Respondo CRM

Respondo is an AI-ready CRM platform designed for local businesses to centralize customer communications, structure them into actionable tickets, and generate AI-assisted responses with a human-in-the-loop approval workflow.

Status: MVP foundation complete. Email ingestion, ticketing, RAG-based FAQs, and AI summarization are implemented. Multi-channel support and deeper AI workflows are planned.

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The Problem

Local businesses receive customer queries across fragmented channels (email, websites, messaging apps). These messages are:

• Unstructured and context-poor
• Manually handled with no memory or intelligence
• Difficult to scale without operational cost

Traditional CRMs store data. Respondo prepares data for reasoning.

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What Respondo Does

Respondo acts as a single system of record for customer interactions and an AI orchestration layer on top of them.

At a high level, Respondo:

• Ingests customer messages
• Structures them into tickets
• Enriches them with business and customer context
• Uses RAG + LLMs to assist response generation
• Keeps humans in control of final communication

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Core Features (Implemented)

1. Message Ingestion (Email-first)

• Ingests customer emails via Nylas
• Normalizes messages into internal records
• Designed to support additional channels (API, WhatsApp, chat)

2. Ticketing System

• Each incoming message becomes a ticket
• Tickets represent discrete customer issues
• Lifecycle handled via secure server actions

3. RAG-Based Knowledge Retrieval (FAQs)

• Implemented Retrieval-Augmented Generation (RAG) for FAQs
• Uses vector embeddings to fetch relevant knowledge
• Currently scoped to FAQs, architected to extend to full conversation history

4. AI Summarization & Draft Responses

• Uses Google Gemini API for:

  • Message summarization
  • Auto-generated draft replies

• Responses are generated as assistive suggestions, not auto-sent messages

5. Business & Customer Profiles

• Business profiles define operational context for AI

• Planned deep customer profiles to capture:

  • Past interactions
  • Preferences
  • Behavioral signals

• Designed to improve response quality over time

6. Background Processing

• Uses QStash for background jobs

• Handles asynchronous workflows such as:

  • Message processing
  • Embedding generation
  • AI calls

• Keeps request-response paths fast and reliable

7. Secure Server-Side Architecture

• Built with Next.js Server Actions
• All mutations are server-controlled
• No sensitive logic exposed to the client

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AI & Data Pipeline (Current)

Customer Email
      ↓
Nylas Ingestion
      ↓
Server Action
      ↓
Ticket Creation
      ↓
Embedding (Upstash Vector)
      ↓
RAG (FAQs)
      ↓
Gemini Summarization / Draft Reply
      ↓
Operator Review (UI)

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Tech Stack

• Framework: Next.js (App Router)
• Language: TypeScript
• UI & Theming: Material UI (MUI)
• Backend Pattern: Server Actions
• Database: MongoDB
• Email Ingestion: Nylas
• LLM Provider: Google Gemini API
• Vector Store: Upstash Vector
• Background Jobs: QStash
• Auth Strategy: JWT-based (prepared)
• Styling: Tailwind (utility-level)
• Deployment Target: Vercel-compatible

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Project Structure (Key Areas)

src/
├── actions/
│   ├── ingestion.ts      # Message ingestion & normalization
│   ├── ticketActions.ts  # Ticket lifecycle logic
│   ├── profileActions.ts # Business & customer profiles
│   └── user.ts           # User operations
├── app/
│   ├── layout.tsx        # Global layout & theme
│   └── page.tsx          # Entry point
├── proxy.ts              # External integrations & workers

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Channels: Current & Planned

Implemented

• Email (via Nylas)

Planned

• API-based ingestion (direct business integration)
• WhatsApp
• Web chat / widgets

The ingestion layer is intentionally channel-agnostic.

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Design Principles

• AI assists humans, not replaces them
• Human-in-the-loop by default
• Pipeline-driven, not UI-driven
• Async-first architecture
• Extensible without rewrites

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Current Limitations (Intentional)

• RAG currently scoped to FAQs only
• No real-time chat UI
• No role-based UI separation yet
• AI suggestions require manual approval

These are conscious MVP trade-offs to prioritize correctness and architecture.

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Why This Project Matters

Respondo demonstrates:

• Real-world AI integration (RAG + LLMs)
• Async background processing at scale
• Clean SaaS architecture decisions
• Practical AI safety via human approval loops

This project is suitable for:

• Strong internship applications
• Full-stack / backend interviews
• AI-enabled SaaS discussions
• Startup engineering evaluation

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Roadmap (High Level)

• Extend RAG to full conversation history
• Introduce detailed customer intelligence profiles
• Add operator dashboard with role-based access
• Enable API and WhatsApp ingestion
• Ship analytics and audit logs