Luna for ET / Documentation

How Luna Was Built for the ET Concierge.

This page explains, in a user-friendly way, how Aryan and Ajay built ET Compass: the product flow, the RAG engine, the memory layer, and the planning logic that makes Luna feel like a real ET guide instead of a plain chatbot.

Hybrid RetrievalMongoDB Session MemoryResponse PlannerUnified Decision ObjectProduct ScoringAnswer/UI SyncFormat-Aware RenderingVoice-AI Loop
ET Compass technical documentation header

Build Focus

ET-first concierge guidance, hybrid retrieval, persistent session memory, structured response planning, voice-AI, and responsive product UX in one platform.

Core Team

Who Built What

The platform was built as a collaboration between product, frontend, backend orchestration, and RAG engineering. The roles below keep the story clean and technically honest.

Contributor

Aryan

@Aryan-coder06

Platform, responsive frontend, connected backend flow, and RAG amplification

Built the complete ET Compass platform, reimplemented the responsive UI infrastructure, connected the backend graph end-to-end, and strengthened the RAG presentation layer so answers, widgets, and navigation work together.

Complete platform architecture and responsive frontend
Backend node wiring, API integration, and production-ready flow
Internal RAG upgrades around response planning, answer shaping, and UI sync

Contributor

Ajay

@ajaykathar30

RAG foundation, MongoDB session memory, and hybrid ET retrieval

Built the original RAG path from scratch, set up MongoDB-backed session memory, created the dedicated retrieval pipeline, and established the hybrid-search foundation that keeps Luna grounded in ET knowledge.

RAG from scratch with clear ET retrieval pathways
MongoDB session persistence and conversation memory
Hybrid search and ET-grounded knowledge flow

Architecture

The ET Concierge RAG Flow

01

Intent Capture

Luna reads the user's natural question and identifies whether the need is discovery, markets, learning, events, benefits, roadmap, or comparison.

02

Profile Memory

The system uses session memory to retain profile signals like user type, goal, experience level, and ET lane without forcing the user to repeat everything.

03

Hybrid Retrieval

ET product registry, curated ET sources, vector retrieval, and keyword matching are combined so the answer stays grounded and not purely generative.

04

Product Scoring

Each question is scored against ET lanes like ET Prime, ET Markets, ET Portfolio, ET Masterclass, ET Events, and ET Benefits to find the best fit.

05

Response Planner

A unified decision object chooses answer depth, structure, next action, and whether the UI should help with bullets, tables, or a contextual module.

06

Answer and UI Sync

The final response and the frontend use the same decision state, so Luna's text, widgets, and concierge rail stay aligned.

07

Voice AI Layer

Sarvam speech-to-text and text-to-speech sit on top of the same ET concierge answer path, so the voice mode uses the existing grounded RAG answer instead of bypassing it.

Why This RAG Is Stronger

Smarter Than a Simple Chat Wrapper

Luna is not only retrieving chunks and sending them to a model. The system combines hybrid retrieval, session memory, product scoring, and a response planner so the answer feels intentional.

That means the backend decides not only the text, but also the best ET lane, the next step, and whether the interface should render a cleaner table, bullet structure, or contextual module. This is the core of answer and UI sync.

The result is a higher-quality ET concierge that stays focused on real Economic Times discovery instead of turning into a generic assistant with random finance knowledge.

Voice AI

Same RAG, Spoken Interface

The voice layer was added without replacing the core ET concierge logic. Speech is only the interface. The actual ET answer still comes from the same retrieval, planning, and product-mapping backend.

01 / STT

Sarvam Speech To Text

Voice audio is transcribed first so the user still enters the same ET concierge flow as a text user.

02 / RAG

Grounded ET Answer

The transcribed text is passed into the current Stage 2 ET concierge graph, which keeps the answer grounded in ET products, source citations, and session memory.

03 / TTS

Sarvam Text To Speech

The final grounded answer is cleaned for voice playback and spoken back to the user, while the same turn is still saved in the thread history and ET journey memory.

Summary

7 Core Things ET Compass Provides

These are the strongest user-facing capabilities the website now delivers.

1

Natural ET Concierge Chat

Luna answers open ET questions in a more human way instead of behaving like a rigid form or static FAQ.

2

Profile-Aware Guidance

The system remembers the user's path and keeps narrowing to the right ET lane over time.

3

Hybrid ET Knowledge Retrieval

ET registry data, curated ET sources, keyword cues, and vector retrieval work together to keep responses grounded.

4

Structured Recommendations

Luna does not just answer. It scores ET products, chooses a primary path, and proposes the next best action.

5

Contextual UI Blocks

The frontend can show tables, bullets, next actions, and market context only when those formats actually help.

6

Responsive Full Platform

The project includes the landing page, auth flows, profile dashboard, concierge search UI, and deployable backend/frontend split.

7

Voice-AI Concierge

Users can now speak to Luna through Sarvam voice APIs while still using the same grounded ET answer path, session memory, and concierge logic underneath.