How WIN Flavor built visibility in Google and AI search

A food manufacturer looking for a natural flavors supplier may start with Google, compare several companies and then ask ChatGPT or Perplexity to identify suitable options. For a B2B ingredients supplier selling internationally, appearing in those searches can create an opportunity to enter the buyer's consideration process before a direct conversation begins.

That was the visibility challenge behind our 2026 engagement with WIN Flavor, a Vietnam-based supplier of natural flavors and food ingredients serving international buyers.

We worked on B2B search engine optimization (SEO) and Answer Engine Optimization (AEO) to improve how buyers could discover WIN Flavor through conventional search and AI tools.

The published results include approximately 145.23% growth in B2B leads measured through Google Analytics 4 (GA4), 657 keywords in Google's Top 10 and 2,560 visits referred by AI platforms in 2026.

These numbers demonstrate measurable search visibility and lead activity. They also require careful interpretation, particularly when distinguishing traffic, citations and business outcomes.

International supplier discovery now involves two search environments

For a B2B ingredients supplier, digital discovery is part of a longer commercial process. A potential customer may need to identify suppliers, understand their product categories and decide which companies merit further evaluation.

Google remains one route into that process. Buyers can search using ingredient descriptions, supplier categories and other terms related to their purchasing requirements.

AI tools introduce another route. A buyer may ask ChatGPT or Perplexity to identify suppliers or explain available options rather than review a conventional list of search results.

These environments overlap, but they produce different evidence.

A Google ranking shows that a page appears for a particular search query. An AI citation shows that a system included or referenced a source in an answer. An AI referral visit shows that someone followed a link from an AI platform to the website.

None of these events, by itself, confirms that the visitor is a qualified buyer.

For WIN Flavor, the commercial objective was to become easier for international buyers to find through Google and AI tools. Our work addressed discoverability across both environments.

This sits within the Found stage of Vibery's Reputation Chain. Visibility creates an opportunity for further evaluation, but subsequent stages of trust, selection and long-term customer value need their own evidence.

We combined B2B SEO with Answer Engine Optimization

Our engagement covered two related disciplines: B2B SEO and Answer Engine Optimization.

SEO focuses on making relevant website information accessible, understandable and competitive in search engines such as Google.

Answer Engine Optimization (AEO) focuses on helping AI-driven search and answer systems discover, interpret and reference information about a business when responding to relevant questions.

The distinction matters because a page can perform in conventional search without necessarily appearing in an AI-generated answer. Conversely, a source may be cited by an AI system without producing a website visit.

For an ingredients supplier, the practical requirement is that information about the company and its offering can be found and understood in the environments where buyers conduct research.

Our work for WIN Flavor combined B2B SEO and AEO to support that requirement.

The published project record identifies these two disciplines but does not itemize individual page changes, technical fixes, content releases or implementation dates. We therefore cannot attribute a specific portion of the outcome to any individual optimization activity.

That distinction is important. Describing an optimization approach is different from claiming that one particular technique caused a measured result.

The engagement can be reviewed in the WIN Flavor client case.

The results show growth in leads, Google rankings and AI visibility

Five groups of published measurements describe the outcome of the 2026 engagement.

B2B lead growth: approximately 145.23%

GA4 recorded ~145.23% B2B lead growth against the comparison period.

This is the clearest published measure connecting the engagement with a business-relevant outcome. It indicates that measured B2B lead activity increased substantially relative to the selected reference period.

However, the published record does not provide the absolute lead counts, the exact comparison dates or a breakdown of lead quality.

We can report the percentage change and its source. We cannot infer the number of additional qualified opportunities, their eventual revenue or how many became customers.

Google visibility: 657 keywords in the Top 10

WIN Flavor reached 657 keywords in Google's Top 10.

This measures search ranking coverage: the number of tracked keywords appearing among Google's first ten organic results.

It is evidence of visibility across a set of searches, rather than evidence that all those searches generated traffic or inquiries.

Keyword relevance, search demand and buyer intent influence the commercial value of that coverage. The published figure does not specify those distributions.

AI referrals: 2,560 visits in 2026

WIN Flavor recorded 2,560 AI referral visits in 2026.

These visits represent traffic attributed to AI platforms. They provide a separate view of discovery from conventional organic search.

Within the recorded AI traffic, ChatGPT accounted for 66.3%, while Perplexity accounted for 26.1%.

Those percentages describe the composition of AI-referred traffic. They do not describe the overall market share of either platform or prove which AI service produced more qualified inquiries.

AI citations: presence across ChatGPT and Perplexity

The published results also report 65.5% ChatGPT citation presence and 26.8% Perplexity citation presence.

Citation presence describes how often a business or its content appears in the set of AI responses being measured.

This is different from referral traffic. A buyer can encounter a supplier in an AI response without clicking through to the website.

The figures provide evidence of recorded citation visibility, but the published record does not specify the underlying prompt set, sampling frequency or denominator. They should therefore be read as reported measurements within the project's tracking scope, not universal visibility rates across all AI answers.

The measurements support a visibility outcome, not complete attribution

The combination of Google rankings, AI referral traffic and recorded citation presence supports a clear finding: WIN Flavor established measurable visibility across conventional search and AI discovery channels.

The GA4 lead growth figure adds evidence of improved lead activity during the measured comparison.

However, these results do not establish that every additional lead came from SEO or AEO.

Other influences may include changes in market demand, commercial campaigns, sales activity or the wider business environment. The published record does not isolate these effects.

There are also important reporting boundaries.

The 2,560 AI referral visits are explicitly reported for 2026. The lead growth measure references a comparison period, but its exact dates are not included in the published evidence. The keyword and citation figures likewise do not carry sufficiently detailed collection windows for independent time-series comparisons.

This limits what can be concluded about the sequence of improvements.

We cannot say exactly when each channel began producing value, whether the results were sustained for a defined number of months or how much incremental revenue the engagement generated.

Visibility becomes useful business evidence when rankings, AI citations and visits are connected to defined lead outcomes.

In our view, this is the right way to evaluate AI-search work: preserve the evidence for what happened while separating it from assumptions about what caused it.

The distinction also matters when comparing projects. Citation percentages without a shared query set or measurement protocol should not be treated as directly comparable benchmarks.

Our guide to measuring AI-search visibility explains how to establish a clearer measurement framework.

A V² engagement today would measure when value first arrives and how long it lasts

WIN Flavor was delivered before the V² Framework existed. It has no V² Score, and we do not assign one retrospectively.

Today, we would structure a comparable engagement around Value = Velocity × Longevity: how quickly a defined outcome becomes measurable, and whether the result remains useful over time.

The first stage, Value Map, would establish the baseline before implementation. That would include existing organic search coverage, AI citation visibility, AI referral traffic and a clearly defined lead measure.

The team and client would agree on the exact query groups, markets, data sources, comparison periods and success criteria. Value Map would also set the First Value Date: the agreed date by which the first defined, measurable result should be available.

Value Sprint would deliver the agreed scope against that date.

That date would be set from the actual scope rather than assumed from WIN Flavor's historical results.

Value Transfer would cover the relevant documentation, access and knowledge needed for the client's team to understand and maintain the work.

Finally, Value Growth would introduce a V² Report at 30, 90 and 180 days.

Those reports would distinguish search visibility from commercial outcomes, compare actual performance against the agreed baseline and document other factors affecting the results. They would also make weak or declining results visible rather than reporting only favorable metrics.

This reporting structure would make it easier to determine whether early visibility improvements turned into sustained lead performance.

It would also provide a clearer basis for prioritizing the next investment.

Start with a baseline your marketing and sales teams can verify

WIN Flavor's case shows that a B2B ingredients supplier can achieve measurable presence across Google and AI discovery environments while recording growth in B2B lead activity.

The transferable lesson is to measure those outcomes separately before drawing conclusions about their commercial contribution.

For your own business, begin by defining the supplier searches that matter, recording where your company appears, separating AI citations from referral visits and agreeing on what counts as a B2B lead. Keep the measurement periods consistent so changes can be reviewed over time.

If you want to establish that baseline and define a measurable First Value Date, you can book a Value Map call.