The specialist AEO and GEO agency, with our own AI search monitoring platform.
Focus vertical — Financial Services.

Generative engine optimisation, or GEO, is the work of getting your products surfaced, described accurately and recommended inside answers from ChatGPT, Claude, Gemini and other AI tools. For financial services, this matters more than in almost any other category. Buyers ask AI for specific numbers, specific terms and specific recommendations, and they act on what AI tells them. A wrong rate, an outdated fee or a miscategorised product is the difference between an application and a lost customer, and in some cases the difference between a sale and a complaint.

Because the answers AI gives about financial products are specific, numerical and consequential. Other categories can tolerate an AI response that is roughly right. Financial services cannot. If ChatGPT quotes a rate that is two years old, a credit limit that no longer applies or describes a regulated product as unregulated, the consumer either makes a decision on bad information or loses trust in the brand. Both outcomes are expensive, and the second one can create regulatory exposure on top of the commercial loss.

Answer engine optimization (AEO) and SEO operate on entirely different signals. SEO is about ranking pages on Google. AEO is about how language models describe your products when a consumer or business asks for a recommendation. Google reads pages and links. Language models draw on training data, third-party citations, structured product information and how clearly and consistently your rates, terms and trust signals are described across the web. A financial brand can dominate Google in its category and be almost invisible inside ChatGPT and Claude responses, and the brands that do well in AI search are often not the ones with the strongest SEO.

This is one of the most common AEO problems in financial services, and the fix is a layered one. Outdated rates and terms usually persist in AI search because the old number is still sitting in cached pages, archived press coverage, third-party comparison sites and review platforms that language models trust as sources. Refreshing your own rate pages is the starting point, but it is not enough on its own. You also need structured, citable rate content, corrections submitted to the comparison and review platforms AI tools cite most heavily, and ongoing monitoring to catch the next round of outdated content before it spreads.

Because product categories in financial services are easy to confuse and language models default to the nearest familiar category when the signal is ambiguous. Credit cards get described as debit cards. Investment accounts get described as savings accounts. Business products get grouped with consumer ones. The fix is to align the category signal across your product naming, on-site copy, structured metadata, third-party listings and the content AI tools cite when describing your category. The clearer and more consistent the signal, the less room language models have to default to the wrong category.

Both, and the boundary is moving fast. A growing share of financial purchase journeys now begin with an AI query. "What's the best bank for freelancers." "Which savings account has the highest rate in Germany right now." "Is this fintech regulated and where are my deposits protected." Consumers and business buyers increasingly use AI tools to build a shortlist first, then go to comparison sites or directly to a brand's website to verify. Brands that appear consistently in those AI-generated shortlists get evaluated earlier, more often and on better terms than brands that have to be discovered downstream.

Yes, and compliance is built into how we run engagements in this category. Every piece of content we recommend, produce or seed externally goes through your internal compliance and legal review process before it goes live. We work with the constraints your regulator imposes, we flag where AI tools are generating content about your products that may itself be a compliance concern, and we structure the GEO work so that accuracy and trust signals are reinforced rather than weakened. In most engagements, the GEO work also improves compliance posture by surfacing where outdated or inaccurate information about your products is circulating online.

By making the trust signal visible, structured and citable, and by reinforcing it across the sources AI tools actually rely on. That means restructuring how licensing and regulatory information appears on your own site, adding structured data, updating profiles on third-party financial platforms, and seeding accurate regulatory descriptions through trade media and review platforms. When the trust signal is consistent across the surfaces AI cites, language models include it in their answers. When the signal is scattered or buried, AI either omits it or, worse, fills the gap with inferred information that may be wrong.

Some changes show up in AI search quickly. Numerical corrections, structured content updates and third-party listing fixes often surface in AI responses within four to eight weeks once language models re-index the affected sources. Deeper shifts, like building category presence in shortlists or correcting persistent miscategorisation, typically take eight to sixteen weeks. Most financial services engagements we run show measurable movement on numerical accuracy, category placement and Share of Voice inside the first 90 days.

Every engagement starts with AEO monitoring tailored to your products, markets, regulatory context, ICP and competitors, run through our in-house platform. From there we audit your GEO readiness across three layers (technical foundations, content and documentation including rate and product disclosures, third-party presence and citations), identify the sources that are actually shaping AI answers in your category, and deliver a prioritised 90-day GEO roadmap. Then we execute, either alongside your team or as a fully outsourced function, with full attention to your compliance review process. The work covers website and semantic structure, product and rate pages, disclosure and documentation, AI-oriented content, comparison and review platform visibility, regulatory and trust signal reinforcement, and ongoing optimisation as AI search behaviour shifts.