Aura Financial Agent

Bhavya Sree — Mon, 18 May 2026 17:45:44 +0000

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AI Engineering · Financial Agents · LLM Architecture

How I Cut AI API Costs by 80% Without Sacrificing Response Quality

Building Aura — a multi-LLM financial agent that routes every query to the right model in real time, remembers everything across sessions, and never sends sensitive data to the cloud.

✦ May 2026 ✦ 12 min read ✦ Live Demo ↗

Introduction

Every AI application I've worked on runs into the same wall eventually: your cloud LLM bill. You start with GPT-4o because the quality is great, you ship fast, and then suddenly you're paying $300/month for an app where 60% of the queries are "what's the difference between SIP and lumpsum." That's not sustainable.

The obvious solution — swap to a cheaper model — breaks the use cases that actually matter. Financial advice is not a uniform task. Routing "Hi, how are you?" and "Draft a SEBI-compliant risk stress analysis for an equity options portfolio under high interest rate environments" to the same model is either wasteful or dangerous depending on which model you pick.

I built Aura Financial Agent to solve this properly. It's a financial intelligence platform powered by two core engines:

Cascadeflow — a real-time multi-LLM routing matrix that scores each prompt for complexity and sends it to the cheapest model capable of handling it.
Hindsight — a persistent memory layer that extracts user facts, financial goals, and risk profiles across sessions, so the agent actually knows who it's talking to.

Combined, they cut API costs by 75–80% compared to a single-model setup — without any perceptible drop in quality for users.

Problem Statement

There are three structural problems that break almost every production AI financial tool I've seen:

1 — The Cost-Cognition Paradox

Financial queries span an enormous complexity range. A basic compound interest calculation needs nothing more than a fast 8B model. A DCF valuation with SEBI compliance constraints requires frontier-level reasoning. Using one model for both is either expensive or unreliable. There's no middle ground with a static setup.

2 — Context Amnesia

Standard stateless LLMs forget the user's risk profile, investment goals, and personal financial context the moment a session ends. Users end up pasting the same background every single time, which destroys the experience and means the agent never actually learns anything about the person it's advising.

3 — Privacy Leakage

Financial planning involves sensitive data — portfolio credentials, tax IDs, bank account details. Routing any of this to public cloud APIs is a compliance and security risk. There's no good reason sensitive identifiers should leave a user's machine.

Proposed Solution

Core Insight: Not all prompts are equal. If you can score a query's complexity before sending it, you can route it to the cheapest model that can handle it — and only escalate when you need to.

Aura addresses each problem directly:

Dynamic query profiling (Cascadeflow) analyzes every prompt against a multidimensional complexity heuristic before it leaves the browser. The score determines which tier the request goes to — no manual configuration needed.

Semantic context hydration (Hindsight) extracts structured facts from the conversation — names, goals, risk preferences, financial context — and persists them to MongoDB. Every subsequent prompt is hydrated with this memory block, giving the model a growing picture of the user.

Privacy-tier routing detects sensitive identifiers (passwords, account numbers, API keys) and bypasses all cloud providers entirely, routing to a locally-running Ollama instance. That data never touches an external API.

Project Objectives

Achieve real-time, zero-latency model routing based on dynamic prompt evaluation — no pre-classification step
Implement a persistent memory cycle using Hindsight that survives page refreshes and re-logins
Guarantee local-first processing for any prompt containing sensitive credentials
Build a resilient fallback chain so the agent never goes dark — even if multiple providers are down simultaneously
Deliver a real-time intelligence HUD that makes the routing decisions visible to the user

Tech Stack

Frontend

React 19 + Vite 8

Styling

Vanilla CSS + Glassmorphism

Backend

Node.js + Express 5

Auth

Passport.js + JWT

Database

MongoDB Atlas

Memory

Hindsight Engine

Routing

Cascadeflow

AI Cloud

Groq / OpenAI / Gemini / Claude

AI Local

Ollama (Llama 3)

System Architecture

Aura is structured as a client-orchestrated, server-synchronized platform. The key architectural decision was to run the Cascadeflow scoring logic directly in the browser rather than on the server. This keeps routing instantaneous — the user sees model selection update in real time as they type, before they even send the message.

The overall data flow looks like this:

User enters a prompt → Cascadeflow scores it client-side
Hindsight's recall() hydrates the prompt with the user's persistent memory
The compiled system prompt + user message goes to the selected AI provider
On success, retain() extracts new facts and updates memory state
Session state (facts, spend, audit log, conversations) syncs to MongoDB via POST /api/user/sync
On reload, GET /api/user/me rehydrates the entire local state instantly

Why client-side routing? Server-round-trips add 80–200ms before the AI call even starts. For simple queries routed to Groq (which itself responds in ~200ms), that's a 50–100% latency increase for no benefit. Scoring stays in the browser.

Workflow & Methodology

Every single user interaction follows this lifecycle:

[Boost]

Bhavya Sree — Mon, 18 May 2026 17:19:15 +0000

DEV Community: Bhavya Sree