Sprout is a Toronto nonprofit operating as an AI- and data-first organization that works alongside urban Canadian communities and grassroots organizations, providing data tools, local insights, and storytelling support that reflects community resilience building work already happening on the ground. With a core team of three, we built and run the Multilingual Climate Chatbot (MLCC) — a production RAG system serving 200+ languages to communities that don’t speak English or French as their first language across Toronto. We did it on a near-zero budget, under real infrastructure constraints, with no option to optimize later. And we did it without compromising on our values: every model and infrastructure choice was evaluated not just on cost and quality, but on environmental footprint. This talk covers four things: team design as architecture, responsible model selection under constraint, what broke in production, and a retrieval architecture built from failure. Every design decision traces back to a constraint the team couldn’t ignore: budget, latency, language quality, team size, or environmental responsibility. Infrastructure cost: $26/month. Languages served: 200+. Team size: 3.