Zentri: AI-Powered Financial OS

An open-source, privacy-first financial OS that aggregates assets across Thai stocks, US equities, crypto, mutual funds, and gold — then uses LLMs to deliver institutional-grade analysis running locally via Docker.

Solo DeveloperOngoingIn Progress
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View on GitHub
5
Asset Classes
4
LLM Providers
< 3s
Analysis Latency
8
Docker Services

View Documentation →

The Problem

Managing a portfolio across Thai stocks, US equities, crypto, mutual funds, and gold requires jumping between five different apps — none of which talk to each other, and none of which give you a plain-language answer to: what should I do right now?

Zentri is a self-hosted financial OS that aggregates everything in one place and answers that question using LLMs. It runs entirely on your machine via Docker — your data never leaves your system.

Features

  • Multi-asset tracking — Thai stocks (SET), US equities, crypto, mutual funds, gold
  • AI analysis — per-asset buy/sell/hold verdicts with reasoning via Claude, GPT-4, Gemini, or local Ollama
  • Two-tier LLM routing — fast local model for quick scans, cloud model for deep analysis
  • Document RAG — upload fund fact sheets and annual reports; LLM cites them in analysis
  • Conversational chat — ask questions about your portfolio in natural language
  • Net worth timeline — daily snapshots of total wealth across all asset classes
  • Watchlist with AI thesis — track assets you don't own yet with AI-generated entry theses
  • IPO calendar — upcoming IPOs with AI analysis
  • Dividend calendar — track upcoming and received dividends
  • CSV import — auto-maps broker CSV formats using LLM column detection

System Architecture

Key Technical Decisions

DecisionChosenRejectedWhy
Job queueRedis + ARQCelerySimpler, no separate broker
Vector storeChromaDBPineconeLocal-first, zero cost
LLM routingTwo-tierSingle modelCost vs quality trade-off
Time-seriesTimescaleDBInfluxDBStays in PostgreSQL ecosystem
AuthJWT + bcryptOAuthSelf-hosted, single-user

Screenshots

Overview dashboard
Overview Dashboard
Portfolio breakdown
Portfolio Breakdown
AI chat interface
AI Chat
Net worth timeline
Net Worth Timeline
Watchlist with AI thesis
Watchlist
Background job pipeline
Background Pipeline
Research and document analysis
Document Research
Document ingestion
Document Ingestion

What I'd Do Differently

Start with the data pipeline before the UI. I spent two weeks building a beautiful dashboard before realising the data ingestion was unreliable — polish means nothing without trustworthy data underneath.

I'd also add end-to-end integration tests from day one. The LLM pipeline has several moving parts (queue → worker → LLM → DB), and unit tests don't catch the integration failures.