Your finances, understood by AI.
Finora ingests raw bank statements, autonomously categorizes every transaction with Gemini AI, and gives you a live RAG-powered financial advisor - all in one production-quality Next.js application.
Manual finance tracking is broken
- Spreadsheets require hours of manual categorization every month
- Bank apps show raw transactions with no useful context
- Budgets are guesses - no automatic overspend alerts
- No way to ask questions about your own spending patterns
Autonomous financial intelligence
- Upload Document → AI instantly categorizes every transaction
- Live budget progress bars update as you spend
- Real-time alerts at 75% and 90% of each budget limit
- Ask Gemini AI questions about your actual spending data
Built for real financial clarity
Every feature is engineered end-to-end - from database schema to UI component - with production-grade reliability.
RAG AI Advisor
Chat with Gemini using your actual financial data injected into context. Zero hallucinations - every answer is grounded in your real numbers.
Smart Budget Tracking
Set monthly limits per AI category. Real-time progress bars alert you at 75% and 90% thresholds before you overspend.
Recharts Analytics
Area charts, pie charts, and horizontal breakdowns turn raw transaction arrays into visual financial intelligence.
Document Ingestion (CSV/PDF)
Drag-and-drop CSV or PDF bank statements. Gemini autonomously reads unstructured PDFs and maps them perfectly to your database schema.
Better Auth Sessions
HTTP-only session cookies, CSRF protection, and 7-day rolling session windows. Enterprise-grade auth without the complexity.
Prisma + PostgreSQL
User-scoped composite indexes ensure every query is O(log n). Repository pattern keeps data logic clean and testable.
How it works
How Finora processes raw transaction matrices into structured financial intelligence, securely and reliably.
Data Ingestion (CSV/PDF)
Users upload bulk bank statements. Client-side workers instantly process CSVs, while an AI document pipeline powered by Gemini rapidly extracts tabular transaction data from unstructured PDFs.
Autonomous AI Categorization
Our Gemini-powered backend engine processes unorganized transaction records. It cleans up ambiguous merchant strings and assigns accurate spending categories structured as strictly typed JSON.
Real-Time Budget Aggregations
Relational database queries continuously calculate total categorized spending against user-defined limits. Visual progress components update dynamically with custom CSS thresholds.
Retrieval-Augmented Chat (RAG)
When interacting with the AI Advisor, the application injects the user's latest 30-day financial summaries directly into the LLM context window to provide verifiable, mathematical guidance.
Modern stack, production architecture
Strict TypeScript throughout, server-side data fetching by default, and scalable database indexing.
Next.js 16
App Router · Server Actions · Streaming
Prisma + PostgreSQL
Repository pattern · Composite indexes
Better Auth
HTTP-only cookies · 7-day sessions
Google Gemini
2.5 Flash · RAG context injection
Recharts
Area · Pie · Bar · Responsive
Framer Motion
Spring physics · Layout transitions
Zod + React Hook Form
End-to-end type safety
TailwindCSS v4
CSS variables · Dark mode
Retrieval-Augmented Generation, not guessing.
When you ask the AI Advisor a question, Finora doesn't just send it to Gemini. It first fetches your real 30-day financial summary and active budgets from the database, then injects that structured context directly into the LLM prompt.
Trusted by engineers and founders
“Finora completely transformed how we manage our startup's runway. The automated categorization saves us hours every month.”
“The RAG advisor is genuinely impressive. It cited my actual spending numbers when giving budget recommendations.”
“The architecture is production-quality. I use Finora as a reference when onboarding engineers to Next.js App Router.”