Brandlight

Executive Overview

Brandlight is an AI-search visibility and optimization platform designed to help marketing teams understand how brands appear in generative answers and turn those observations into content and technical actions. Its Visibility Insights offering monitors brand presence, sentiment, sources, citations, and competitors across major AI discovery experiences. Adjacent workflows help users diagnose content gaps and website issues that may affect retrieval or representation. The company primarily addresses enterprise brand, SEO, content, and digital marketing teams rather than consumers. Its commercial model appears to be demo-led software sold on a recurring basis, with onboarding and customer support; public pricing and detailed contract terms are not prominently disclosed. Brandlight is U.S.-based and positions the platform for geographically distributed organizations, although its public product materials provide limited detail about its corporate office footprint. Within the technology provider landscape, Brandlight sits between specialized AI-visibility trackers and broader enterprise SEO suites. Its principal differentiation is the combination of answer monitoring, source and competitor intelligence, sentiment analysis, and workflows intended to connect measurement with content and technical execution.

Brandlight at a Glance

Logo
Name	Brandlight
Segment	GEO platforms with optimization workflows
Headquarters	United States; headquarters city is not prominently identified in the reviewed product materials
Organization type	Enterprise AI-search visibility and GEO software platform
Official website	Brandlight

1. What Is the GEO Strategy of Brandlight?

Winning Aspiration

Brandlight addresses a problem created by the migration of discovery from conventional search-result pages toward generated answers: organizations often cannot see when an AI system mentions them, omits them, mischaracterizes them, or relies on sources they do not control. Brandlight’s strategic role is to make this environment measurable and to help teams convert findings into practical content and technical work. A reasonable interpretation is that it wants to become an operating layer for managing brand performance in AI search, not merely a dashboard that counts mentions.

Where It Plays

The platform plays primarily in enterprise marketing, brand management, SEO, content, and digital intelligence. It covers the middle of the GEO workflow: monitoring answers, comparing brands, inspecting sentiment and cited sources, diagnosing gaps, and organizing optimization work. Its natural buyers are organizations with meaningful brand search demand, several product or topic areas, multiple competitors, or a need to coordinate teams across markets. Brandlight monitors prominent conversational and generated-search systems rather than building a consumer answer engine of its own.

How It Tries to Win

Brandlight combines four views that are sometimes sold separately: visibility in generated answers, interpretation of brand portrayal, intelligence about sources and competitors, and action-oriented content or technical workflows. That combination can reduce the handoff between analysts who identify an AI-search problem and practitioners responsible for fixing websites or content. Its enterprise positioning also emphasizes comparisons over time and across topics rather than one-time manual testing.

Required Capabilities

Executing this strategy requires reliable prompt management, repeated collection of changing AI answers, brand and competitor entity recognition, citation extraction, sentiment classification, website analysis, and usable recommendations. It also requires normalization across platforms whose interfaces, retrieval methods, citation conventions, and regional behavior differ. Historical storage is important because individual answers are volatile and become more meaningful when viewed as trends.

Management Systems

Relevant disciplines include prompt-set governance, quality checks for entity and sentiment classification, monitoring of AI-platform changes, security controls for enterprise accounts, and customer-success processes that translate data into action. Brandlight does not publicly disclose every internal control or scoring rule, so prospective customers should assess sampling, data retention, permissions, and methodology during procurement.

2. What Are the Strategic Priorities of Brandlight?

Brandlight does not publish a complete corporate roadmap. The following priorities are analytical interpretations of its product design and market positioning rather than a claim that the company formally labels them this way.

• Move from monitoring to execution. Visibility data becomes more useful when it identifies what a team should change. Brandlight’s content and technical workflows indicate an effort to connect answer intelligence with work that SEO, content, and web teams can perform.
• Build an enterprise system of record for AI visibility. Historical comparisons, competitor benchmarks, source analysis, and topic-level reporting can make the platform relevant to recurring planning and reporting rather than occasional experiments.
• Expand source intelligence. Knowing which domains, pages, publishers, and content patterns appear in generated answers helps customers distinguish an owned-site problem from a broader authority or earned-media problem.
• Broaden platform and market coverage. AI-search behavior differs by model, interface, geography, and language. Continued coverage expansion is strategically important for customers managing multinational brands.
• Improve proof of business value. The category will increasingly need to connect visibility changes with qualified visits, brand outcomes, leads, or revenue. Analytics integrations and disciplined test design would strengthen that connection.
• Preserve comparability as AI products change. Answer engines frequently alter models, retrieval systems, citations, and user interfaces. Brandlight must update collection methods without making historical trends misleading.

3. What Is the Business Model of Brandlight?

Brandlight appears to operate principally as enterprise software rather than as a conventional SEO agency. Customers buy continuing access to a platform that monitors selected topics, prompts, brands, competitors, sources, and AI systems. The public website uses a consultative, demonstration-led sales motion; it does not prominently present a standardized self-service price list.

The economic buyer is likely to be a senior digital marketing, brand, growth, content, or SEO leader. Daily users may include search strategists, content managers, analysts, communications teams, and web-operations personnel. Commercial packaging can reasonably be expected to reflect the scope and cost of monitoring—such as the number of topics, competitors, markets, platforms, users, or retained observations—but Brandlight’s exact pricing formula should not be inferred without a proposal.

Value is created by replacing manual answer checking with systematic observation, aggregating fragmented signals, and helping teams prioritize work. Repeat use is driven by answer volatility, new product launches, competitor activity, changes in cited sources, and the need to demonstrate progress over time. Onboarding and customer support can also be important because prompt design and competitor taxonomies materially affect what the dashboard measures.

Switching costs are operational rather than absolute. A customer can move to another tracker, but it may lose historical baselines, established prompt sets, classifications, reports, and team routines. The costs become higher when Brandlight data is embedded in executive reporting or content planning. Public information does not establish that Brandlight routinely delivers full-scale content production, digital PR, or technical implementation as a managed service; those activities should be distinguished from software-generated recommendations and customer-success guidance.

4. What GEO Products and Services Does Brandlight Offer?

Visibility and Brand Intelligence

Visibility Insights is the core monitoring layer. It is designed to show whether and how a brand appears in AI-generated responses for relevant questions. The platform brings together brand presence, relative visibility, sentiment or portrayal, and comparisons with named competitors. This allows a user to move beyond checking isolated prompts and instead review patterns by topic, platform, or period.

Source and Citation Intelligence

Brandlight examines the sources associated with generated answers. This can reveal which websites or pages are repeatedly cited, whether a customer’s own domain is represented, and which third-party sources appear to shape an answer category. Source intelligence is particularly useful when a competitor is visible because of publishers, review sites, communities, or reference material rather than superior content on its own website.

Content Workflows

The platform’s content-oriented workflows are intended to identify coverage gaps and opportunities to improve existing or planned material. Typical uses include finding questions a site does not answer clearly, determining where competitors receive stronger representation, and prioritizing pages for refresh or creation. These workflows support human planning; they do not establish that a generated article will be retrieved or cited by an AI system.

Technical Workflows

Brandlight also addresses technical factors that can affect whether content is accessible, understandable, and discoverable. This can include checks or guidance related to crawlability, page structure, metadata, structured information, and other website foundations. Technical readiness is an enabling condition, not a guarantee of inclusion. An AI service may still rely on different indexes, retrieval partners, external sources, or model knowledge.

Reporting and Competitive Analysis

Dashboards and trend reporting help teams communicate changes to stakeholders and compare performance with competitors. Brandlight’s broad value lies in bringing these analytical views into one workflow. Prospective customers should verify the availability of exports, role-based access, APIs, custom reporting, and service support for their intended plan.

5. Which Information Routes and AI Platforms Does Brandlight Address?

Brandlight addresses several routes through which information can reach an AI-generated answer. It measures some routes directly and can only influence others through recommended marketing or technical action.

• Generated answers and live retrieval: The platform observes answers returned for monitored questions and extracts brand, competitor, sentiment, and source signals. This measures sampled responses, not every answer shown to every user.
• Owned website content: Content analysis helps users determine whether their sites provide clear, relevant material for target questions. Better content can improve retrievability, but Brandlight cannot compel an answer engine to use it.
• Crawling and indexing: Technical workflows can identify barriers that may prevent search or AI-related crawlers from accessing useful pages. Access enables discovery; it does not guarantee indexing, retrieval, citation, or model training.
• Third-party citations and publishers: Source intelligence shows where answer engines appear to obtain supporting information. Customers may respond with digital PR, publisher outreach, stronger reference material, or partnerships, although the software does not control independent publishers.
• Entities and structured information: Clear organization, product, and relationship data can reduce ambiguity. Structured data may help machines interpret a page, but it is not an instruction that an AI system must cite or recommend the entity.
• Competitive information: Comparing sources and answer language can reveal where rivals have stronger content coverage, authority, or third-party validation. It does not disclose an AI provider’s private ranking or model weights.

Brandlight’s public positioning centers on prominent AI-search experiences including ChatGPT, Google AI Overviews and related Google AI experiences, Gemini, Perplexity, and Microsoft Copilot. Coverage should be confirmed for the customer’s required country, language, device, and product tier because a named platform can expose several interfaces with different retrieval behavior. Brandlight should also be evaluated on whether observations come from consumer interfaces, approved APIs, or another collection method; these routes may not produce identical outputs.

6. How Does Brandlight Measure GEO Performance?

Brandlight turns sampled AI responses into answer-level observations and aggregated indicators. The most important distinction is between directly observed data and derived metrics.

• Brand presence: Whether the monitored brand appears in an answer. This is directly observable within the sampled response.
• Visibility or share of voice: An aggregation of appearances relative to prompts, competitors, or both. The result depends on the prompt set, weighting, run frequency, and treatment of partial mentions.
• Prominence and recommendation: A brand may be named, discussed prominently, included in a list, or explicitly recommended. These are different states and should not be reduced to a binary mention when decision quality matters.
• Sentiment or portrayal: Classification of answer language as favorable, neutral, mixed, or unfavorable. This is inferred by an analytical model and may require human review for nuanced or regulated subjects.
• Citations and sources: Domains or pages displayed as supporting material. Citations are observable when the interface provides them, but a visible citation does not prove that the source caused every part of the answer.
• Competitor comparison: Relative presence, portrayal, and source patterns across the same monitored questions.
• Trend data: Changes across repeated collection periods. Trends are generally more informative than a single run, provided the prompt set and collection method remain comparable.

Customers may organize measurement around their own topics, products, competitors, and business questions. Public product descriptions do not fully specify how prompts are generated, how often each prompt is repeated, whether prompt demand is modeled, or how Brandlight handles stochastic variation. Nor is every proprietary score formula publicly documented. Buyers should request definitions, weighting logic, geographic controls, historical backfilling rules, and confidence measures.

GEO metrics are not equivalent to market demand. A large prompt library may offer broad diagnostic coverage without showing how frequently real consumers ask those questions. Likewise, visibility is not traffic, a recommendation is not a conversion, and a citation is not evidence of commercial impact. Business measurement requires separate referral, engagement, lead, and sales data.

7. How Does Brandlight Try to Improve AI Visibility or Favorability?

Brandlight’s optimization approach begins with diagnosis. Users identify topics where the brand is absent, weakly represented, portrayed unfavorably, or supported by less authoritative sources than competitors. The platform then helps translate those patterns into content and technical priorities.

• Content creation and refresh: Teams can address unanswered questions, improve factual clarity, add evidence, or update outdated pages. This is technically plausible because retrieval systems need accessible passages that correspond to a user’s question.
• Comparison and decision content: When relevant and accurate, product comparisons, use-case explanations, limitations, specifications, and selection criteria can help answer engines resolve evaluative questions. Such content should be substantive rather than written solely to repeat a brand name.
• Technical remediation: Fixing blocked resources, weak internal linking, unclear page structure, duplicate content, or machine-readable inconsistencies can improve discoverability and interpretation.
• Entity consistency: Consistent names, descriptions, product relationships, and organizational facts across owned and reputable third-party properties can reduce ambiguity.
• Source strategy: Citation analysis can identify publishers, directories, communities, or reference sources that recur in answers. A marketing or communications team may use this intelligence to guide legitimate outreach, original research, or expert contributions.
• Competitive prioritization: Teams can focus on prompts where a competitor advantage appears addressable rather than treating all visibility gaps as equally important.

These mechanisms can increase the availability and credibility of information, but they do not give Brandlight control over a model’s output. AI providers choose their own indexes, retrieval systems, policies, ranking signals, and answer composition. Publicly reported improvements should therefore be treated as vendor or customer evidence unless supported by controlled independent testing. Implementation also appears primarily customer-led: Brandlight supplies intelligence and workflows, while content, engineering, PR, and governance teams make the changes.

8. Who Uses Brandlight?

Brandlight is most relevant to organizations for which brand discovery, category consideration, or reputation is materially affected by AI-generated answers. Its enterprise orientation suits companies that need repeatable reporting across a large question set rather than occasional manual checks.

• SEO and search teams use it to extend conventional rank tracking into answer visibility, citation analysis, and AI-specific technical reviews.
• Content strategists use gaps, competitor patterns, and source data to prioritize briefs, page updates, explanatory content, and original research.
• Brand and communications teams examine how an organization is characterized, which narratives recur, and whether unfavorable or inaccurate descriptions require attention.
• Digital marketing leaders use aggregated reporting to assess exposure across AI channels and decide where to allocate people and budget.
• Product and competitive-intelligence teams compare how AI systems describe alternatives, strengths, weaknesses, features, and use cases.
• Agencies and consultants can potentially use the platform to conduct audits and recurring client reporting, although buyers should verify whether Brandlight offers dedicated multi-client administration or agency packaging.

High-value use cases include new product launches, category-entry analysis, reputation monitoring, multinational brand reviews, source audits, and content-roadmap development. The software is horizontal rather than limited to one vertical. Consumer brands, technology providers, financial or professional services firms, healthcare organizations, retailers, and other information-intensive businesses can face the underlying problem, although sector-specific evidence, compliance, and measurement requirements differ.

Brandlight is less necessary for a small organization with very few relevant prompts and no capacity to act on findings. In that case, periodic manual research or a lighter monitoring tool may be sufficient. Its value rises when several teams need a shared dataset and when content, technical, and communications resources are available to implement recommendations.

9. Who Competes with Brandlight?

Profound

Profound is a direct enterprise peer focused on how brands appear across AI answer engines, including visibility, citations, prompt intelligence, and emerging agent-related discovery. It competes for central AI-search measurement budgets. Brandlight’s positioning places particular emphasis on connecting brand and source intelligence with content and technical workflows; buyers should compare data collection, global coverage, reporting, and implementation support.

Scrunch AI

Scrunch AI overlaps in AI visibility monitoring and in helping organizations make their digital content more usable by AI agents and answer systems. Its market identity has emphasized the customer experience that AI systems construct from brand information. The comparison matters for customers seeking both measurement and website-readiness guidance rather than a traditional rank tracker.

Conductor

Conductor is an enterprise organic-marketing and SEO platform that has expanded into AI-search monitoring and optimization. It can be a substitute for customers that prefer AI visibility inside an established content and search workflow. Brandlight is more narrowly identified with the AI-search problem, while Conductor offers a broader conventional SEO and digital-content operating environment.

BrightEdge

BrightEdge combines enterprise SEO data, content recommendations, technical capabilities, and AI-search analysis. It competes when a large company wants one provider for both traditional search and generative discovery. Brandlight may appeal to buyers seeking a more dedicated GEO layer, while BrightEdge benefits from integration with long-standing enterprise search workflows and datasets.

Semrush

Semrush provides a broad digital-marketing suite with AI visibility, brand monitoring, competitor research, SEO, content, and advertising capabilities. It is a practical substitute for teams that value breadth and an existing Semrush workflow. Brandlight’s comparative case rests on enterprise-focused AI-search analysis and the depth of its visibility-to-action process rather than on offering the widest marketing toolkit.

Peec AI

Peec AI is a specialized answer-engine visibility platform used to monitor brand mentions, rankings, sentiment, competitors, and cited sources. It overlaps closely at the measurement layer and is relevant to brands and agencies seeking focused monitoring. Buyers should compare enterprise controls, regional coverage, prompt methodology, workflow depth, support, and total monitoring scale rather than relying on headline feature lists.

10. How Does Brandlight Market and Sell Its GEO Offering?

Brandlight’s public go-to-market motion is primarily category education followed by consultative sales. Product pages explain the shift from conventional search to generated answers, frame the risks of limited or unfavorable brand representation, and demonstrate the kinds of visibility, sentiment, source, and competitor questions the software can address. This helps prospective buyers define a problem that may not yet have an established budget line.

The sales process appears demo-led rather than centered on transparent self-service checkout. That model fits an enterprise product because effective deployment requires decisions about topics, brand entities, competitors, markets, languages, reporting needs, and internal owners. A discovery process also allows Brandlight to scope monitoring and explain where its platform ends and customer implementation begins.

Educational content and product-led demonstrations can attract SEO, content, and brand practitioners, while executive messaging expands the conversation to reputation, market intelligence, and customer acquisition. Reports, webinars, events, and practitioner discussions are natural channels for a category in which methods and platforms change quickly; their value depends on whether Brandlight publishes enough methodological detail to make findings interpretable.

Customer success is likely an important component of retention. A dashboard can lose value if a company monitors the wrong prompts or produces reports without an action owner. Onboarding that helps customers establish a prompt taxonomy, benchmark competitors, select priority markets, and define an operating cadence supports adoption. Public information does not establish a large reseller or implementation-partner network, so prospective agency and technology partners should verify available channel arrangements directly.

11. Where Does Brandlight Operate?

Brandlight is a U.S.-based software provider. The reviewed public product materials do not prominently establish a specific headquarters city or an extensive office network, so a more precise corporate location should not be inferred from the website URL or customer availability.

Because the product is delivered as cloud software, its potential customer footprint is broader than its physical office footprint. Brandlight positions the offering for organizations managing AI-search performance across multiple topics and markets. International usefulness, however, depends on more than access to a web application. AI outputs can change with language, country, location settings, model version, search index, and product interface.

Multinational buyers should confirm which combinations of platform, country, and language Brandlight can test natively; whether prompts are translated or independently localized; where collection appears to originate; and whether results can be segmented by market. Direct translation alone may not represent how local customers phrase questions or which publishers local retrieval systems trust. Regional teams may therefore need to help design the prompt set and validate sentiment classifications.

The same issue applies to regulatory and data requirements. Enterprise customers should evaluate data hosting, access controls, retention, and contractual provisions for the regions in which they operate. Geographic availability of Brandlight software should not be assumed to mean identical coverage or measurement quality for every AI platform in every country.

12. Who Owns and Operates Brandlight?

Brandlight is presented publicly as an independently operated software company and platform, not as a feature name inside a larger SEO suite. Its directory identity therefore refers to both the provider and its GEO offering. The reviewed product materials do not indicate that Brandlight is an acquired subsidiary or a rebranded product owned by a major search, advertising, or marketing-software group.

Detailed ownership, capitalization, and legal-entity information is not central to Brandlight’s public product positioning. In the absence of fuller disclosure, it is appropriate to describe the organization as a privately operated AI-search software provider without speculating about ownership percentages or investor control. Customers conducting enterprise procurement should confirm the contracting entity, data-processing terms, insurance, security responsibilities, and continuity provisions.

Operationally, the business needs several closely connected functions: product and engineering teams that maintain answer collection and analysis; data or applied-AI specialists responsible for classifications and scoring; subject-matter experts in SEO, content, and brand; enterprise sales; and customer-success personnel who help customers configure useful monitoring programs. This is an inferred capability model, not a published Brandlight organization chart.

Brandlight appears to have been developed specifically for the AI-search and GEO market rather than retrofitted from a decades-old SEO product. That focus can allow faster adaptation to emerging answer engines, but it also means buyers should examine company maturity, historical data depth, service capacity, and long-term platform resilience as part of vendor due diligence.

13. How Does Brandlight Operate and Integrate with Customer Workflows?

A typical Brandlight operating cycle begins with configuration. The customer defines its brands, products, competitors, priority topics, questions, markets, and relevant AI platforms. The quality of this taxonomy matters: a narrow or biased prompt set can produce precise-looking results that do not represent the wider customer journey.

Brandlight then collects responses, identifies entities and source references, classifies portrayal or sentiment, and aggregates observations into dashboards and comparisons. Users review changes by prompt, topic, competitor, platform, or period. Source patterns and content gaps feed into an action queue for content, SEO, communications, or technical teams.

Implementation is best understood as mixed:

• Brandlight-led elements include platform operation, data processing, product support, and potentially configuration guidance.
• Customer-led elements include deciding which questions matter, validating findings, approving content, changing websites, conducting outreach, and measuring commercial outcomes.
• Shared elements include interpreting unusual shifts, revising prompt groups, setting benchmarks, and establishing reporting routines.

The reviewed public product information does not provide a definitive catalog of native integrations with Google Analytics, Google Search Console, Bing Webmaster Tools, specific content-management systems, customer relationship management platforms, or business-intelligence tools. Buyers should therefore verify APIs, exports, scheduled reporting, authentication, and connector availability rather than assume them. The same applies to server-log ingestion and direct AI-crawler analytics.

In practice, the most useful workflow connects Brandlight findings with an existing content calendar, technical backlog, digital PR plan, and executive scorecard. Teams should assign an owner to each action and annotate major changes. Without those disciplines, the platform can show movement but cannot explain whether a content release, model update, competitor campaign, or random answer variation caused it.

14. What Evidence Supports Brandlight, and What Are Its Limitations?

Company-Published Evidence

Brandlight’s strongest public evidence is its product-level demonstration of a coherent measurement framework: monitoring brand presence, sentiment, sources, citations, and competitors, then linking findings with content and technical workflows. Product demonstrations and company-published examples can show that the platform captures and organizes these signals. They do not by themselves prove that recommended actions caused subsequent changes in AI answers or business outcomes. Publicly accessible materials provide less methodological detail than would be required to reproduce every score independently.

Independent Evidence

Independent research on generative engine optimization suggests that content characteristics such as clarity, factual support, quotations, statistics, and relevance can affect visibility in controlled generative-search settings. Search and retrieval research also supports the importance of crawlability, passage relevance, entity clarity, and authoritative sources. This evidence makes Brandlight’s general approach plausible, but it is not independent validation of Brandlight’s collection system, sentiment classifications, recommendations, or proprietary metrics.

Technical Rationale

Repeatedly sampling answers is more informative than manually checking a few questions. Citation extraction can reveal the information ecosystem around a topic, and competitor comparisons can expose addressable gaps. Content and technical changes may improve the probability that a relevant passage is crawled, indexed, retrieved, or trusted. Historical data can help separate persistent patterns from one-off outputs. These mechanisms support probabilistic improvement rather than control.

Limitations

• Prompt sampling: A monitored prompt set is a designed panel, not a complete record of real customer demand.
• Answer variability: The same prompt may return different answers because of model updates, retrieval changes, personalization, location, or randomness.
• Collection route: API output may differ from a consumer product interface. Method changes can disrupt time-series comparability.
• Visibility scores: Aggregates depend on weighting choices and can conceal important differences between a passing mention and a strong recommendation.
• Sentiment: Automated classification can miss context, irony, mixed statements, or sector-specific meaning.
• Citations: A citation is neither proof of causation nor proof that the source supports every generated claim.
• Attribution: A rise after an optimization does not prove the optimization caused it; model and competitor changes may occur simultaneously.
• Commercial impact: Visibility does not establish traffic, conversion, customer preference, or revenue. Those outcomes require separate measurement.

15. How Companies Like Brandlight 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 platform provider include:

• GEO product strategy: Define priority use cases, platform coverage, workflow boundaries, and a differentiated product roadmap.
• Pricing and packaging: Design plans around prompts, markets, platforms, seats, data history, service levels, and enterprise requirements.
• Customer segmentation: Identify the most attractive industries, buyer roles, company sizes, and maturity profiles for focused growth.
• Go-to-market design: Refine positioning, category education, sales stages, demonstration structure, and proof-of-value programs.
• Competitive intelligence: Benchmark specialized GEO platforms, enterprise SEO suites, agencies, and emerging substitute technologies.
• International expansion: Prioritize countries and languages using customer demand, platform availability, localization needs, and commercial feasibility.
• Measurement framework: Connect AI visibility metrics with content actions, referral behavior, leads, revenue, and controlled experimentation.
• Operating-model and AI governance: Clarify ownership across product, data, customer success, security, methodology, and responsible-AI review.

These are examples of work that may suit a company in Brandlight’s category; they do not imply that Brandlight has engaged Umbrex.