I'm building an AI financial assistant — here's how it's architected (would love your input!)
A few months ago, I got laid off and did the most janky thing ever: pasted my bank statements into ChatGPT to figure out if we could afford for me to take a break.
It kinda worked… but it also felt sketchy. And slow.
That got me thinking:
What if GPT could actually see your real finances — securely — and answer real questions about your money?
So I started building Ask Linc — a privacy-first AI assistant that connects to your accounts (via Plaid), pulls in live economic + market data, and lets you ask questions like:
- “Are we spending more this month than last?”
- “How long could we cover expenses if one of us lost income?”
- “Are my CDs earning enough compared to bond yields?”
I’ve got a working beta now, and I’m here to sanity-check my architecture and learn from anyone who's been down a similar path.
🧱 Stack Overview
Frontend:
Next.js 15(App Router)TypeScriptTailwind CSS- Deployed via
Vercel
Backend:
Node.js + ExpressPrisma + PostgreSQL- Hosted on
Render
AI + Data:
GPT-4(OpenAI)Plaid APIfor bank/transaction dataFRED APIfor economic indicatorsAlpha Vantagefor live market + yield data
Security:
- JWT auth, bcrypt, HTTPS
- No sensitive data stored
- GDPR-compliant delete + audit support
CI/CD & Dev:
- GitHub Actions
- Hot reloading, ESLint, Prettier
- Automated deployments on Vercel + Render
🔐 Design Priorities
- Token-based, read-only access via Plaid
- All financial data encrypted in transit
- Redis-style caching for rate-limited APIs
- No user data used for training or stored unnecessarily
- Privacy-first analytics (
Plausibleonly)
👇 Where I’d love input
- Prisma + Render: too many moving parts? Worth simplifying?
- Alpha Vantage: anyone using better market/yield data sources?
- Caching/syncing: how do you handle rate limits + stale data in multi-user apps?
- Infra: stay with Express + cron jobs or move to serverless?
- Security: any red flags from a compliance or risk standpoint?
Trying to keep things simple and scalable without overengineering.
Would love to hear how you’d approach this — especially if you’ve built fintech tools, GPT wrappers, or anything API-heavy.
If you’re working on anything similar (AI + real data), drop a link too — I’d love to check it out.
Sounds interesting. This is definitely an example of how traditional SaaS is being transformed, where AI agents serve as the interface users interact with.