Jellyfish can identify influential source categories and conduct legitimate media or content programs, but it cannot control independent publishers.
• Crawling and indexing: Technical audits can identify blocked crawlers, rendering problems, weak internal linking, or inaccessible content. Allowing a crawler permits access; it does not prove that content will be indexed, retrieved, cited, or used for model training.
• Model training data: Older or non-retrieval answers may reflect pre-existing training data. Public web optimization cannot reliably insert a claim into a model’s parameters, and Jellyfish does not control model training decisions.

Jellyfish materials position the offering around prominent generative and AI-search environments, including ChatGPT, Gemini and Google’s AI search experiences, and Perplexity. Share of Model research has also examined outputs associated with other major model providers. Coverage can differ by engagement, geography, language, API availability, and product version. An API response from a base model is not necessarily equivalent to the consumer interface, which may add search, personalization, citations, memory, safety layers, or advertising. Buyers should therefore confirm the exact platform, model, interface, location, and collection method included in a program.

6. How Does Jellyfish / Share of Model Measure GEO Performance?

Share of Model is based on repeated observation of generative answers for a defined question set. Jellyfish organizes questions around a category, consumer need, brand, product, or decision journey, submits them to selected systems, and processes the returned text. The analysis can then compare brands, competitors, topics, platforms, markets, and time periods.

Important measures include:

• Brand presence: whether a brand appears in an answer and how frequently it appears across the monitored response set.
• Share of model: a relative measure of a brand’s presence compared with named or detected competitors within the selected prompt universe. It is a proprietary benchmark, not equivalent to market share or search share.
• Prominence and recommendation: whether a brand is merely mentioned, appears early or prominently, or is presented as an option. The interpretation depends on the answer format and classification rules.
• Perception and sentiment: descriptive language, positive or negative framing, strengths, weaknesses, and recurring associations. Automated sentiment can miss irony, conditional language, mixed opinions, and category-specific meaning.
• Competitive context: which alternatives appear, how their positioning differs, and where a competitor has stronger coverage for a topic or intent.
• Citations and source domains: the pages or domains shown as support where a platform exposes citations. Platforms without citations require different analysis.
• Change over time: movement in observed outputs after model updates, market events, or marketing activity.

Public information establishes the broad measurement concept but does not expose every weighting rule, classifier, prompt-generation procedure, or sample-size requirement. Customers should ask how questions are selected, whether they are customer-defined or generated, how many repeat runs occur, and how Jellyfish handles refusals, duplicate answers, model-version changes, localization, and missing citations.

The measures are mainly observed or derived from sampled outputs. They are not direct records of total consumer demand. Unless linked separately to analytics, CRM, ecommerce, or brand-lift data, they also do not show traffic, conversion, incremental revenue, or causality. Historical consistency is valuable, but a methodology change can create apparent movement unrelated to consumer behavior.

7. How Does Jellyfish / Share of Model Try to Improve AI Visibility or Favorability?

Jellyfish uses Share of Model as a diagnostic layer. The platform identifies weak visibility, undesirable associations, competitor advantages, missing topics, and commonly cited sources. Agency teams can then select interventions based on the apparent cause rather than applying a single GEO tactic to every problem.

• Content improvement: Jellyfish can create or revise pages so they answer important questions directly, contain verifiable facts, distinguish products clearly, and cover the attributes consumers use when comparing alternatives. This is technically plausible because retrieval systems require accessible passages relevant to a query.
• Entity clarity: Consistent company, brand, product, location, and relationship information can reduce ambiguity. Structured markup may reinforce that information for systems that process it, although markup alone does not establish authority.
• Technical readiness: Work can address crawl access, rendering, architecture, canonicalization, internal linking, page performance, and indexable formats. These measures improve discoverability but cannot force an AI system to retrieve a page.
• Evidence-rich publishing: Original data, expert explanation, transparent methodology, and properly supported claims can make a source more useful to search and answer systems. Quality and independent relevance matter more than merely increasing content volume.
• Earned authority: Digital PR, media relations, partnerships, and credible third-party coverage may broaden the source environment from which an answer engine draws. Legitimate earned coverage is different from placing repetitive promotional claims across low-quality sites.
• Narrative correction: Where monitored answers repeatedly use inaccurate or outdated descriptions, Jellyfish can improve authoritative first-party facts and pursue corrections or updated coverage elsewhere. Changes may take time and are not guaranteed to alter model output.
• Continuous testing: Repeated monitoring can show whether observed visibility changes after implementation. Because models and external sources also change, a before-and-after difference should not automatically be attributed to the campaign.

The mechanism is influence through better information availability, relevance, clarity, and authority—not direct control of an AI provider. Jellyfish cannot guarantee that a model will mention, recommend, cite, or favor a client. Nor does increased visibility by itself establish favorable sentiment, qualified traffic, or conversion.

8. Who Uses Jellyfish / Share of Model?

The offering is most naturally suited to medium-sized and large organizations with recognized competitors, meaningful digital content, and sufficient brand or product complexity to justify systematic monitoring. A single local business can test AI answers manually; a multinational brand needs repeatable analysis across many questions, models, markets, and business units.

Economic buyers and sponsors can include:

• chief marketing officers and digital leaders seeking an enterprise view of AI discovery;
• brand and consumer-insights teams monitoring perception and competitive narratives;
• SEO and organic-search leaders expanding measurement beyond conventional result pages;
• content teams deciding which subjects, comparisons, and evidence gaps to address;
• communications and PR teams evaluating the third-party sources reflected in answers;
• analytics teams connecting AI visibility with site behavior, leads, or commercial outcomes;
• regional marketing teams comparing brands across countries and languages; and
• executive teams assessing reputational or category risks created by generative answers.

Relevant use cases include category benchmarking, product launches, brand-positioning reviews, reputation monitoring, market entry, competitive research, content planning, and evaluation of AI-search readiness. Consumer categories with frequent comparison and recommendation questions—such as retail, travel, financial services, technology, automotive, beauty, and entertainment—are particularly suitable. Business-to-business companies can also use the approach where buyers ask detailed solution, vendor, or implementation questions.

Jellyfish’s existing enterprise agency relationships create a practical customer base for Share of Model. However, broad corporate relationships should not be interpreted as proof that every Jellyfish client uses its GEO product. Public customer evidence specifically tied to controlled GEO outcomes remains less extensive than the company’s wider portfolio of media, creative, and digital marketing work.

9. Who Competes with Jellyfish / Share of Model?

Profound

Profound is a software-focused AI-visibility and answer-engine analytics provider serving enterprise brands. It overlaps in prompt monitoring, citations, competitive visibility, and analysis of how AI agents access content. Compared with Jellyfish, its identity is more centered on a dedicated software platform; Jellyfish has a broader agency execution model spanning media, content, search, and brand work.

Scrunch AI

Scrunch AI monitors brand representation across AI answers and offers tools related to AI-readable customer experiences. It is relevant when buyers want software for visibility analysis, source insights, and site readiness. Jellyfish differs through its global agency organization and its ability to incorporate findings into larger marketing programs, while Scrunch presents a more specialized AI-customer-experience proposition.

Peec AI

Peec AI provides prompt-level visibility, mention, citation, and competitor tracking with an accessible software model used by in-house teams and agencies. It competes most directly where customers prioritize rapid dashboard deployment and self-service monitoring. Jellyfish is more consultative and enterprise-service-oriented, with Share of Model embedded in broader strategic and execution capabilities.

Semrush

Semrush extends a large SEO and competitive-intelligence suite into AI visibility. It is a substitute for organizations that want AI-answer monitoring alongside keyword research, site audits, backlinks, content tools, and established SEO workflows. Jellyfish offers a more agency-led model and proprietary brand-perception framing, while Semrush benefits from broad self-service distribution and conventional search data.

BrightEdge

BrightEdge serves enterprise SEO teams with content-performance, search, and AI-related visibility capabilities. The overlap is strongest in enterprise measurement, recommendations, and integration with organic-search programs. BrightEdge is primarily an enterprise software platform; Jellyfish combines technology with managed marketing execution and can coordinate work beyond the SEO function.

iPullRank

iPullRank is a search and content agency with a strong technical and relevance-engineering orientation, including generative-search strategy. It is a meaningful services competitor when a company wants research, technical SEO, content, and implementation rather than monitoring alone. Jellyfish has a larger global marketing-services footprint and Share of Model benchmarking; iPullRank is more specialized around search engineering and content relevance.

10. How Does Jellyfish / Share of Model Market and Sell Its GEO Offering?

Jellyfish uses a research-led, consultative go-to-market model. Share of Model gives the company a source of category findings that can be turned into reports, commentary, articles, presentations, webinars, and executive conversations. Demonstrating how familiar brands appear differently across AI systems makes an unfamiliar measurement problem tangible to marketing leaders.

The company can also sell through its established client relationships. A brand already using Jellyfish for media, organic search, creative, analytics, or digital transformation can add an AI-visibility assessment without selecting an entirely new agency. This cross-sell route is especially relevant because GEO often requires coordination among functions Jellyfish already supports.

The likely sales journey is consultative:

1. identify a category, market, reputation, or discovery problem;
2. define brands, competitors, platforms, geographies, and question themes;
3. run a Share of Model baseline or discovery engagement;
4. present strategic gaps and an implementation roadmap; and
5. expand into recurring monitoring or managed optimization.

Public thought leadership and earned media help establish the Share of Model terminology, while category benchmarks provide an entry point for senior brand and marketing audiences. Enterprise account teams can then scope customized engagements. Public information does not indicate that the core offering depends on a broad free tier or purely product-led conversion. Its complexity, proprietary methodology, and connection to agency services make assisted sales more consistent with the proposition.

This model can support larger, durable relationships, but it also lengthens evaluation and raises the need for methodological transparency. Buyers should request a clear demonstration using their own category and confirm data ownership, platform coverage, reporting frequency, service responsibilities, and renewal terms.

11. Where Does Jellyfish / Share of Model Operate?

Jellyfish is headquartered in London and operates as a global company. Its wider organization has teams and offices across Europe, North America, Latin America, the Middle East, and Asia-Pacific. This footprint supports multinational marketing programs and gives Share of Model access to local market and language expertise beyond a single centralized analytics team.

The major customer opportunity is global rather than limited to the United Kingdom. Large brands need to know whether AI systems describe them consistently across the United States, Europe, Latin America, Asia, and other markets. Geographic comparison can expose differences caused by language, local competitors, regional publishers, product availability, cultural context, regulation, and the varying rollout of AI-search features.

Geographic availability should not be confused with equivalent measurement. An AI product may not offer the same features, search grounding, citations, model version, or access method in every country. Responses can also change with interface language and inferred location. A robust international engagement therefore needs separate prompt sets, local review, and market-specific interpretation rather than direct translation alone.

Jellyfish’s distributed agency model is an advantage for implementation because local teams can adapt content and authority programs. Customers should still verify which languages receive native analysis, whether queries are executed from the relevant country, and whether sentiment or narrative classifiers have been validated for the language in question.

12. Who Owns and Operates Jellyfish / Share of Model?

Jellyfish is a company within The Brandtech Group, a marketing technology group formerly known as You & Mr Jones. Jellyfish joined The Brandtech Group in 2023 through a transaction involving its previous owner, Fimalac. It operates as a global marketing-services brand within the parent group rather than as an independent public company.

Share of Model is a Jellyfish-developed technology and service proposition, not a separate independent company. The directory identity “Jellyfish / Share of Model” therefore combines the operating organization with its principal proprietary GEO measurement capability. Buyers contract for an offering delivered through Jellyfish’s technology, strategy, search, content, media, analytics, and client-service resources.

The Brandtech Group structure matters strategically. It provides access to adjacent capabilities in generative creative technology, media, data, and broader brand transformation. Those relationships may support integrated programs, but capabilities elsewhere in the group should not automatically be attributed to Share of Model unless they are included in a customer’s scope.

Jellyfish’s practical operating model appears multidisciplinary. Technology and data teams maintain the measurement capability; strategists interpret category and brand findings; and channel specialists implement actions. Global account management can coordinate regional teams. Detailed legal entities and contracting arrangements can differ by country, so enterprise customers should confirm the relevant contracting party, data-processing terms, and service responsibilities during procurement.

13. How Does Jellyfish / Share of Model Operate and Integrate with Customer Workflows?

A typical program begins by defining the commercial question. Jellyfish and the customer select the brand, competitors, markets, platforms, languages, and prompt themes to monitor. Prompt families may cover awareness, education, comparisons, recommendations, product attributes, objections, reputation, or purchase considerations. Establishing this taxonomy is strategically important because results describe the sampled question universe, not all AI usage.

Share of Model then collects outputs from the selected systems and processes them for mentions, relative visibility, descriptive themes, sentiment, recommendations, and citations where available. Analysts review patterns and identify potential causes, such as missing owned content, weak entity clarity, stronger competitor evidence, or reliance on third-party sources. Reporting can be organized into dashboards, recurring reviews, executive summaries, and prioritized work plans.

Implementation is mixed:

• Vendor-led: Jellyfish can conduct analysis, strategy, content, technical work, authority programs, and ongoing optimization.
• Customer-led: An internal SEO, content, PR, product, or engineering team can use the findings and implement recommendations itself.
• Shared: Jellyfish can manage measurement and specialist work while customer teams retain publishing, legal approval, product data, and communications responsibilities.

Jellyfish has extensive broader experience with advertising, analytics, search, cloud, and marketing platforms. However, public descriptions of Share of Model do not establish a universal list of native connectors to Google Analytics, Google Search Console, CRM systems, content-management systems, or ecommerce platforms. Such data may be combined through reporting, agency workflows, exports, or project-specific engineering rather than a standard integration available to every customer.

For commercial evaluation, customers should clarify API access, export formats, role-based permissions, historical retention, alerting, source-level data, model and prompt identifiers, and the treatment of personally identifiable or confidential information. They should also define how GEO findings enter editorial calendars, technical backlogs, PR planning, and executive reporting so the platform does not become an isolated dashboard.

14. What Evidence Supports Jellyfish / Share of Model, and What Are Its Limitations?

Company-Published Evidence

Jellyfish has published Share of Model analyses, category comparisons, and explanations showing that brands receive different levels of visibility and different descriptions across large language models. These materials demonstrate that the platform can collect and organize outputs at greater scale than informal manual testing. They also illustrate practical questions for marketers: which competitors appear, which attributes recur, and which information sources are surfaced. Publicly available evidence is stronger as proof of measurement capability than as proof that a particular optimization caused incremental revenue.

Independent Evidence

Independent research on generative search supports several underlying ideas. AI answers can vary by prompt wording and model; retrieval-augmented systems draw on accessible web documents; and content containing clear explanations, evidence, quotations, or statistics may be more likely to supply useful answer passages in some settings. Search-platform documentation also confirms that crawling, indexing, and retrieval affect whether current web information can appear. This evidence supports technical plausibility but does not independently validate Jellyfish’s proprietary score or guarantee results from its programs.

Technical Rationale

Repeatedly querying a defined prompt set is a reasonable way to observe brand representation under controlled conditions. Competitive comparison can reveal patterns that individual screenshots miss. Improving accurate, accessible, well-supported information may also increase the pool of material available to retrieval systems. Monitoring third-party sources is rational because many answer engines cite publishers, reviews, communities, and other domains rather than relying exclusively on a brand’s website.

Limitations

• Sampling: A prompt set is a research panel, not a census of real user demand. Results depend heavily on which questions, competitors, and markets are included.
• Variability: Model outputs can change across repeated runs, accounts, locations, interfaces, model versions, and time. Adequate repetition and timestamps are necessary.
• Interface differences: API output may not reproduce a consumer product with browsing, memory, personalization, citations, or commercial modules.
• Proprietary scoring: A summarized Share of Model score is useful only when its denominator, weighting, exclusions, and classification rules are understood.
• Sentiment ambiguity: Automated labels can oversimplify nuanced, comparative, or conditional statements.
• Citation meaning: A cited page may support one sentence without indicating overall trust or brand preference.
• Recommendation meaning: Being listed as an option does not prove that a user notices, trusts, selects, or buys the brand.
• Attribution: Output changes may result from model updates, new web sources, competitor actions, news, or sampling variation rather than Jellyfish’s work.
• Commercial outcomes: Visibility is an intermediate measure. Traffic, leads, sales, retention, and brand lift require separate measurement.

15. Relevant History

Jellyfish was founded in the United Kingdom in 2005 and developed from a digital marketing business into a global partner spanning media, search, creative, data, technology, and training. Its expansion included international offices and the combination of Jellyfish with French programmatic specialist Tradelab under Fimalac ownership. This history matters to GEO because the offering emerged from an agency with established search, media, and enterprise marketing operations rather than from a newly formed standalone analytics vendor.

In 2023, Jellyfish became part of The Brandtech Group. The same period marked the introduction of Share of Model as a way to measure how large language models represent brands. The timing reflected a rapid change in discovery behavior following broad consumer adoption of generative assistants and the addition of AI-generated answers to search products.

The initial proposition focused attention on measurement: brands lacked a stable equivalent of conventional search share for generative systems. Jellyfish subsequently positioned Share of Model as the diagnostic foundation for broader generative engine optimization. This expanded the offering from observing model perceptions to developing and implementing programs across content, technical search, authority, and brand strategy.

The distinction between company history and product history is important. Jellyfish dates from 2005; Share of Model is a much newer capability created for the generative-AI era. Its methodology and platform coverage should be expected to evolve more quickly than the company’s mature agency services.

16. Growth Opportunities

• Outcome-linked measurement: Connecting Share of Model observations with referral traffic, assisted conversion, CRM outcomes, brand studies, and experiments would help customers distinguish visibility from business value.
• Greater methodological transparency: Clearer documentation of prompt construction, repetition, geographic controls, scoring, and interface collection could improve buyer confidence without disclosing commercially sensitive technology.
• Enterprise workflow integration: APIs, data-warehouse delivery, analytics connectors, editorial integrations, and issue-management workflows could make GEO data part of normal marketing operations.
• International depth: Native-language prompt research, market-specific source analysis, and reliable location controls would use Jellyfish’s global footprint and address a weakness in many English-first monitoring tools.
• Source and crawler intelligence: More detailed analysis of citations, retrieval sources, crawler behavior, and content accessibility could help teams move from a brand-level score to specific actions.
• Vertical applications: Tailored methods for ecommerce, travel, financial services, local discovery, business-to-business buying, and regulated sectors could reflect the different ways AI answers influence decisions.
• Governance services: Large companies need ownership rules for AI-search claims, regulated content, outdated answers, escalation, legal review, and cross-functional reporting. Jellyfish can combine measurement with operating-model design.

The principal strategic risk is commoditization of basic prompt tracking. Many software vendors can collect mentions and citations. Jellyfish’s more defensible opportunity is to combine longitudinal data, global category expertise, and credible execution while proving that its recommendations improve outcomes beyond a proprietary visibility score.

17. How Companies Like Jellyfish / Share of Model Leverage Independent Consultants through Umbrex

Umbrex has built a global community of more than 8,000 independent management consultants based in more than 50 countries, including alumni of McKinsey, Bain, BCG, and other top firms. Companies engage Umbrex when they need experienced consulting talent without necessarily requiring a full traditional consulting team. Umbrex consultants work across strategy, operations, organization, marketing, sales, finance, technology, ERP, analytics, and AI.

Representative projects relevant to a GEO technology and agency business include:

• GEO product strategy: define priority customer problems, product boundaries, platform coverage, and a multi-year roadmap.
• Pricing and packaging: design tiers around brands, markets, prompts, models, data access, and managed services.
• Customer segmentation: quantify opportunities across global brands, mid-market companies, agencies, and specialized industry verticals.
• Go-to-market design: align thought leadership, enterprise sales, account expansion, partnerships, and product-led entry points.
• Competitive intelligence: benchmark specialist GEO platforms, SEO suites, agencies, analytics vendors, and emerging substitutes.
• Measurement framework: connect AI visibility, citations, sentiment, referrals, brand outcomes, pipeline, and conversion without overstating causality.
• International expansion: prioritize markets and develop operating plans for local-language research, sales, implementation, and support.
• Operating-model and governance design: clarify ownership across product, data, agency delivery, customer success, security, and AI governance.