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FinSight AI

An AI-powered financial intelligence platform — parallel agents deliver probabilistic revenue forecasts, buy/sell signals, and peer benchmarks in under 20 seconds.

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PythonFastAPIReactScikit-learnSHAPGroq LLaMAMongoDB
FinSight AI

Overview

FinSight AI is an agentic, multi-model financial intelligence platform that automates analysis which traditionally takes analysts hours. Enter a ticker and a date — get a probabilistic revenue forecast, a buy/sell signal, peer benchmarks, and a full audit trail in under 20 seconds. It combines quantitative finance, multi-agent LLM reasoning, and real-time market data into one fast pipeline.

The Problem

Traditional financial analysis means reading earnings transcripts by hand, building Excel models, monitoring news, and running competitive comparisons — repetitive work that's slow and hard to keep consistent. FinSight AI integrates all of it into a single automated pipeline with explainable outputs.

What I Built

  • Parallel AI agents running concurrently via Python asyncio for sub-20-second results
  • Probabilistic forecasts — Bear / Base / Bull scenarios via quantile regression, not a single point estimate
  • SHAP explainability so every forecast can be traced to its drivers
  • News + NLP + competitor analysis for macro and peer context
  • PDF reports with a full audit trail of how each signal was produced

Architecture

Specialized agents run concurrently and a final ensembler fuses their outputs into one calibrated signal:

  • Transcript NLP agent — extracts and summarizes key financial drivers from earnings calls
  • Financial model agent — quantitative forecasting with quantile regression (Bear/Base/Bull)
  • News macro agent — aggregates, classifies, and scores macro news sentiment
  • Competitor agent — benchmarks the target against industry peers
  • Ensembler (CIO agent) — fuses all outputs into one calibrated investment signal with reasoning

Tech Stack

Frontend: React, Vite, Tailwind CSS

Backend: FastAPI, Python, asyncio

ML: Scikit-learn, quantile regression, SHAP

LLM: Groq (LLaMA 3)

Database: MongoDB Atlas

Use Case

Retail and analyst users who want a fast, explainable second opinion on a stock — forecast, signal, peer context, and the reasoning behind it — without spending hours building models by hand.

Status

Production (v2.0) with CI; actively maintained.