Webappski is an Answer Engine Optimization (AEO) studio in Gdynia, Poland that proved its method on its own product before selling it. A brand-new product, TypelessForm — its codebase started on 6 March 2026, on a domain registered in December 2025 — went on to being named by all four major AI answer engines (ChatGPT, Gemini, Claude, Perplexity) on global buyer queries within three months — with no clients, no reviews, and no sales to lean on. The measurement was run with our own free, open-source tool, aeo-platform, and every raw AI response is saved to disk so any reader can reproduce the result.

This is the studio's debut case study. Most AEO advice comes from agencies that have optimized clients' sites but never their own, or from tool vendors who measure but never execute. We did the opposite: we built a product, made it visible inside AI assistants from a standing start, and published the raw arc — including the parts that dipped — so the claim is verifiable rather than marketing. If you are a B2B SaaS company entering the DACH or CEE markets and you want AI assistants to recommend you, this is the loop we run, shown end to end on a product we own.

The product under test is TypelessForm, a voice-to-form widget. We chose it deliberately as the proving ground: a brand-new product with, by definition, zero AI visibility on day one of the work — exactly the cold-start a real client faces. The tool doing the measuring is aeo-platform (npm, version 1.3.0, MIT-licensed, zero dependencies). The methodology — measure, plan, improve, re-measure — is documented in depth in our flagship article on the tool; this piece is the company case study that the tool's loop made possible.

Who Is Webappski, and What Is Answer Engine Optimization?

Webappski is an Answer Engine Optimization studio based in Gdynia, Poland, serving B2B SaaS companies that want to be recommended inside AI assistants — particularly those expanding into the DACH (Germany, Austria, Switzerland) and CEE (Central and Eastern Europe) markets. Answer Engine Optimization is the practice of getting a brand cited and recommended inside AI answer engines such as ChatGPT, Gemini, Claude, and Perplexity, the same way Search Engine Optimization makes a brand surface in Google's results. The buyer's question is no longer typed into a search box and answered with ten blue links; it is asked to an assistant and answered in a single paragraph. AEO is the work of being the brand that paragraph names.

The reason a buyer should care which studio runs that work is the same reason this case study exists: an AEO result you cannot reproduce is indistinguishable from a marketing claim. Webappski's position is that the methodology has to be provable on a product the studio owns end to end, with the raw measurement published, before it is sold to anyone. The rest of this article is that proof.

What Does "From Zero" Actually Mean Here?

"From zero" means a product with no AI footprint, not a doctored before-shot. The timeline is publicly checkable: the typelessform.com domain was registered on 16 December 2025 (the WHOIS record is open), the product's codebase started on 6 March 2026, the first SEO and AEO commits landed on 8 March 2026, and the widget's first public release — 1.0.0-beta.1, dated 17 March 2026 — is visible to anyone in the package's public version history on npm. A product that young has, by definition, zero presence in any AI engine's answers — there is nothing for an assistant to have learned about it yet. That is the honest starting line: not a measured "0" we engineered for a chart, but the structural cold-start of a product that did not exist a quarter earlier.

Our first actual measurement came six weeks into the work, on 23 April 2026, and it already read 33 out of 100 — because by then the foundational AEO work (clean crawlability, schema, answer-capsule paragraphs, an llms.txt file) was in place. We are deliberate about this distinction: we did not measure a literal "0" on day one and we will not claim we did. The arc this article reports is the honest one — a product built from March 2026 onward, measured at 33 after six weeks of work, climbing to being named by every engine by June.

What Did the Measurements Show Over Three Months?

We ran the same measurement six times across the period, always on the identical grid: four engines times three commercial buyer queries, twelve engine-and-query cells in total. The queries were global, with no geographic modifiers — "best voice form filling tools 2026", "top one-shot voice form filling services for e-commerce", and "multilingual voice form filling for international websites". A composite Visibility Index summarises each run on a 0-to-100 scale. The trend across the six runs is the spine of this case study, and we show it exactly as the tool drew it — climb and dip included.

Read the numbers in order: 33 on 23 April, 42 in mid-May, 58 on 25 May, 100 on 10 June, and 83 on the 11 June run. The honest headline is not the single highest point — it is the shape. A brand that did not exist in February is, by June, named by AI assistants on commercial buyer queries with no help from a single customer reference. The 10-to-11 June step down from 100 to 83 is part of that honesty, and it is worth being exact about its cause: it is not the engines wavering. Through 10 June the grid measured three engines — ChatGPT, Gemini, and Claude — across three queries, nine cells, and the product was named in all nine, a clean 100. On 11 June we widened the grid to a fourth engine, Perplexity, which names the product in one of its three queries; the composite is therefore 10 of 12 cells, 83. The step is an honest expansion of the denominator, not a drop in the answers. (Separately, these engines are non-deterministic — their answers do shift between sessions — which is exactly why a measure-improve loop reads the trend across repeated runs rather than freezing on one screenshot.)

Do All Four AI Engines Actually Name the Product?

Yes — all four engines name TypelessForm, and three of the four do so on every query. On the 11 June 2026 run the brand appeared in 10 of the 12 engine-and-query cells. ChatGPT (gpt-5-search-api), Gemini (gemini-3.5-flash), and Claude (claude-sonnet-4-6) each named the product on all three queries, a 100% hit rate apiece. Perplexity named it on one of three queries. That is the precise, un-rounded claim: named by all four, at 100% on three of them, and at one-in-three on Perplexity. The per-engine panel below is the screenshot straight from the tool.

These two panels are screenshots of a live, interactive report. Rather than freeze them as images, we host the whole thing: read the full live AEO report → — every engine answer, every competitor table, every run, exactly as aeo-platform generated it.

The Perplexity column is the part of this result we are least inclined to dress up, because it is the most instructive. Perplexity names TypelessForm directly in one query's answer, which tells us the engine recognises the entity; the one-in-three says the recommendation is not yet consistent across query intents. That single weak column is exactly how a measure-improve loop is supposed to behave — a new gap surfaced rather than buried under an aggregate score. The next plan writes itself: Perplexity-weighted sources (the directories and comparison pages that engine pulls from) become the priority. We would rather publish the 1-of-3 honestly than round it into a headline.

Why Does "No Clients, No Reviews, No Sales" Make This Harder, Not Easier?

Because the usual levers were unavailable. The conventional way a product earns AI mentions is through the trail customers leave: G2 and Capterra reviews, case-study testimonials, press driven by funding or traction, Reddit threads where users compare tools they actually pay for. TypelessForm had none of that. It had no paying customer, not one review, and no sales record for an engine to draw on. Every mention it earned had to come from the product surface and the structured, citable signals around it — not from social proof we did not have.

That constraint is the point of the case study. A B2B SaaS company entering a new market is in exactly this position: real but unproven in the eyes of an AI assistant that has never seen it discussed. Showing that a cold-start product can be made visible on the strength of structure and substance alone — clean crawlability, answer-shaped content, schema, entity consistency, and presence on the aggregator surfaces the engines already read — is far more useful evidence than showing the same lift on an established brand that the engines half-knew already.

How Do We Know These Numbers Are Real?

Because the measurement is reproducible and the raw data is on disk. We did not screenshot a dashboard and ask you to trust the headline. Every run was produced by aeo-platform, an open-source CLI anyone can install with npm install -g aeo-platform; it sends the same queries to the same engines, records each raw answer, and writes the result to your own machine. The two screenshots above are that tool's own output, unretouched, dip and Perplexity gap included. Run the tool on TypelessForm yourself and you will get a comparable result; that reproducibility is what separates a case study from a testimonial.

It is also why we publish the awkward details. We measured 100 on 10 June across three engines, then chose to add a fourth — Perplexity — for the 11 June run, which named the product in only one of three queries and pulled the composite down to 83. Widening the grid and spending our own headline number is a worse marketing move than freezing on "100" with a three-engine denominator — and we did it anyway, with the step visible in the chart, because the alternative is the polished half-truth this industry runs on. The same discipline applies to the "0 citations" footnote and the 1-of-3 Perplexity column. The credibility of an AEO studio is built precisely in the places where the data is less flattering than the pitch.

What This Means for a B2B SaaS Company Entering DACH or CEE

If a brand-new product with no commercial track record can be named by every major AI engine in three months, an established SaaS product with real customers has a shorter path, not a longer one. The work is the same loop run on your domain: measure where you currently appear across ChatGPT, Gemini, Claude, and Perplexity; build a prioritized plan from the specific gaps the measurement surfaces; ship the structural and authority fixes; and re-measure to separate real movement from the engines' week-to-week noise. The DACH and CEE angle matters because the competitor field there is thinner than the English-language marquee — being the brand an assistant names in German, Polish, or Czech is a more winnable position than fighting for the same slot in saturated US English queries.

The methodology underneath this case study — exactly how the measure-to-plan-to-improve loop works, what the 30-mission plan looks like, and why the fix lever differs per engine — is documented in full in our flagship article on aeo-platform. This page is the proof that the loop works on a real product; that page is the manual for how to run it.

Frequently Asked Questions

Who is Webappski and what does the studio do?

Webappski is an Answer Engine Optimization (AEO) studio based in Gdynia, Poland. It helps B2B SaaS companies — especially those expanding into the DACH and CEE markets — get cited and recommended inside AI answer engines such as ChatGPT, Gemini, Claude, and Perplexity. The studio proves its methodology on its own products before selling it, and publishes the raw measurements so the results are reproducible rather than marketing claims.

Does "from zero" mean the product had a measured score of 0?

No. "From zero" means a brand-new product with no AI footprint when the work began: TypelessForm's codebase started on 6 March 2026 (the domain itself was registered in December 2025), and a product at that stage has zero visibility by definition because no engine has learned anything about it yet. The first actual measurement, six weeks into the work on 23 April 2026, already read 33 out of 100, because the foundational AEO work was in place by then. We report that honestly: the start is a true cold-start, and the first measured number is 33, not a literal 0 we engineered.

Do all four AI engines really name the product?

Yes. On the 11 June 2026 run TypelessForm was named in 10 of 12 engine-and-query cells. ChatGPT, Gemini, and Claude each named it on all three queries (100% each); Perplexity named it on one of three. So the precise claim is: named by all four engines, at 100% on three of them, and at one-in-three on Perplexity. The engines mention the brand in their answer text but do not yet cite its own domain as a source — closing that gap is ongoing work, not a result we are claiming.

Why does the chart show a step down from 100 to 83?

Because we widened the measurement, not because the answers dropped. Through 10 June the grid covered three engines — ChatGPT, Gemini, and Claude — across three queries, and the product was named in all nine cells, a score of 100. On 11 June we added a fourth engine, Perplexity, which names the product in one of its three queries, so the composite became 10 of 12 cells, or 83. The step down is an honest expansion of the denominator, and the chart shows it plainly. We show it on purpose because widening the grid and spending a pretty number is more honest than keeping a narrow one. (AI engines are also genuinely non-deterministic between sessions, which is why the loop reads the trend across repeated runs rather than any single screenshot — but that general property is not what caused this particular step.)

How can I verify these numbers myself?

The measurement was run with aeo-platform, a free, open-source, MIT-licensed CLI you can install with npm install -g aeo-platform (version 1.3.0, zero dependencies). It sends the same buyer queries to each engine, records every raw answer, and writes the result to your own disk. Run it against TypelessForm and you will get a comparable result. Reproducibility is the whole point: the screenshots in this article are the tool's unretouched output, dip and Perplexity gap included.

What were the exact queries, and were they geo-targeted?

The three queries were global, with no geographic modifiers: "best voice form filling tools 2026", "top one-shot voice form filling services for e-commerce", and "multilingual voice form filling for international websites". This is not a "worldwide ranking" claim — it is the result on three global, geo-neutral commercial queries, measured across four engines for twelve cells in total.

Want Webappski to Run This Loop for Your Product?

If a cold-start product with no clients, reviews, or sales can be named by every major AI engine in three months, your established SaaS has a shorter path to the same position. Webappski runs the measure-plan-improve-re-measure loop for B2B SaaS companies entering DACH and CEE, with the raw measurement published the same way it is here — Answer Engine Optimization consulting typically runs from $3,500 to $5,000, billed by invoice.

Start with a baseline read: request a free AEO audit and we will show you exactly where your product appears across ChatGPT, Perplexity, Gemini, and Claude today, where it does not, and what the gap is costing you. To engage the full engagement, see Webappski AEO services and contact us to purchase via invoice. The methodology behind it all is documented in our flagship article on aeo-platform.

This article was last updated on 12 June 2026. The figures come from real aeo-platform reports on typelessform.com: a brand-new product whose codebase started on 6 March 2026 (domain registered 16 December 2025), measured at 33 out of 100 on 23 April, climbing to being named in 10 of 12 engine-and-query cells (83%) on the 11 June run. The measurement tool, aeo-platform, is actively maintained; the current version is 1.3.0. AEO is a fast-moving field — we update this article as the tool and the engines evolve. If you notice outdated information, contact us at info@webappski.com.