You win the deals you are in. A machine now decides which deals you are in, and it has never heard of you. This is the growth system that changes that, built on your real product, customers, and founder knowledge.
Your product is good and the customers are real. The problem is nobody can find you. Buyers now ask ChatGPT for the best AI support platform and pick from its answer, and you are not in it. Founder-led sales cannot win a shortlist it never sees.
Two moves. Say what you actually are, because the homepage sells a label anyone could use while your real edge, European, accurate, human-controlled, data that never leaves the EU, sits buried. Then get into the answer: the roundups, G2, and Reddit threads that ChatGPT reads to build its list. Barely anyone is fighting for the AI answers, and the two European rivals who could have owned this spot just got bought. The lane is open.
First 30 days: talk to your customers, lock that one sentence, ship the first pages, and measure real pipeline instead of traffic.
You have product, customers, funding, and sales momentum. What you do not have is a repeatable growth engine. That gap has one main cause, and the cause points straight at the fix.
In 2026, buyers do not start on Google. They ask ChatGPT to name the best AI customer service platform. 51% of B2B buyers now start their research in an AI chatbot, ahead of search, and most build their shortlist before they ever talk to a vendor (G2, 2025). A third buy from a vendor they had never heard of before that search.
Founder-led selling closes the deals that reach the table. It has no say over which deals get there. That shortlist is built by ChatGPT, Perplexity, G2, and a few Reddit threads, and none of them have heard of Typewise. So David in a room, your strongest asset, only fires after a step you are quietly losing.
You win the deals you are in. A machine decides which deals you are in. The growth job is two moves: make the position sharp enough that a person and a model can both repeat it, then get Typewise into every place the shortlist is built.
It is also the most fixable gap you have. The category is two years old and the answer is not locked. Sierra is spending toward a $15.8B valuation and Decagon $4.5B, both chasing US consumer brands with a pure-automation pitch (TechCrunch). In the rush, they left one answer open, and it is the one Typewise can win.
Your own live copy, read for one thing: how legible you are to a stranger and to a model. That is what growth runs on. The homepage hero today, word for word:
"AI Agent Platform for Customer Service"
subhead "Launch your first AI agents in 15 minutes, with humans in control as you scale."
"AI Agent Platform for Customer Service" is a line the whole category shares. Forethought led with almost exactly the same words right up until Zendesk bought it (TechCrunch). Swap the logo and it reads as Ada, or Decagon, or Intercom Fin. A person skims past it, and a model has no reason to surface you over the better-known name beside you.
"Launch in 15 minutes" and "Start free trial" speak to a solo admin trying a tool on a Tuesday. But your customers are TUI Cruises, DPD, IVECO, Mainova, Kelag, and Planzer: European enterprises with procurement, security review, and legal sign-off (typewise.app). The copy courts a small self-serve buyer while the logos prove you close large regulated ones. It is aimed at the wrong person.
The hero promises autonomous, end-to-end resolution. Dig, and the proof underneath is about something else. Testimonials and G2 reviews mostly praise agent-assist autocomplete and Snippets. Your own SDK page still sells "Smart Autocomplete," "Magic Reply," and "3-4x better than Apple/Google AI" (typewise.app/ai-sdk). That older product is real. But the gap between the autonomous promise and the assist-tool proof reads as risk to the cautious buyer you want.
Scroll far enough and it appears: "humans in control," "Hybrid Intelligence," "Enterprise-grade security and governance," EU data residency, ISO 27001, EU AI Act compliance, roots in ETH Zurich research. Every week brings a new story of an AI agent issuing a rogue refund or inventing a policy. "The autonomous agent enterprises do not have to be afraid of" is the most valuable line you own, and it is sitting three scrolls down while a generic label takes the headline.
You do not have a product problem, and at 60+ enterprise logos you do not have a demand problem. The market cannot say what you do in one sentence, so it cannot repeat you, to a colleague in a Slack channel or to a model building a shortlist. Fix the sentence first. Everything else compounds off it.
A clear market narrative starts with where the competition stands. Plot the field on the two axes a European enterprise buyer cares about, and the whole market falls into one corner.
Everyone crowds the same message: automate the team away, deflect the tickets. Salesforce's Marc Benioff made it a slogan, calling AI agents "digital labor" and telling a podcast he needs "less heads" (Fortune). It is a loud, expensive, American, replace-the-humans fight, and it leaves the top-right corner empty: trustworthy, governed, European autonomy.
That corner is not empty by accident. The US leaders cannot stand in it: Sierra and Decagon are US-headquartered, so their data answers to US law under the CLOUD Act. And the two European independents who could have owned it just sold. NICE bought Cognigy for about $955M, Zendesk bought Ultimate, and Gartner has already marked Cognigy down from Leader to Visionary for losing agility after the deal (Forbes).
The independents are being rolled up into US incumbents. The last credible European-native, accuracy-first platform standing on its own is yours.
One sentence a support leader, a legal reviewer, and a language model can all repeat, and a hero that leads with it instead of hiding it:
Short, concrete, and it leads with what your buyer is afraid of getting wrong. And it is not invented. It is your founder's own logic: on why the consumer keyboard failed and B2B worked, David Eberle told TechCrunch that for customer-facing communication "quality matters a lot, because it can impact a brand's reputation" (TechCrunch, 2022). The company was built on accuracy over speed. The homepage forgot it.
Sierra and Decagon sit under US jurisdiction, and the European independents just sold to US incumbents. Switzerland is a stronger trust signal than "EU region," and it is one the giants cannot buy.
Co-developed with researchers at the ETH Zurich AI Center. On-device models, ISO 27001, GDPR, EU AI Act, and data hosted exclusively in the EU. Verifiable, not a claim you have to manufacture.
TUI, DPD, IVECO, Mainova, Kelag, Planzer. European enterprise in regulated and industrial sectors. You are not entering a market. You are naming the one you already lead.
The job is not to invent a new Typewise. Take the true one that is already buried on your homepage, the governed, European, accuracy-first platform, and put it up top. Then point every channel below at that one sentence.
For your buyer, "can we trust it, and is our data safe" is the deal. It is also the part of the market you own by default, and where European enterprise deals are won and lost.
A complex B2B purchase runs through six to ten decision-makers, and 77% of buyers call it "very complex or difficult" (Gartner). Security and privacy review is one of the biggest sources of that late-stage friction: 65% of organizations say buyers now demand more proof of security and compliance than before (Vanta, 2024). For a European buyer, one question decides it: where does our data live, and who can be compelled to open it. A Swiss vendor with EU data residency answers in one word. A US vendor answers with a paragraph of caveats and the CLOUD Act underneath.
Getting this right is itself the pitch. A customer service agent is not "high-risk" under the EU AI Act. It is limited-risk: one binding duty. From 2 August 2026 you must clearly tell users they are dealing with AI, with penalties up to 3% of global turnover (Article 50). Human oversight and audit logging are high-risk obligations the Act does not put on a support bot; those come from GDPR and good practice.
So the honest pitch is not "the law makes you do this." It is sharper: Typewise is ready for the transparency duty on day one, and already runs past the legal floor with human-in-the-loop control and EU hosting, which is what a cautious buyer and their legal team want anyway. Most vendors fumble this and overclaim. Getting it exactly right is how you win the compliance reader, and it is on-brand for a company that sells accuracy.
Every US competitor has to explain their data handling to a nervous European legal team. You get to lead with it. Build the growth engine to reach that legal reviewer on purpose, because they are the one stakeholder who says yes to you faster than to anyone else.
The single biggest move you have. When half of buyers open ChatGPT and ask for "best AI customer service platform for enterprise" or "AI support with EU data residency," the answer is your new top of funnel. Today you are not in it.
AI engines barely quote your homepage. For a "best tools for X" question, third-party sources are 80 to 100% of what the model pulls, so your own site barely moves the answer. Models synthesize from roundup listicles, review sites like G2 and Gartner Peer Insights, Reddit threads, comparison pages, and high-authority editorial. And there is a trap: a model can describe Typewise perfectly and never name it. Recognition is not recommendation. What flips one into the other is co-mention, how often you turn up alongside Sierra, Decagon, and Ada, so the model shelves you with them.
I ran this exact analysis for a language app, Volley: mapped its presence across the "best AI language learning" articles and found it in none, while three rivals appeared in all (that audit is live here). Typewise has the same hole. eesel's current "best customer service AI tools" list names Decagon, Sierra, Ada, Gorgias, Zendesk, and Freshdesk, and never mentions Typewise (eesel). The bars show what a day-one audit puts numbers on: your presence when a buyer asks an AI engine for the best platforms.
The upside hiding in that red bar: you already hold two assets most challengers your size lack, a Wikipedia page and a G2 "High Performer" profile. You are not starting from zero. You are starting on a foundation nobody has built on.
| Move | What it is | Why it earns citations |
|---|---|---|
| Roundup campaign | Get into and create the "best AI customer service platforms 2026" and "best AI support for Europe" articles. Pitch the ones that already rank, publish your own. | Listicles are the format AI engines lean on hardest for "best X" questions. |
| Review engine | A steady push to grow G2 and Gartner Peer Insights reviews in the right categories, weighted to recency, from your happiest EU logos. | Products with 100+ recent G2 reviews get named by AI engines far more than those with a handful. Typewise has about 15 today. |
| Comparison pages | A fair, tabular set: vs Sierra, vs Decagon, and the "European alternative" pages nobody is writing. | They capture the switcher query and the sovereignty query, and they manufacture the co-mention the model needs. |
| Reddit and community | Real, useful participation where CS leaders actually ask, never spam. | LLMs cite Reddit heavily. One genuine, well-placed thread can seed a model's answer. |
| Extractable content | Definitional headings that answer real buyer questions, FAQ schema, sourced stats, a public trust center. | Structure is what lets a model lift Typewise cleanly as the answer. |
| Digital PR | Turn your existing data reports into citations in high-authority articles. | You already run this engine. Point it at earning third-party mentions, not just self-published PDFs. |
A measurable channel now. Track share of answer: how often Typewise appears when you run the top twenty buyer questions across ChatGPT, Perplexity, and Google AI Overviews. Track AI-referral traffic and its conversion, because a visitor who arrives pre-sold on "the European one you can trust" closes differently. The tools exist: Profound, Peec AI, Ahrefs Brand Radar, plus GA4.
Every competitor is pouring money into ads, events, and funding PR. Almost none are engineering their presence in AI answers. It is the cheapest top of funnel in the category and it compounds, because a citation earned this quarter keeps being served next quarter. The first company to own the European enterprise answer takes a spot that is expensive to dislodge, and there is a clock on it.
Campaigns end. A system keeps working. This is the operating model I would build, the hub-and-spoke logic I run in Marketing OS, adapted to enterprise B2B. You already own the hardest part: a data-report engine mining 10M+ interactions. Point it at the wedge and mill every report into the assets your buyer reads.
Each channel does one job the others cannot, and each points at the same European enterprise buyer. The named destinations are where that buyer already is.
| Channel | Its job | Specific starting points |
|---|---|---|
| AI answer engines | Own the shortlist | The eesel, Fin.ai, and Kore.ai roundups; Profound or Peec AI for tracking |
| Reviews and analysts | Pass the trust gate | G2 "AI Customer Support Agents," Gartner Peer Insights, then the Forrester Conversational AI for Customer Service Landscape |
| Founder-led (David) | Carry the human voice | His existing LinkedIn and blog, plus The Ticket, The Support Experience, and Amazing Business Radio podcasts |
| Community | Win dark social | Support Driven (13k CS leaders) and Pavilion for senior-buyer referral |
| European events | Own home turf | CCW Berlin, the UK Institute of Customer Service, CCW Europe Amsterdam |
| Newsletters | Earn credible reach | The Ticket (Intercom), DCX by Mark Levy, CX Today |
| Partner co-sell | Borrow reach | Your existing Mitel CX and PartnerStack channels |
A system needs a heartbeat, so the team always knows what ships when. Founder time is bounded on purpose: David is the strategic voice, not the production line.
| Cadence | What ships | Founder input |
|---|---|---|
| Weekly | 2-3 comparison or answer-engine pages, 3-4 David posts from briefs, review outreach, community participation, a share-of-answer check. | ~45 min of takes |
| Monthly | One hub report and its full spoke set, one podcast or newsletter placement, a review that re-weights next month's mix. | 1 recording, 1 approval pass |
| Quarterly | Analyst submission progress, one European event, a positioning and proof refresh against the live competitive set. | Strategy session |
Your proof is thin for what you now claim, and it points at the old product. The fix is an assembly line that turns real outcomes into the assets a buying committee reads before it ever contacts sales.
| Asset | What it does | The Typewise angle |
|---|---|---|
| ROI case studies | Let a CFO read the payback in 90 seconds | DPD's 30% and IVECO's 25% efficiency, reframed as cost per resolution, CSAT delta, and time to value, with an honest mix, not a cherry-picked deflection stat Verify |
| Comparison pages | Win the switcher and the sovereignty query | The vs-Sierra, vs-Decagon, and European-alternative set from the section above |
| Use-case pages | Let a buyer see their own workflow | Returns, billing, renewals, mapped by your real verticals: logistics, utilities, travel, industrial |
| Trust center | Clear the enterprise gate self-serve | SOC 2, ISO 27001, GDPR, EU AI Act, DORA, data residency. Your moat, made prominent instead of buried |
| ROI model | Survive Finance's scrutiny | A defensible payback range with transparent per-outcome pricing, not a black-box number Finance rejects |
The centerpiece is the European logo base. TUI, DPD, IVECO, Mainova, Kelag: names Sierra and Decagon cannot put in front of a European legal team as easily as you can. Make them the story, not a grey logo bar. A named, sourced EU case study answers what the committee is really asking: has a company like mine, under rules like mine, trusted this and lived.
AI drafts every asset. A human verifies every number and every claim against the customer's real data before it goes public. For an enterprise buyer, one inflated resolution figure that legal catches does not dent a deal, it ends it, and it ends trust in the next three. Two lines I do not cross: AI never invents a number, a client, or a testimonial, and AI never publishes. In a category that sells trust, that discipline is the product.
A strategy is worth what comes out of it. These are real drafts in the proposed position, written the way I would ship them: short, human, every number flagged for verification.
Comparison pages do three jobs at once: rank, get cited, and force the co-mention the model needs. This is the top of a fair "Typewise vs Sierra" page, one that survives editorial and a buyer's scrutiny because it does not pretend Sierra is bad.
Sierra is a strong platform. If you are a US consumer brand chasing maximum deflection, shortlist it. If you are a European enterprise where a wrong answer is a brand and compliance problem, the trade-offs run the other way. The honest comparison:
| Typewise | Sierra | |
|---|---|---|
| Data & jurisdiction | EU-hosted data, Swiss company | US-headquartered, DPA available |
| Autonomy model | Human-in-the-loop by design | Autonomy-first |
| Built for | Regulated European enterprise | US consumer brands at scale |
| EU AI Act | Native, day one | Adapting |
Short, opinionated, and willing to pick a fight. Nobody reads a press release on LinkedIn. They stop for a take.
"Our AI resolves 92% of tickets."
Ask what counts as a resolution. Watch the room go quiet.
Most of that number is customers giving up and closing the chat. Abandoned, filed as solved.
We stopped reporting deflection two years ago. If the customer did not get a real answer, it does not count. Our numbers look smaller. They are just honest.
Ask your vendor for their definition in writing. The stall is the answer.
Your US AI vendor is one subpoena away from your customers' data.
The CLOUD Act lets US authorities compel US companies to hand over data they hold, anywhere in the world. An "EU region" checkbox does not change who can be forced to open it.
This is why your data team keeps stalling the AI rollout. They are not being difficult. They are being right.
Ours runs in Europe, under European law. That is not a feature. It is why the deal clears legal.
Everyone is racing to fully autonomous support. The serious teams are doing the opposite.
From August, EU law says you have to tell customers when they are talking to AI. That is the floor. The teams that win keep a human in the loop past it, not because a regulation forces them, but because "no humans, full autonomy" is a great demo and a terrible thing to defend when it gets one wrong.
Autonomy is not the finish line. Autonomy you can stand behind is.
Depends on your blocker. If it is data residency and the EU AI Act, most of the big US names will cost you weeks in legal review, because the data story points at US infrastructure.
Worth looking at European-hosted options for that reason alone. We run Typewise (Swiss, EU data residency, ISO 27001) mostly because it cleared our privacy review without a fight. Happy to share what our legal team actually asked for.
Genuine only, posted by a real customer or team member with real context. A template for tone, never a script to spam.
No "in today's fast-paced landscape," no "it is not just X, it is Y," no "seamless" or "leverage", which, notably, appear in your current copy. My Marketing OS runs an anti-AI-tell pass on every draft, and I keep sharpening it as the tells change, the same as the answer-engine and review playbooks. For a company whose product is quality communication, the marketing has to clear the bar the product sells.
One bet I would want to run early. Before pitching it, the test that matters: does it solve a real problem for the buyer? Their problem is that they cannot tell which vendor actually works and which will burn them. So yes, it does.
You already publish data reports. Turn that into a recurring public benchmark that answers the buyer's real question, and that no incumbent will publish because it exposes the gap they hide. The category advertises 80 to 90% resolution; independent tests land the same products at 40 to 70%. Somebody credible should keep score, methodology in the open, Typewise held to the same bar. It should be the vendor whose whole position is honesty.
| Variable | How I would run it |
|---|---|
| The buyer's problem it solves | "Which of these actually works, and which passes EU compliance?" A trustworthy, methodology-first shortlist they cannot get from any vendor's own site. |
| Why it compounds | Scorekeepers get cited. The answer engines you want to win quote benchmark content, so the asset that plants your accuracy flag is the same one that earns the citation. |
| Cost | Low. You run the reporting engine already. This is a reframing and a commitment to a cadence. |
| Kill or scale metric | Citations earned in AI answers and third-party articles, plus inbound from CS leaders who found the report. Two quarters with no lift, retire it. |
| Guardrail | Methodology public, Typewise judged by the same rules, never a hit piece on a named rival. The credibility is the entire value. |
A founding growth hire turns a signal into a bet instead of waiting to be told what to make. The downside here is a few weeks of report work you were half-doing anyway. The upside is becoming the honest voice in a category drowning in inflated numbers, in the exact channels where your buyers now start. That asymmetry is the kind of call I would bring to the weekly cycle.
Qualified pipeline, not vanity traffic. The metric tree runs from attention all the way to a deal, and traffic on its own never counts as a win.
| Metric | What it tells us | Layer |
|---|---|---|
| Share of answer in AI engines | Are we in the shortlist the machine builds? The new top of funnel. | Discoverability |
| Roundup and review presence | Are we where the buyer and the model both look? Count and recency. | Trust |
| Qualified organic pipeline | Not sessions. Buyer-fit inbound that organic and AI search created. | Pipeline |
| Influenced pipeline | Deals where the buyer touched our content before sales did. The dark-funnel proxy. | Contribution |
| "How did you find us" at close | The honest attribution question, asked on won deals. Where trust actually formed. | Outcome |
Enterprise cycles here run six months and up, with security and legal eating the back half. So 90 days proves leading indicators and pipeline creation, not closed revenue. Anyone who promises closed enterprise deals in 90 days is wrong, or about to buy traffic that never converts. What 90 days can prove: discoverability gains, a channel showing real buyer inbound, and a proof stack that makes sales convert better. That is the signal I would hold myself to.
The system on this page is the destination. The path to it is built to show visible output fast, without breaking what already works.
Understand from the inside. Shadow sales calls, read win and loss notes, interview David and Janis, and talk to three customers across your verticals, a TUI, a DPD, an IVECO. Mine the exact words they use for why they chose you.
Run the AI-search visibility audit. The Volley analysis, pointed at Typewise: map where you appear and where you do not across ChatGPT, Perplexity, Google AI Overviews, G2, Reddit, and the roundups. Baseline share of answer.
Lock the position and ship the first assets. One repeatable sentence, founder sign-off, a rewritten hero, the first two or three comparison pages, a review drive, and one David post spine. Instrument share of answer and pipeline.
The engine runs. The monthly hub-to-spokes cadence is live off the first report. The comparison-page set is complete and indexing.
Trust signals climb. Review count rises in the right G2 categories, founder content ships on a schedule, and the first movement in share of answer is measurable.
The long game starts. The Forrester Landscape submission is in, and the trust center is public and self-serve.
Measurable signal. Qualified inbound attributable to organic and AI search, discoverability up and into named roundups, review count several times where it started, and a proof stack the sales team actively uses.
One channel worth scaling. At least one channel showing enough commercial signal to double down on, with the numbers to justify it.
The Playbook is documented. The first Growth Playbook is a living doc, so the engine outlives any single campaign and the next hire inherits a system.
By day 90 the numbers point to the first hire, so I would rather show you the evidence than guess now. My bet: you hit a production wall before a strategy one. This engine burns through content and comparison pages faster than one person can feed it, so the first hire is a producer, not another strategist. If the data says otherwise, we hire to the data.
Do not send a standard CV, you said, send something that shows how you think. So I did not describe a growth strategy. I built the first version of your Growth Playbook and shipped it live. This whole page was researched and produced with Marketing OS, a content operating system I built and would point at Typewise on day one. It is why an AI-native operator can do the work of a team.
Grew a founder's audience from 250 to 100k, the exact founder-led motion this role runs with David.
Ran performance campaigns across Reddit, Meta, and Google at $0.11 CPC. I know the numbers, even where this role is organic-first.
Lifted conference registrations 400%. The events and association lever the plan leans on for the EU market.
Growth systems shipped for real companies, including the exact AI-search roundup analysis this strategy runs on.
I have not run enterprise ABM into Unilever-tier accounts. I will not dress that up. Long, sales-led, procurement-heavy enterprise motions are not on my resume, and if that is the whole job, I am not your person.
But it is not the whole job, and it is the part you already have. David's founder-led selling wins the deals that reach the table. What does not exist yet is the system that fills the top of that funnel, the positioning, the answer-engine presence, the proof, the founder-content engine, the organic pipeline. That is the part I have built before, and the part I would build at a speed a traditional hire cannot match. Hire me for the gap that is open, not the one that is filled.
When a customer picks Typewise over Sierra or Fin, what do they actually say in the room, and is it the same thing your homepage leads with? My read is that they buy you for trust, control, and jurisdiction while the site sells a generic label, and that closing that one gap is the fastest growth lever in the company. If I am right, day one starts here. If I am wrong, I want to know why, because that answer rewrites the plan.
The way I would run it on the job: every external claim carries a source, and every customer number is verified against the customer's own data before it goes public. The core references:
Customer-specific numbers (DPD, IVECO and others) are shown as Typewise publishes them and are marked for re-verification with each customer before any public use.