Aurelia Ledger

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Aurelia LedgerEnterprise Financial Intelligence Agent Platform

A sprint-by-sprint learning site for a production-style AI agent portfolio project

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Explore Concepts

Senior AI Engineering

RAG, Qdrant retrieval, SEC ingestion, macro data, SQL analytics, deterministic evaluation, and reliable FastAPI interfaces.

AI Solution Architecture

Governance, risk controls, cost planning, deployment roadmap, observability, and business-value framing.

Interview-Ready Narrative

Each sprint explains the goal, design decisions, workflow, validation, and how to discuss the work in an interview.

What This Site Explains ​

Aurelia Ledger simulates an internal financial intelligence platform for research analysts, compliance reviewers, and operations teams. It combines document RAG, live SEC EDGAR ingestion, FRED macro data, structured SEC Company Facts, LangGraph routing, deterministic evaluation, security guardrails, and observability.

This site explains the project as a learning journey. It is not a discussion forum and it is not a replacement for the running dashboard. It is a public-facing knowledge base that explains how the system was built and why each engineering decision exists.

Suggested Learning Path ​

  1. Read the sprint guide from Sprint 1 to Sprint 10 to understand the project evolution.
  2. Use the concept guide to study each technical building block in isolation.
  3. Use the workflow guide to understand request lifecycles and data movement.
  4. Use the reference section for APIs, environment variables, local run commands, and glossary terms.

Current Platform Capabilities ​

AreaCapability
Document intelligencePolicy and SEC filing RAG with citation-aware answers
Macro analysisFRED or sample macro series summaries with cached observations
SQL analyticsSafe structured financial metric queries over PostgreSQL
OrchestrationLangGraph workflow with deterministic routing and multi-agent traces
SecurityPII masking, prompt injection blocking, and audit records
EvaluationDeterministic smoke suites, scoring, markdown and JSON reports
ObservabilityRequest, evaluation, and security audit summaries in a custom dashboard

Portfolio Positioning ​

The project is designed to show both execution depth and architecture judgment:

  • It starts with a narrow RAG MVP rather than a broad unfinished agent system.
  • It adds real retrieval persistence before orchestration.
  • It keeps SQL analytics deterministic and safe.
  • It evaluates behavior with repeatable tests rather than vague demo claims.
  • It surfaces security, cost, monitoring, and deployment concerns as first-class deliverables.

Built as a Senior AI Engineer and AI Solution Architect portfolio project.

Aurelia Ledger