- Slow response times leading to customer frustration and churn
- Repetitive queries consuming valuable agent time
- Lack of intelligent routing causing mishandled tickets
- Disconnected systems creating data silos
- Complex pricing making it inaccessible for SMBs
All case studies
Case study · Aether AI Support
Building Aether AI Support.
How we built a modern, AI-powered customer support platform from the ground up using cutting-edge technologies and cloud-native architecture.
6
Months development
50K+
Lines of code
120+
API endpoints
85%
Test coverage
Client
Aether AI Support
Industry
Customer Support · AI
Services
Full-stack development
Traditional support is broken.
Traditional customer support software suffers from critical limitations. We set out to build a platform that leverages AI to solve these problems while remaining affordable and easy to implement.
AI-first, real-time, multi-tenant.
Aether AI Support combines the latest in AI technology with modern web development practices to deliver a transformative support experience.
01
AI-First Architecture
We built a Retrieval-Augmented Generation (RAG) pipeline using OpenAI GPT-4o-mini and ChromaDB vector embeddings. The AI understands context, retrieves relevant knowledge base articles, and generates accurate responses in real-time.
02
Real-Time Collaboration
Using WebSockets and Server-Sent Events (SSE), we implemented instant updates across the entire platform. Agents see live ticket changes, typing indicators, and presence status without page refreshes.
03
Multi-Tenant SaaS Design
The platform supports unlimited companies with complete data isolation. Each tenant can customize their widget, knowledge base, SLA rules, and integrations independently.
04
Serverless Infrastructure
By deploying the backend on Google Cloud Run and frontend on Vercel, we achieved automatic scaling, zero cold-start latency, and 99.95% uptime without managing servers.
How it's built.
Backend architecture
FastAPI for its exceptional performance (40,000+ requests/second) and native async support. The API is fully typed with Pydantic models, providing automatic validation and OpenAPI documentation.
- Service-oriented design with dependency injection
- Background task processing with asyncio
- Structured logging with correlation IDs
- Rate limiting and quota enforcement
AI & machine learning
The RAG pipeline processes incoming queries through multiple stages.
- Query analysis: intent detection and entity extraction
- Retrieval: hybrid semantic + keyword search
- Context assembly: relevance scoring and window optimization
- Generation: GPT-4o-mini with custom system prompts per company
- Post-processing: confidence scoring and escalation detection
Frontend engineering
Next.js 14 with App Router provides server-side rendering, streaming responses, and optimal bundle splitting.
- Context-based theming system (light/dark/gradients)
- Framer Motion animations for smooth UX
- Cmd+K command palette with keyboard shortcuts
- Optimistic UI updates with SWR caching
- Responsive design with Tailwind CSS
Database design
Google Firestore provides automatic scaling and real-time sync capabilities.
- Companies: multi-tenant configuration and settings
- Tickets: support requests with full history
- Messages: conversation threads with metadata
- Knowledge Base: articles, FAQs, and embeddings
- Users: agents, admins, and customers
Measurable impact.
75%
Ticket deflection
AI handles most common queries automatically
<2s
Avg response time
vs. industry average of 12+ hours
4.8/5
CSAT score
From post-conversation surveys
99.95%
Uptime
Serverless architecture ensures reliability
What we'd tell our past selves.
Start with the AI, not the UI
We built the RAG pipeline and AI capabilities first, then designed the interface around them. This ensured the AI was a core feature, not an afterthought.
Invest in real-time from day one
Retrofitting WebSocket support is painful. By designing for real-time updates from the start, we avoided technical debt and delivered a more responsive experience.
Multi-tenancy requires early planning
Every database query, API route, and frontend component needed tenant awareness. Planning this architecture upfront saved months of refactoring.
Type everything
TypeScript on the frontend and Pydantic on the backend caught countless bugs before they reached production. Strong typing is non-negotiable.
Built with
Technology stack.
AI / MLFastAPINext.jsPythonTypeScriptGCPFirestoreOpenAI GPT-4o-miniChromaDBWebSocketsVercelCloud Run
Try it live
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Start your 14-day free trial and experience AI-powered support. Or contact us to discuss building a similar platform for your business.
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