I Built an AI-Powered WhatsApp Helpdesk with Aurora DSQL, Multi-Model AI, and a Visual Chatbot Builder

Titus Eddy — Mon, 08 Jun 2026 10:44:06 +0000

The Problem

Millions of small and mid-sized businesses — especially across Africa and emerging markets — run their entire customer support on WhatsApp. Yet the tooling available is either enterprise-priced, dumb (no AI), or introduces unbearable latency for distributed users.

I wanted to build something that's:

AI-native from day one (not bolted-on)
Multi-region with sub-100ms database latency globally
Accessible to any business with a WhatsApp number
No-code friendly for building complex chatbot flows

What I Built

ImaradeskAI — a full-stack WhatsApp customer support platform that turns any WhatsApp Business number into an intelligent helpdesk with AI-powered automation, visual chatbot flows, and real-time analytics.

🧠 Multi-Model AI Engine

Three providers working together — Gemini 2.5 Flash, GPT-4o, and Claude Sonnet 4 — switching transparently based on task type and availability.

Every inbound message gets:

Intent classification into 9 categories with confidence scoring
Sentiment analysis (-1 to +1) feeding escalation risk
Language detection + auto-translation — respond in the customer's native tongue
Conversation summarization for automatic ticket generation

The system caches classifications and embeddings, so repeated queries are near-instant.

🤖 Visual No-Code Chatbot Builder (13 Node Types)

A drag-and-drop flow canvas where non-technical users can build complex conversation logic without writing code:

Node Type	What It Does
Trigger	Entry point — keyword, regex, business hours, first message
Send Message	Send text to the customer
Quick Replies	Present numbered options for structured responses
AI Response	Single-turn AI generation using conversation context
Agent (Full AI)	Multi-turn autonomous agent with memory + KB access
Knowledge Base	Vector similarity search + AI-synthesized answer
Condition	Multi-rule branching (contains, regex, equals, not_equals)
Delay	Configurable wait (optimized for serverless at ≤5s)
Connector	Merge multiple branches into one path
Action	Side effects: create ticket, assign agent, add tag, close
API Call	External HTTP calls with SSRF protection (blocks private IPs)
Smart Reply	Template-based replies with shortcut matching
Human Handoff	Escalate to agent + auto-create ticket + assign team

The flow engine supports:

Trigger priority matching (specific keywords before catch-all)
Depth-limited execution (max 20 nodes) to prevent infinite loops
Stateful conversations — activeFlowId + currentNodeId per contact
Full execution logging for debugging and analytics

📚 RAG Knowledge Base with Vector Embeddings

Upload (PDF/DOCX/TXT/CSV) → Extract Text → Embed → Store → Cosine Search → AI Generate

Documents embedded with gemini-embedding-001 or text-embedding-3-small
Vectors stored as JSON arrays directly in Aurora DSQL — no separate vector DB needed
Cosine similarity ranks all KB entries against the user's query
Top-k results passed as context to AI for grounded, hallucination-resistant answers
Files managed in AWS S3 with UUID-based naming for security

🌍 Multi-Region Database with AWS Aurora DSQL

This was the biggest architectural bet. Aurora DSQL gives us:

Two active regions (us-east-1 + us-east-2) with automatic geo-routing
Short-lived IAM tokens via DSQL Signer (auto-refreshed every 10 min)
Connection pooling per region with failover to secondary
Client lat/lon routing — the nearest cluster handles each request

function getNearestRegion(lat: number, lon: number) {
  const regions = [
    { endpoint: US_EAST_1_ENDPOINT, lat: 39.0, lon: -77.5 },
    { endpoint: US_EAST_2_ENDPOINT, lat: 40.0, lon: -83.0 },
  ];
  // Euclidean distance → return closest endpoint
}

The result: sub-100ms database responses regardless of where the user is connecting from.

🎫 Intelligent Ticketing + SLA Engine

Tickets are auto-created from AI conversation analysis with generated titles and descriptions. SLA policies define per-priority timers, and an escalation risk score (0-100) considers:

Factor	Points
SLA due within 30 min	+40
Urgent priority	+25
Customer frustration (4+ follow-ups)	+15
Negative sentiment streak	+15
Stale (8+ hrs no update)	+10

📊 AI-Powered Analytics

AI Copilot — suggests replies to agents with acceptance rate tracking
Voice of Customer — sentiment distribution, complaint detection, praise identification
Agent Quality Score — resolution rate (40%) + sentiment impact (35%) + activity (25%)
Contact Health Score (0-100) — engagement recency, open tickets, message frequency
Topic Trending — intent volume week-over-week with severity classification
Workload Distribution — agent capacity management with concurrent chat limits

📢 Broadcast Campaigns

Scheduled or immediate mass messaging via WhatsApp templates
Audience segmentation with JSON-based filters (tags, segments, contact lists)
Real-time delivery analytics: sent → delivered → read → replied → failed
Template variable substitution: {name}, {phone}, custom fields

Tech Stack

Layer	Technology
Framework	Next.js 16, React 19, TypeScript
Styling	Tailwind CSS 4, Radix UI, shadcn/ui
Database	AWS Aurora DSQL (PostgreSQL-compatible, multi-region)
ORM	Drizzle ORM
File Storage	AWS S3
AI Models	Gemini 2.5 Flash, GPT-4o, Claude Sonnet 4
Embeddings	gemini-embedding-001, text-embedding-3-small
Auth	JWT (jose) + bcrypt, httpOnly cookies
Messaging	Meta WhatsApp Cloud API (Graph v21.0)
Deployment	Vercel (Edge + Serverless)
Charts	Recharts
Forms	React Hook Form + Zod

What's Next

More regions (eu-west-1, af-south-1 for African markets)
Voice message transcription with Whisper
CSAT surveys sent via WhatsApp after ticket resolution
Shopify/WooCommerce integration for order status automation
Team performance leaderboards with gamification

Live Demo: imaradesk-ai.vercel.app

If you're building WhatsApp-based tools or working with Aurora DSQL, I'd love to hear your experience. Drop a comment below!

How I Built a Full Helpdesk SaaS from Scratch as a Solo Developer

Titus Eddy — Fri, 01 May 2026 11:59:09 +0000

The Problem

I needed a helpdesk for a project. Zendesk wanted $55/agent/month. Freshdesk wasn't much better. As a developer I thought — I'll just build one. How hard can it be?
Several months later, ImaraDesk was born.
In this post I want to share the technical decisions, architecture choices and lessons learned building a full SaaS product solo.

The Stack

Backend: Django 5.0+ with Django REST Framework
Frontend: React with Vite
Database: PostgreSQL
Auth: JWT + 2FA implementation
Deployment: Docker

I chose Django because of its batteries-included approach — admin panel, ORM, migrations and authentication out of the box. React on the frontend for a snappy, modern UI.

The Modular Architecture
The most important technical decision I made was building ImaraDesk as a modular system. Each feature — tickets, knowledge base, assets, surveys, tasks — is an independent module that gets activated per workspace.
This means:

Clean separation of concerns
New businesses only activate what they need
Adding new modules doesn't break existing ones
Billing can eventually be per module or per agent

Each module lives in its own folder with its own models, views, serializers and routes.

The Hardest Parts

Multi-tenancy Making sure one business never sees another business's data was the most critical and most stressful part. Every query is scoped to a workspace — one wrong filter and you have a data breach.
SLA Tracking Calculating response and resolution times sounds simple. It isn't. Timezone handling, business hours, paused states — this took longer than I expected.
Real-time notifications Getting email notifications to fire reliably for ticket updates required proper async task handling with Celery.

The Open Source Decision
The core version of ImaraDesk is fully open source on GitHub under AGPL license. Anyone can self-host it for free.
The cloud version at imaradesk.com is the commercial product. This is the open core model. Open source builds trust and community. The cloud product pays the bills.

What I Learned

Ship before you're ready — I kept adding features. At some point you just have to launch.
Documentation is harder than code — writing clear docs took almost as long as building features.
Modular architecture pays off — early investment in clean structure saves massive headaches later.
Solo doesn't mean alone — communities like dev.to, IndieHackers and GitHub stars kept me going.

What's Next

AI features in v2 — suggested replies, auto-tagging, sentiment detection
More integrations — Slack, WhatsApp, email piping
Mobile app

Try It

🔗 Cloud version: imaradesk.com
📖 Open source: github.com/imaradesk/imaradesk-os
📄 Docs: docs.imaradesk.com

I'd love feedback from the dev.to community — what would you add? What would make you self-host or switch from your current helpdesk tool?

DEV Community: Titus Eddy

I Built an AI-Powered WhatsApp Helpdesk with Aurora DSQL, Multi-Model AI, and a Visual Chatbot Builder

The Problem

What I Built

🧠 Multi-Model AI Engine

🤖 Visual No-Code Chatbot Builder (13 Node Types)

📚 RAG Knowledge Base with Vector Embeddings

🌍 Multi-Region Database with AWS Aurora DSQL

🎫 Intelligent Ticketing + SLA Engine

📊 AI-Powered Analytics

📢 Broadcast Campaigns

Tech Stack

What's Next

How I Built a Full Helpdesk SaaS from Scratch as a Solo Developer

The Problem

The Stack