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ON builds AI chat engagement for e-commerce and entertainment brands, measuring value in conversion lifts and incremental revenue rather than satisfaction scores – attracting $81M in total funding.
ENTRY ANGLES
Specialist integrator focused on conversational AI deployment quality · Vertical-specific deployment with proprietary playbook for revenue outcomes · Configuration and conversation flow optimization with measurable revenue attribution
VERTICALS
CAPABILITIES
Deep domain expertise in target vertical, Ability to measure and attribute revenue outcomes from AI deployments, Knowledge of conversation flows and configurations that drive conversion
FOUNDER
“we make customers smile.”
ON's pitch to brands is not about chatbot technology – it is about revenue. That distinction has proven more valuable than anyone giving it might expect.
The company builds AI-powered chat engagement platforms for brands in e-commerce, sports, media, and entertainment, integrating across every channel a customer might use: social networks, messaging apps, SMS, email, Discord, or a brand's own website. The infrastructure is relatively standard. What differentiates ON is its framing: chat engagement is measured not in satisfaction scores or resolution rates, but in conversion lifts and incremental revenue.
The numbers they have published are specific. At Armani Exchange, chat volume grew 33% month-over-month within six months of launch, with attributable revenue up 38% over the same period. Customers who engaged in chat were between three and five times more likely to make a purchase than those who did not. In sports – ON's original vertical under its prior name GameOn – clients saw a tenfold increase in click-through rates on links sent through chat, with 40% more clicks on ticketing pages and revenue up 69% from tickets, merchandise, and related products. The Chicago Sky women's basketball team is a documented case: 75% of ticket-related chat conversations ended with a click to the purchase page, and the platform surfaced purchase intent in 20% of all conversations.
ON has been operating for roughly ten years, exclusively in sports, under the GameOn brand. A year ago it began piloting with luxury e-commerce clients and recently announced expansion into financial services, healthcare, and legal. The rebrand from GameOn to ON reflects that pivot. It closed a $35 million round when the e-commerce push began and has now added another $25 million.
The timing of ON's expansion is not accidental. Gartner estimates that AI-powered customer interactions will account for 10% of all engagements by 2026, up from roughly 1.6% today. Every brand building a customer-facing chatbot between now and then is a potential ON client.
What ON figured out early – and what competitors often miss – is that the business case for conversational AI is clearest when it is tied to revenue, not experience. A chatbot that resolves 90% of queries and makes customers smile is hard to budget for. A chatbot that demonstrably lifts conversion by 30% is easy. The whole product positioning is built around that insight.
The second notable decision is horizontal scope across vertical industries. At first glance, luxury fashion, professional sports, and healthcare have nothing in common. What they share is a desire to convert conversations into commercial outcomes – and the playbook for doing that turns out to be surprisingly portable across sectors. Siena AI, [covered previously](/review/bez-sochuvstvija-ne-poluchitsja), raised $4.7 million on an empathetic chatbot framing – their headline was "we make customers smile." ON's headline is "we grow your revenue." Those two positioning choices attract different buyers and, ultimately, different valuations.
The deeper point: ON is not really selling a technology platform. It is selling implementation competence – the accumulated knowledge of what chat engagement configurations actually drive purchase behavior, built up over a decade of sports deployments and now being applied to adjacent verticals.
The market for conversational AI in customer-facing contexts will grow regardless of what any single company does. The constraint is not demand – it is implementation quality. Most businesses that try to deploy chat AI do it poorly, generate mediocre results, and conclude that the technology is not ready.
That gap is the opportunity. Rather than building another chatbot platform, the higher-leverage position is acting as a specialist integrator: the team that knows which configurations actually drive revenue, which conversation flows convert, and which metrics to watch. The technology exists; the expertise to deploy it well does not.
ON's decade of sports deployments gave them a proprietary playbook. The same logic applies in adjacent verticals: a firm that spent ten years doing this for healthcare or financial services would have an equivalent moat. The entry angle is picking one vertical, going deep, and building a track record of attributable revenue outcomes before expanding. Generic chatbot platforms are a commodity; vertical deployment expertise is not.