09. Build an AI Email Assistant

Introduction

Build an AI-powered email assistant that classifies incoming emails, generates smart replies, detects spam and phishing, prioritizes messages, and automates routine email tasks.

Email remains the backbone of business communication, but the average professional spends 3+ hours/day managing it. An AI email assistant brings inbox zero within reach.

Problem Statement

Knowledge workers receive 100+ emails daily and spend hours reading, categorizing, and responding. An AI email assistant should:

Classify emails by intent and priority
Generate context-aware reply drafts
Detect spam, phishing, and malicious content
Summarize long email threads
Automate routine responses (out-of-office, scheduling)

Business Use Case

An enterprise with 1000 employees needs an AI email assistant that integrates with Gmail/Outlook, reduces email management time by 60%, and catches phishing attempts before they reach users.

Requirements

Functional Requirements

#	Feature	Description
FR1	Email classification	Categorize by intent (question, request, spam)
FR2	Reply generation	Draft context-aware replies
FR3	Spam detection	ML-based spam + phishing detection
FR4	Thread summarization	Summarize long email chains
FR5	Priority ranking	Flag important emails
FR6	Smart labels	Auto-label based on content
FR7	Automation rules	Auto-respond, archive, forward
FR8	Calendar integration	Detect scheduling requests

Non-Functional Requirements

#	Requirement	Target
NFR1	Processing speed	< 1s per email
NFR2	Spam detection rate	> 99%
NFR3	False positive rate	< 0.5%
NFR4	Reply quality	Human rating > 4.0/5
NFR5	Privacy	Email data never leaves enterprise infra

Architecture

flowchart TD
    subgraph INPUT["Email Sources"]
        GMAIL["Gmail API"]
        OUTLOOK["Outlook API"]
        IMAP["IMAP/SMTP"]
    end
    subgraph PROCESS["Processing Pipeline"]
        CLASSIFY["Email Classifier\nIntent + Priority"]
        SPAM["Spam Detector\nML model"]
        SUMM["Thread Summarizer"]
        REPLY["Reply Generator"]
    end
    subgraph STORE["Storage"]
        PG["PostgreSQL\nEmail records"]
        VDB["pgvector\nEmail embeddings"]
    end
    subgraph UI["User Interface"]
        DASH["Dashboard\nEmail overview"]
        INLINE["Inline\nReply suggestions"]
    end

    INPUT --> CLASSIFY
    INPUT --> SPAM
    CLASSIFY --> SUMM
    CLASSIFY --> REPLY
    CLASSIFY --> PG
    CLASSIFY --> VDB
    SPAM --> PG
    UI --> CLASSIFY

    style INPUT fill:#3b82f6,color:#fff
    style PROCESS fill:#8b5cf6,color:#fff
    style UI fill:#22c55e,color:#fff

Email Classification Pipeline

flowchart LR
    EMAIL["Incoming Email"] --> EXTRACT["Extract\nSender, subject, body, attachments"]
    EXTRACT --> CLASS{"Classification\nModel"}
    CLASS -->|"Question"| Q["Priority: Medium\nSuggest: Reply draft"]
    CLASS -->|"Meeting Request"| M["Priority: High\nSuggest: Calendar check"]
    CLASS -->|"Spam"| S["Priority: Low\nAction: Move to spam"]
    CLASS -->|"Urgent"| U["Priority: Critical\nAction: Flag + notify"]
    CLASS -->|"Newsletter"| N["Priority: Low\nAction: Archive"]
    CLASS -->|"Task/Request"| T["Priority: Medium\nSuggest: Reply + todo"]

    style CLASS fill:#f59e0b,color:#fff
    style S fill:#ef4444,color:#fff
    style U fill:#ef4444,color:#fff

Reply Generation

sequenceDiagram
    participant User as User
    participant AI as AI Assistant
    participant LLM as LLM

    User->>AI: Open email (thread: 15 messages)
    AI->>AI: Build context (thread summary + latest email)
    AI->>LLM: Generate reply options
    LLM-->>AI: 3 draft replies

    AI-->>User: Show suggestions

    Note over User: Option 1: Professional - ✓<br/>Option 2: Brief - ✓<br/>Option 3: Detailed - ✓

    User->>AI: Select option 1
    User->>User: Edit and send

API Design

Method	Endpoint	Purpose
GET	`/api/emails`	List emails
GET	`/api/emails/{id}`	Get email details
POST	`/api/emails/{id}/classify`	Trigger classification
POST	`/api/emails/{id}/summarize`	Summarize thread
POST	`/api/emails/{id}/reply`	Generate reply drafts
POST	`/api/emails/{id}/action`	Archive, mark spam, etc.
GET	`/api/stats`	Email statistics dashboard

Security

Concern	Implementation
Email access	OAuth with minimum required scopes
Data privacy	Process in-region, never train on customer emails
Phishing detection	ML model + domain reputation checks
Compliance	GDPR for EU users, SOC2 for enterprise
Retention	Auto-delete processed email data after 30 days

Evaluation

Metric	Method	Target
Classification accuracy	Human validation sample	> 95%
Spam recall	% of actual spam caught	> 99%
False positive rate	Good emails marked as spam	< 0.1%
Reply acceptance rate	% suggestions used	> 40%
Time saved	Hours saved per user per week	> 2 hours

Interview Questions

Q: Design the email classification system for an assistant processing 1M emails/day.

Pipeline: (1) Ingestion — Gmail/Outlook push notifications → SQS queue, (2) Feature extraction — Extract sender, subject, body, attachments, previous interactions, (3) Classification — Fine-tuned BERT model for intent (question, request, spam, urgent, newsletter), (4) Priority scoring — Heuristic + ML: combine sender importance, urgency keywords, thread length, (5) Action routing — Spam → auto-delete, Newsletters → archive, Urgent → notification, (6) Storage — PostgreSQL + pgvector for search.

Summary

Feature	Implementation
Classification	Fine-tuned BERT for email intent
Spam detection	ML + domain reputation
Reply generation	GPT-4o with thread context
Summarization	LLM thread summary
Priority	Heuristic + ML scoring
Integration	Gmail/Outlook APIs

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