Fintech B2B Infrastructure AI Visibility Index 2026

Stripe, Inc. leads this B2B Fintech Infrastructure measurement with a 63.9% appearance rate across answers returned. Its 95% interval is 58.2% to 69.2%. Plaid Inc. ranks second at 42.7%, but has the highest owned-domain citation rate among the leading brands at 11.5% of answers with retrieval evidence. The data separates being named from being cited.

This segment includes B2B financial technology infrastructure, API gateways, and enterprise billing tools. It is one segment in the Fintech AI Visibility Index 2026. It does not cover Payments & Processors, Trading & Investing Platforms, or BNPL & Consumer Credit.

The gap matters because a brand can appear in many generated answers while its own site supplies little of the supporting evidence. Citation rates use answers with retrieval evidence. Appearance rates use answers returned.

Which brands lead the B2B fintech infrastructure leaderboard?

Stripe, Inc. ranks first with a 63.9% appearance rate. Plaid Inc. ranks second at 42.7%, followed by Adyen at 41.0%, Marqeta, Inc. at 37.2%, and Unit, Inc. at 26.7%. Stripe appears first in an average position of 2.3 when it is named.

The leaderboard measures appearances across 288 returned answers. It ranks visibility in this prompt set, not market size, consumer demand, or product quality.

Appearance leaderboard

Appearance rate is the share of the 288 returned AI answers in which each brand was named.

Stripe's interval does not overlap Plaid's interval. Plaid's 37.1% to 48.5% interval overlaps Adyen's 35.4% to 46.7% interval and Marqeta's 31.8% to 42.9% interval. Adyen and Marqeta also overlap. Those differences are within the margin of this sample. Unit's 22.0% to 32.1% interval overlaps Marqeta's interval at the edge.

The leaders differ by engine and by the extent to which their own domains appear as evidence. The profiles below report appearances across returned answers and citations across answers with retrieval evidence.

Where do the engines disagree?

The engines disagree sharply for several brands. Each engine returned 72 answers. Perplexity, ChatGPT, and Claude had retrieval evidence for all 72 answers. Gemini had retrieval evidence for 70 of 72 answers. Differences may reflect model and retrieval-tool design, rather than a stable measure of category-wide visibility.

Engine appearance patterns

Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row.

Stripe is broad but uneven: Perplexity named it in 81.9% of its 72 answers, ChatGPT in 80.6%, Gemini in 54.2%, and Claude in 38.9%. Plaid follows a similar pattern, with 62.5% on ChatGPT and 13.9% on Claude. Adyen and Marqeta also have much lower Claude rates than Perplexity and ChatGPT.

Several brands are concentrated in one system. Airwallex appears in 63.9% of Perplexity answers but 1.4% of ChatGPT answers, 5.6% of Gemini answers, and 2.8% of Claude answers. Galileo appears in 51.4% of ChatGPT answers but 2.8% of Perplexity and Claude answers. Modern Treasury appears in 38.9% of Gemini answers, compared with 18.1% on Perplexity, 15.3% on ChatGPT, and 8.3% on Claude.

Treasury Prime appears in 48.6% of Perplexity answers and 40.3% of ChatGPT answers, but 4.2% on Gemini and 1.4% on Claude. Tipalti's 47.2% Perplexity rate falls to 15.3% on ChatGPT, 12.5% on Gemini, and 5.6% on Claude. Synapse appears in 26.4% of ChatGPT answers and 0% in the other three engines. Tradier appears in 0% of the 72 answers from every engine.

Which demand clusters produce each brand's visibility?

The cluster chart separates the kinds of questions in this study and identifies which brands lead them. Use it to distinguish broad visibility from visibility tied to a narrower kind of request.

Demand-cluster leaders

The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count.

The measured data supplied for this report does not include cluster values in text form. The chart is the record of cluster-level results. It should be read as a map of where the brands lead within the question groupings shown, not as evidence about unmeasured requests.

Which owned domains and publishers supply evidence?

Plaid Inc. has the highest owned-domain citation rate among the leading brands at 11.5% of answers with retrieval evidence. Stripe, Inc. is close at 10.8%. Lower-ranked brands can show stronger citation performance than their appearance rank suggests: Ramp has an 8.0% citation rate at rank 12, while Tipalti has 7.3% at rank 7 and Modern Treasury has 7.0% at rank 8.

Owned-domain citation rates

Share of the 286 answers with retrieval evidence that cited a URL on the brand's own website.

Owned-domain citations are not the same measure as brand appearances. Plaid's 33 owned-domain citations exceed Stripe's 31, although Stripe is named in 184 returned answers and Plaid in 123. Coupa and Currencycloud each have a 0.3% owned-domain citation rate. Payoneer and Tradier each have a 0% owned-domain citation rate.

Publishers cited in answers

Publishers cited as evidence, counted once per answer rather than once per link.

Third-party sources appear often in the evidence set. connectpay.com was cited in 112 answers, or 39.2% of answers with retrieval evidence. fintechspecs.com appeared in 91 answers, or 31.8%. openbankingtracker.com appeared in 62 answers, or 21.7%. Plaid.com appears among the listed sources with 31 cited answers and 10.8% answer penetration.

Who is winning and who is at risk?

Brands with the strongest measured position

Stripe, Inc. has the strongest measured appearance position. It ranks first at 63.9%, has a 2.3 average first-mention position, and is named across all four engines. Plaid Inc. ranks second at 42.7% and has the highest listed owned-domain citation rate at 11.5%. Both brands have 100% cross-engine consistency.

Adyen and Marqeta remain in the top four with 41.0% and 37.2% appearance rates. Their intervals overlap, so this sample does not establish one as more visible than the other. Tipalti, Modern Treasury, and Ramp have citation rates of 7.3%, 7.0%, and 8.0%, respectively, which are stronger than several brands with higher appearance ranks.

Brands with measured exposure risk

Tradier has a 0% appearance rate across 288 returned answers and a 0% owned-domain citation rate among answers with retrieval evidence. It is absent from every engine's 72-answer set. Payoneer has a 7.3% appearance rate and 0% owned-domain citation rate. Coupa has a 10.8% appearance rate and a 0.3% owned-domain citation rate.

Synapse has a 6.6% appearance rate, a 4.3% to 10.1% interval, and 25% cross-engine consistency. It is named only by ChatGPT in this measurement. Currencycloud has an 8.3% appearance rate and is absent from Gemini and Claude. These patterns create exposure to system choice within the measured prompts.

What does the tally say about this category?

The top of this leaderboard is concentrated around Stripe, Inc. Stripe's 63.9% appearance rate is separated from Plaid Inc.'s 42.7% interval. Below that lead, the intervals for Plaid, Adyen, Marqeta, and Unit overlap in sequence, so the rank order does not settle every comparison among those brands.

The engines converge for several large brands because Stripe, Plaid, Adyen, Marqeta, and Unit each have 100% cross-engine consistency. They diverge in frequency. Airwallex, Galileo, Treasury Prime, Tipalti, Modern Treasury, Synapse, and Currencycloud show engine-specific concentrations or absences.

Third-party publishers carry much of the visible citation evidence. connectpay.com and fintechspecs.com each appear in more retrieval-backed answers than any listed owned brand domain. Among the named brands in the brief, AvidXchange, Inc. has an 8.7% appearance rate and 0.7% owned-domain citation rate. Tradier is measured at 0% for both appearance and owned-domain citation rate. Plaid Inc. is measured at 42.7% appearance and 11.5% owned-domain citation rate.

What changed during the measurement window?

The data records one measurement window, not changes over time. It does not show a before-and-after shift for this category.

What actions address the measured gaps?

Use the next measurement to test concrete changes against the weaknesses recorded here. These are measurement actions, not claims about unmeasured results.

• For brands with low owned-domain citation rates, publish pages that answer the product and integration questions represented in the measured prompt set. Re-measure owned-domain citation rate against the 286-answer retrieval-evidence denominator.
• For Tradier, test whether category pages and technical explanations increase its 0% appearance rate across the same 288 returned-answer denominator. Re-measure every engine separately because all four recorded 0%.
• For Airwallex, test content that can support recognition outside Perplexity. Re-measure the 1.4% ChatGPT rate, 5.6% Gemini rate, and 2.8% Claude rate against their 72-answer sets.
• For Galileo, test content and third-party references that support recognition outside ChatGPT. Re-measure the 2.8% Perplexity rate, 12.5% Gemini rate, and 2.8% Claude rate.
• For Treasury Prime and Tipalti, test material that reduces the gap between their Perplexity results and Gemini or Claude results. Re-measure each engine's appearance rate using its 72 returned answers.
• For Plaid, Stripe, Adyen, and Marqeta, test whether pages tied to the cluster leaders preserve their appearance positions while raising owned-domain citation rates. Re-measure appearance and citation separately because the denominators differ.

What does this mean for brands in the category?

Interpretation: The measurement separates brand recognition from a brand's role as a cited source. Stripe has the widest measured presence. Plaid has the strongest listed owned-domain citation rate. Brands with one-engine concentration need to assess results engine by engine, because an aggregate rate can hide an absence in another system.

The practical reading is narrow. This study covers 24 stratified prompts, repeated three times per engine. It identifies where these systems named brands and retrieved domains on September 14, 2026. It does not predict outcomes for every B2B fintech infrastructure selection.

How was this B2B fintech infrastructure study measured?

This flagship study measured 24 stratified prompts three times on each of four systems: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). That produced 288 attempted and returned answers. Appearance rates use 288 returned answers. Citation and source rates use 286 answers with retrieval evidence.

Measurement summary

How this study was measured

• Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked.
• Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each.
• 288 answers returned of 288 attempted, 286 with retrieval evidence.
• Product lines rolled up to parent brands using a registry fixed before scoring.
• Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence.

Perplexity, ChatGPT, and Claude had 100% grounding rates in their 72-answer sets. Gemini had a 97.2% grounding rate, with 70 grounded answers out of 72. The results describe this prompt set and date. Repeated generations expose variation, but the data does not establish consumer demand, product quality, or category-wide market position.

Charts and data tables

These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard.

Methodology appendix

This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result.

What was measured

• Category: B2B Fintech Infrastructure
• Measurement date: 2026-09-14
• Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines
• Answers attempted: 288
• Answers returned (appearance denominator): 288
• Answers with retrieval evidence (citation denominator): 286 (99.3% of attempted)
• Answers that failed outright: 0
• Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27

Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable.

How the prompts were sampled

Fixed sampling frame: 3 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: B2B Fintech Infrastructure; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure.

Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility.

How each metric is defined

• Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator.
• Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it.
• 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other.
• Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions.
• Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study.
• Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%.
• 0% — the metric could be evaluated and the event did not occur.
• Average position — mean position of the brand's first mention in the answer's ordered list of named brands.
• Cross-engine consistency — the share of engines that named the brand at least once.
• Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent.

Evidence rules

• An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched.
• Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers.
• Publisher identity could not be established for 1 retrieval receipt(s); those are disclosed here rather than attributed or dropped.
• Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL.

Entity handling

Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone.

No roll-ups were applied in this study.

40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here.

Limits of these findings

• This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-14. Engine behaviour changes without notice.
• The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category.
• Where intervals overlap, differences between brands are not statistically distinguishable at this sample size.
• A brand not listed here was not measured as absent — it simply was not named in these answers.
• Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate.
• 1 of 26 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero.