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Investment Intelligence

AI VC - Investment Intelligence

Solo-built investment intelligence service that ingests news/trends/macro indicators, runs a theme workflow (LangGraph when available, sequential fallback otherwise), and provides RAG-based Q&A with PII redaction and multi-provider routing/fallback.

AI VC - Investment Intelligence

Tech Stack

PythonFastAPIPostgreSQLAPSchedulerLangGraphVector Search

Year

2025 - Present

Executive Summary

The Challenge

Needed reliable scheduled ingestion with backoff/dup handling, plus orchestration that can route across multiple LLM providers and keep theme state consistent over time.

The Solution

Implemented ETL pipelines with PostgreSQL upserts, a multi-provider router with fallback policies, and a StateGraph orchestrator for theme analysis. Added a RAG pipeline with PII masking + retrieval to support evidence-based theme queries, driven by APScheduler jobs.

Technical Documentation

Methodology & Implementation

Overview

  • Solo-built investment intelligence system focused on theme discovery, tracking, and Q&A.

Purpose & User Flow

Purpose

  • Detect and track investable themes over time and answer questions with evidence over ingested sources.

User Flow (high level)

  • Scheduled ETL (news/trends/macro) → theme normalization/modeling → report outputs → RAG Q&A with evidence (PII-safe).

Key Components

  • ETL pipelines for GDELT, Google Trends, FRED with PostgreSQL upserts
  • Multi LLM provider router + fallback policies
  • LangGraph(StateGraph) orchestrator for theme lifecycle (collect → normalize → model → report)
  • RAG pipeline with PII redaction and vector retrieval
  • APScheduler runner for scheduled ingestion and analysis execution