Greenpark has become a verified partner of AI visibility platform Profound, as brands seek greater control over how they appear in answers generated by artificial intelligence services.
The partnership combines Profound’s monitoring and benchmarking technology with Greenpark’s strategy and campaign execution services. The agency will use the platform to assess how clients are represented across ChatGPT, Google AI Overviews, Perplexity, Gemini, Grok, Meta AI, and Copilot.
Those services increasingly influence how consumers discover brands, compare products, and gather information before making a purchase. Unlike a conventional search results page, an AI-generated response may summarise several sources without giving every company equal visibility.
Profound measures citations, brand mentions, competitive position, and changes in the way AI platforms describe an organisation. Greenpark intends to use those findings to develop content and technical strategies designed to improve discoverability.
Chris Pearce, group managing director at Greenpark, said: “Profound has built the definitive platform for understanding how brands show up in AI search. It’s become essential infrastructure for any marketing team serious about this space.
“Partnering with Profound means our clients are working from the best data available, while our team focuses on turning those insights into measurable business results.”
The agency says recent assignments have helped brands progress from limited visibility to becoming more frequently cited within AI-generated answers. It has not disclosed the clients, baseline measurements, time periods, or commercial outcomes associated with those examples.
Trevor Pyle, head of marketing at Profound, said: “Greenpark’s expertise and global scale make them an outstanding addition to our Verified Partner ecosystem.
“As AI becomes a primary channel for brand discovery, organisations need trusted partners that can turn visibility data into action. Greenpark brings exactly that capability to customers.”
Search engine optimisation has traditionally been built around rankings, links, traffic, and conversions that can be observed through relatively established tools. Generative systems create a less transparent environment because answers vary by model, prompt, location, account, and time.
A company can occupy a strong position in conventional search while barely appearing in an AI-generated response. Another may receive extensive exposure because a model relies on an authoritative third-party source rather than the brand’s own website.
Content strategy consequently extends beyond product pages and campaign material. Accurate technical documentation, research, structured information, expert commentary, and independent references may all influence whether a model regards a source as dependable.
The divided experience of generative AI among advertisers has already exposed a gap between widespread experimentation and evidence of substantial commercial results. AI visibility measurement will face the same test, since greater citation frequency must eventually connect to awareness, consideration, leads, sales, or retention.
Attribution is likely to remain difficult. A consumer may encounter a brand in an AI answer and complete a transaction through another channel without producing a conventional referral, making the influence of generative search difficult to isolate from wider marketing activity.
Models also change frequently. An intervention that improves representation on one platform may have little effect on another, while updates to training data, retrieval systems, or citation policies can alter results without any action by the brand.
Marketing teams will therefore need to avoid treating AI visibility as a static ranking exercise. Monitoring must be supported by governance to ensure that product claims, company descriptions, prices, and other information remain accurate across several systems.
Reputational risk forms another part of the discipline. An AI service can reproduce outdated or incorrect information, confuse similarly named companies, or summarise criticism without sufficient context.
Identifying those problems is valuable, although a company’s ability to correct them depends on the sources used by each model and the mechanisms available for updating or challenging inaccurate information.
The commercial market around generative discovery is attracting agencies, analytics platforms, search specialists, and software providers. Some services will concentrate on measurement, while others will sell content production, technical implementation, reputation monitoring, or competitive intelligence.
Greenpark and Profound are entering that market with a combination of data and execution. The durability of the model will depend on whether citation counts and visibility scores can be connected consistently to customer behaviour and financial performance.
You must be logged in to post a comment.