The Fintech Playbook for Latin America

2026-06-17 · Source: The a16z Show · Field: Finance & Economics — FinTech & Digital Financial Services, Entrepreneurship & Start-ups, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

Addi, a prominent Latin American fintech company founded by Santiago Suárez, has evolved from a buy now, pay later product into a comprehensive financial platform encompassing payments, commerce, logistics, and banking. Serving over 3 million consumers and 50,000 merchant partners across more than 1,000 cities in Colombia, Addi leverages a monorepo architecture and an event sourcing system logging over 10 million daily events, partnered with Databricks for real-time data processing. The company has made significant strides in AI, deploying agents that handle 100% of customer service inquiries with an 80% resolution rate and onboard 2,000-3,000 merchants monthly, improving conversion by over 20%. Addi is also developing its own transformer, "AdiDNA," for release later this year. Key to its success are foundational technology investments, a remote-first, English-speaking culture attracting global talent, and a focus on profitability and a strong written culture.

Key takeaway

For Directors of AI/ML or entrepreneurs building in emerging markets, recognize that foundational technology choices like monorepos and event sourcing are critical for scalable AI adoption. You should prioritize investing in a robust data architecture early, even if it seems unconventional, to enable future AI-driven efficiencies in operations like customer service and legal compliance. This approach can significantly reduce cost-to-serve and accelerate growth, allowing you to outpace competitors.

Key insights

Addi's success in Latin American fintech stems from early foundational tech decisions and aggressive AI integration.

Principles

• Prioritize technology investments as core equity value.
• Challenge conventional wisdom for market alpha.
• Measure product managers by customer NPS.

Method

Build a monorepo with event sourcing architecture (e.g., Kafka, Databricks) for a unified data foundation. Start AI adoption with complex problems like legal compliance to build robust pipelines, then scale to customer service.

In practice

• Adopt a monorepo for code reuse and agent access.
• Implement event sourcing for comprehensive data logging.
• Use AI agents for customer service and merchant onboarding.

Topics

• Fintech
• Latin America
• AI Agents
• Monorepo Architecture
• Event Sourcing
• Financial Inclusion

Best for: Investor, CTO, VP of Engineering/Data, Director of AI/ML, Entrepreneur, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The a16z Show.