VOYGR
Winter 2026Real-world place intelligence for AI apps and agents
About Company
VOYGR helps AI apps and agents understand and act in the real world with comprehensive place data. Unlike static mapping APIs, VOYGR combines accurate place intelligence with fresh web context – news, articles, and events. Real-world interactions rely on maps: 10M+ apps/websites, up to 40% of search queries, and 20% of LLM prompts need local context. Yet a restaurant recommendation can fail because the place is closed, menus aren’t searchable, or delivery goes to the wrong entrance. AI pushes expectations further – answering semantic prompts like “specialty coffee shops in SF with Wi-Fi and YC founders", and taking actions like making reservations or placing orders. We’re experienced builders in maps, search, and ML to solve this. Vlad knows this space inside and out – he worked on the Google Maps APIs GTM, plus firsthand customer experience from building in ridesharing and travel. Yarik has spent over a decade leading ML/search teams at Apple, Google, and Meta. Several customers already run VOYGR to continuously validate places data accuracy at scale, and we outperform on accuracy and coverage. Maps were built for humans browsing pins; agents need continuously updated place ground truth they can act on – from copilots and in-car assistants to delivery robots and AR. As agents become the interface to commerce and services, VOYGR aims to be the default ground-truth layer.
Active Founders
Vlad is the co-founder and CEO of VOYGR. He brings firsthand experience from Google Maps, where he led Product Strategy and GTM, bootstrapping new API products, shaping Maps–Gemini data sharing, and driving key improvements in merchant experience and offline ads. He has 15+ years of Growth and GM experience across mapping and its core markets – ridesharing and travel. He previously worked at McKinsey and holds an MBA from Harvard.
Yarik is the co-founder and CTO of VOYGR. He brings nearly two decades of engineering experience, including over a decade building and leading ML and search teams at Apple, Google, and Meta. He led multiple 0 -> 1 efforts across large-scale search and ranking, ML platforms, and production ML systems - powering products used by hundreds of millions of users.