See which business banking accounts are dominating the AI search

Find out which brands are emerging on top in AI search visibility - and where you stand amongst them.

Sample prompts we track for business banking

  • Which business accounts in Singapore can be opened fully online, and what are the usual eligibility criteria?
  • Explain FX spread vs transfer fee with a simple example, and how to estimate total cost for overseas payments.
  • I invoice in foreign currencies. What's the best setup to receive, hold, convert, and pay internationally with low friction?
  • I need team cards with per-person limits and visibility. What capabilities should I look for?
  • What typically triggers account reviews or freezes for business accounts, and how can I reduce the risk?
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A report for AI Visibility Index for all Singapore coworking brands

Frequently Asked Questions

  • What's the difference between being mentioned and being actively recommended by AI?

    Not all brand appearances count as recommendations. Aspire and Airwallex achieved 100% recommendation rates, meaning every single mention actively suggested them as solutions (22 out of 22 mentions). DBS Bank and OCBC Bank achieved 86.4% and 85.0% recommendation rates respectively, with some mentions describing them neutrally without endorsing them. When AI merely mentions a brand versus actively recommends it, the language shifts from "ideal" or "top choice" to "available" or "also offers," weakening positioning in the founder's mind. Brands optimizing for AI visibility should track recommendation rate alongside raw mention count to measure true advocacy.

  • What's the difference between being mentioned and being actively recommended by AI?

    Standard Chartered Bank demonstrates this gap, achieving 90.9% visibility yet scoring only 67.7 on position, a 23-point spread. The bank appeared in nearly every prompt theme, yet AI consistently placed it after other providers in recommendation sequences. This pattern occurs when AI trusts a brand enough to mention it, yet associates it more strongly with secondary use cases rather than primary founder pain points. Closing the visibility-position gap requires repositioning content to address core SME questions directly rather than emphasizing enterprise capabilities that defer consideration.

  • Do different types of providers actually perform differently on sentiment scores?

    Sentiment scores among top performers clustered between 82.2 and 87.9, a 5.7-point range that translates to measurably different language patterns. Providers scoring above 86 (Airwallex at 87.9, Aspire at 87.2, Wise Business at 86.1) consistently triggered "ideal," "top choice," and "highly recommended" descriptions. Those scoring 82-85 received "suitable," "strong option," and "recommended" language, still positive yet less emphatic. The three-point difference between 84 and 87 sentiment may seem small numerically, yet reflects the gap between "good option" and "best choice" in AI-generated recommendations that founders read and internalize.

  • If a brand has perfect visibility, why would it not rank first overall?

    Four brands achieved 100% visibility (Airwallex, Aspire, Wise Business, DBS Bank), yet ranked differently due to sentiment and position scores serving as tiebreakers. Airwallex ranked first with 87.9 sentiment and 85.6 position, meaning it appeared in every prompt with the most positive language and earliest placement. DBS Bank achieved identical 100% visibility yet ranked fourth with 84.9 sentiment and 85.7 position, trailing three providers on sentiment despite matching on early placement. Perfect visibility establishes presence in the conversation, yet sentiment determines how strongly AI endorses a brand, and position determines where it appears in recommendation sequences.

  • How long does it take for content changes to improve AI visibility scores?

    AI visibility reflects training data that models ingest during periodic retraining cycles, typically quarterly for major updates. Brands publishing structured content (comparison guides, onboarding checklists, transparent fee breakdowns) should expect a 90-to-120-day lag before new material influences model outputs. Content published in January may not surface in AI recommendations until April or May retraining. However, high-authority third-party mentions (founder forums, review sites, industry case studies) can accelerate incorporation if they appear on sites the model indexes frequently. Providers tracking AI visibility should measure quarterly to detect shifts and attribute them to specific content initiatives launched 3-4 months prior.

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