MoneyLingual

Home page in English
Speaking with AI in Hindi
Document Upload in Chinese
Dashboard in Spanish

Inspiration

We built MoneyLingual because it’s personal. As immigrants and children of immigrants, we’ve sat at kitchen tables translating credit reports, tax letters, and mortgage terms, watching hardworking people feel shut out simply because the rules weren’t explained in their language. MoneyLingual removes that barrier. You speak in your native language, and the AI answers back clearly and accurately, translating U.S. financial jargon into culturally familiar terms you already understand. It’s not about flashy tech; it’s about dignity, agency, and making confident money decisions without needing an interpreter. Financial literacy should belong to everyone, no matter the language on the page.

What it does

MoneyLingual is a voice-first financial companion that lets you speak in your native language and get clear answers back in the same language. It turns jargon into plain words, adds glossary chips for terms like APR or utilization, and uses comparisons to help you better understand financial concepts (equivalents to the 401(k) abroad). You can upload a credit statement or tax page, and it returns a short summary, key numbers, watch-outs, and next steps you can save to a personal plan. Everything supports a Bilingual view (Your Language | English), read-aloud, and a Simplify option for low literacy. Overall, this is meant to act as your own personal financial translator and educator that can converse with you in your native language.

How we built it

We built MoneyLingual on top of our RealityCheck architecture using React, Tailwind, and Flask. The platform integrates multiple APIs: Google Gemini for financial reasoning and translation, Google Cloud Translation for multilingual accuracy, ElevenLabs for natural voice synthesis, Echo AI for monetization and analytics, XRPL for remittance analysis, and Capital One Nessie for mock financial data. Together, these power real-time multilingual chat, document understanding, and voice interaction to make financial literacy accessible to everyone.

Challenges we ran into

The hardest parts were multilingual voice UX (reliable end-of-speech detection and smooth TTS across scripts), keeping bilingual text aligned so users can compare lines easily, and handling low-quality scans with mixed languages. We also had to make sure accessibility (contrast, focus order, motion) stayed correct in RTL languages.

Accomplishments that we're proud of

We’re proud of building a functional multilingual AI assistant in under a day, as well as designing a clean, continuous, and responsive UI that feels natural to scroll and interact with. Through integrating voice interaction, we were able to enable users to talk to the AI naturally in their language. Most of all, we’re proud of how we created a real impact-driven project centered on inclusivity and accessibility.

What we learned

Through this hackathon, we learned how to use AI translation and NLP models for financial education. We also realized the importance of UI accessibility and cultural design sensitivity in fintech products. We saw how small design touches such as micro-interactions, hover effects, and scroll transitions can completely change user engagement. As a team, we learned to work together through collaboration, version control, and late-night debugging lessons.

What's next for MoneyLingual

MoneyLingual is next looking to expand language support to include regional dialects and voice accents. We hope to also integrate live financial coaching and credit-building recommendations. Through partnering with banks, employers, and community organizations, we will be able to offer MoneyLingual as a real-world financial literacy tool. We are also looking to strengthen security and compliance for deployment in real financial contexts.