Western Hemisphere

Konko AI Raises US$6M to Scale Patient-Workflow Automation

Konko AI, a New York City- and San José, Costa Rica-based platform that automates patient coordination and clinical-administration workflows, has raised US$6 million in a funding round led by Mexico City venture capital firm Hi Ventures. LifeX Ventures, SquareOne Capital, GroundUp Ventures, Phoenix Fund, and angel investors affiliated with Harvard, MIT, Google, and Tesla also participated. Co-founder and CEO Jean-Marc Goguikian launched the company with AI specialist and Harvard classmate Michael Haddad.

The company plans to use the financing for product development and expansion across Latin America. Konko began in Costa Rica and now operates in Mexico, Colombia, and Costa Rica, where it says it has managed more than 2 million patient interactions for 90 healthcare institutions across 60 specialties in one year. It says 75% of those interactions are handled end-to-end by AI. Its software combines messaging, triage, scheduling, referrals, follow-up outreach, patient-record consolidation, and analytics.finance.

Market Context

The financing highlights a shift in Latin American health-tech from narrow chatbots and appointment tools toward software designed to orchestrate the entire patient journey. Konko’s stated pitch is operational rather than diagnostic: reduce front-office and care-coordination workloads while helping providers reactivate patients, reduce missed appointments, and unify information spread across multiple systems. The company says customers have achieved up to 30% revenue growth, 50% higher productivity, and a 30% increase in patient net promoter score; those are company-reported results and have not been independently verified in the announcement.

The addressable market is expanding alongside digitization of providers’ operations. One industry forecast estimates Latin America’s digital-health market at US$18.48 billion in 2026, rising to US$79.71 billion by 2034, while a separate forecast projects the region’s AI-in-healthcare segment to grow from US$590 million in 2025 to US$4.43 billion by 2034. Such projections should be treated as directional rather than as audited market data, but they illustrate why workflow automation has become an active investment category.

Key Signal

“Applying AI to healthcare is more complex than most companies realize. Every clinic has unique workflows across dozens of systems, that’s why we’ve spent years working with providers to build AI that understands how healthcare operates.”
— Jean-Marc Goguikian, co-founder and CEO, Konko AI

Regional Relevance

For the United States, Konko represents a cross-border healthcare software model built from New York with operational traction in Latin America. The round also connects U.S.-linked founders, technology talent, and angel networks with a Mexico-based lead investor, underscoring the increasingly regional nature of early-stage health-tech financing. If Konko can translate reported operational outcomes into repeatable contracts, it could become a case study in exporting AI workflow infrastructure across fragmented provider systems rather than selling a single-purpose healthcare tool.

For Costa Rica, the company’s origins are especially relevant. Konko’s first deployment was at a clinic founded by Goguikian and his wife, Dr. Juliana Vallejo, before expanding into Mexico and Colombia. That trajectory positions Costa Rica not just as an adoption market but as an operating base for a venture-backed health-tech company serving the wider region.

For Latin America, the investment points to a practical AI opportunity: administrative and patient-access bottlenecks often constrain care even where clinical talent exists. The regional challenge will be whether platforms can integrate securely with diverse clinic systems, comply with local data and healthcare rules, and demonstrate that automation improves access without reducing quality or patient trust. The Inter-American Development Bank has noted that digital health transformation can improve quality and efficiency when health information is collected and used appropriately for decision-making.

The Other Side

Is “end-to-end AI” appropriate for sensitive healthcare interactions?
Automating 75% of patient interactions could improve speed and reduce administrative burdens, but patient triage, language nuance, escalation rules, clinical accountability, and data protection require clear human oversight. The commercial opportunity depends on workflows that are both useful and trusted.