We built Faindo because buyers moved to AI before revenue teams noticed.
When a buyer asks an AI model who to trust, that model is acting as a gatekeeping sales engineer. If your brand is not recommended — or is recommended with negative context — a competitor wins the deal before your sales team even gets the call.
Faindo monitors what AI models say when buyers ask purchase-intent questions, and surfaces the moments where competitors are stealing pipeline.
The buying conversation moved. The tools didn't.
B2B buyers no longer start their shortlist with a vendor rep or a Google search. They ask AI models — "which CRM handles enterprise integrations best?", "who should I use for contract intelligence?" — and the model's answer shapes who gets a meeting request and who gets ignored.
Revenue and marketing teams were flying blind in these conversations. They could track website clicks, ad impressions, and keyword rankings — but they had no signal from the decision-stage buying queries where AI models are now the first filter.
Faindo was built to close that gap. We run buyer-aligned prompts across the AI models your buyers actually use, surface where competitors are mentioned and with what sentiment, and track which cited sources the models lean on when forming those recommendations. The result is a pipeline-focused view of AI model influence — not a vanity score, but a revenue risk signal.
We are a small, distributed team working with revenue-focused teams across Europe and the United States.
The people behind Faindo
Nerijus Rimdžius
Co-Founder & CEO
Decade+ in B2B SaaS as CEO and product lead, having co-founded and scaled several companies. Faindo grew out of watching deals quietly disappear into AI answers — buyers now decide before sales ever picks up the phone, and most companies have no visibility into that conversation.
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Tomas Petkevičius
Co-Founder & CTO
13+ years building production software at Bentley Systems, Trimble, Visma, and Adroiti — distributed systems, data pipelines, and applied AI. At Faindo, he leads the engineering behind multi-model scouting, sentiment-aware analytics, and the infrastructure that turns AI conversations into pipeline signal.
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