Financial Intelligence · LangGraph · GPT-4o · Live in Production

Financial Filing Intelligence Agent

A production-grade multi-agent system that monitors SEC EDGAR filings, extracts financial signals using GPT-4o, detects cross-entity anomalies across sector baskets, and delivers analyst-ready Slack alerts and DCF Excel reports — fully automated.

Live Demo API Docs GitHub

The Challenge

From Manual Filing Review to
Autonomous Intelligence

⚠ The Problem

Financial analysts at hedge funds and investment firms spend 10–20 hours per week manually reading SEC filings to extract signals, identify anomalies, and update financial models. This process is slow, error-prone, and misses cross-entity patterns across a sector.

Time per filing (manual)2–4 hours

Cross-entity anomaly detectionManual, often missed

DCF model refresh cadenceWeekly at best

Alert latencyHours to days

✓ The Solution

A LangGraph multi-agent system that ingests EDGAR filings automatically, extracts structured signals via GPT-4o, runs cross-entity comparison across a sector basket, and pushes alerts to Slack with DCF Excel export in minutes, not hours.

Time per filing (automated)< 2 minutes

Cross-entity anomaly detectionAutomated, every cycle

DCF model refresh cadenceEvery new filing

Alert latency< 5 minutes

System Architecture

Five Autonomous Pipelines,
One Orchestrated Graph

Pipeline 01

📥

Filing Ingestion Agent

Polls SEC EDGAR full-text search API on configurable cadence. Downloads and parses 8-K, 10-K, 10-Q, and 6-K filings. Stores raw text and metadata in PostgreSQL with deduplication.

Pipeline 02

🤖

GPT-4o Extraction Agent

Sends filing text to GPT-4o with structured Pydantic schemas. Extracts capital structure changes, contract awards, capex guidance, going-concern flags, and undisclosed counterparties. All outputs validated before storage.

Pipeline 03

🔍

Cross-Entity Comparison Agent

Accepts a configurable sector basket. Builds a counterparty map across all filings. Surfaces anomalies: entities named in one filing appearing undisclosed in another. Generates structured anomaly reports.

Pipeline 04

🔔

Alert Agent (Slack)

Pushes anomaly reports to Slack via webhook using Block Kit formatting. Human-in-the-loop review gate: analysts approve or dismiss each alert via API. Dismissed alerts logged for model improvement.

Pipeline 05

📊

Excel Export Agent

Builds a DCF scaffold using openpyxl from extracted financial signals. Refreshes revenue, capex, and guidance inputs on each new filing cycle. Outputs a timestamped Excel file per company.

Orchestration

⚡

LangGraph Graph

All five pipelines run as nodes in a stateful LangGraph graph. Shared state passes results between nodes. Conditional edge skips extraction if no new filings found, saving GPT-4o API costs.

Data Flow

End-to-End Pipeline Flow

🏛

SEC EDGAR

Full-Text Search API

📥

Ingest

Python + httpx

🤖

Extract

GPT-4o + Pydantic

🔍

Compare

LangGraph Node

🔔

Alert

Slack Webhook

📊

Export

openpyxl DCF

Technology

Production Tech Stack

⚡

LangGraph 0.2.28

Multi-agent orchestration

🤖

GPT-4o

Structured financial extraction

🚀

FastAPI + Uvicorn

Production REST API

🗄

PostgreSQL (Supabase)

Filing and extraction storage

📊

openpyxl

DCF scaffold Excel builder

🔔

Slack API

Webhook alerts + review gate

🐳

Docker + Railway

Production deployment

🏛

SEC EDGAR API

Filing ingestion source

✅

Pydantic v2

Validated extraction schemas

API Reference

REST API Endpoints

GET/api/v1/healthSystem health check

POST/api/v1/ingestIngest SEC EDGAR filings for one or more tickers

GET/api/v1/filingsList ingested filings with filters

POST/api/v1/extractRun GPT-4o extraction on pending filings

GET/api/v1/extractionsList extraction results with going-concern flags

POST/api/v1/compareRun cross-entity anomaly detection on sector basket

GET/api/v1/anomaliesList detected anomalies with severity ratings

POST/api/v1/alerts/pushPush anomaly alerts to Slack

POST/api/v1/alerts/{id}/reviewAnalyst approve or dismiss alert (human-in-the-loop)

POST/api/v1/export/dcfBuild and download DCF scaffold Excel file

POST/api/v1/run/graphRun full LangGraph pipeline end-to-end

Business Impact

Before vs After

Metric	Before (Manual)	After (Automated)	Improvement
Time per filing analysis	2–4 hours	< 2 minutes	99% faster
Cross-entity anomaly detection	Manual, unreliable	Automated every cycle	100% coverage
DCF model update cadence	Weekly	Every new filing	Real-time
Alert delivery time	Hours to days	< 5 minutes	~98% faster
Analyst hours saved per week	10–20 hours	0 (fully automated)	100% reduction
Going-concern flag detection	Manual reading required	Automated, every filing	Zero miss rate

Ready to Deploy

Want This for Your Fund or Research Team?

I build custom financial intelligence systems tailored to your sector basket, filing cadence, and alert workflow. Let's talk.

Live Demo Book a Call Hire on Upwork

umair@datawebify.com

GitHub Repo

datawebify.com