Most cold outreach platforms are either:
- expensive,
- heavily restricted,
- or difficult to customize for real operational workflows.
So I started building an internal outreach automation system under Aurvyz — an engineering-focused company building intelligent systems, automation infrastructure, and scalable operational tools for businesses.
The goal wasn’t just “send emails”.
The goal was building a scalable operational system capable of handling:
- lead imports
- AI-generated personalization
- multi-step campaign flows
- follow-up automation
- reply classification
- real-time tracking
🚀 Core Features
🧠 AI-Driven Personalization
The system uses the Google Gemini API to generate:
- contextual intro lines
- personalized outreach
- follow-up variations
- reply classifications
Instead of static templates, the engine dynamically generates messaging based on:
- company information
- industry
- lead context
- workflow relevance
📂 Apollo CSV Import & Validation
The platform supports large Apollo lead imports with:
- CSV validation
- email verification workflows
- deduplication
- lead normalization
This allows campaigns to scale while keeping data quality clean.
⏰ Intelligent Scheduling & Send Windows
One of the biggest challenges in outreach automation is avoiding “robotic” sending behavior.
The system includes:
- custom send windows
- weekday-only automation
- randomized delays
- sequence timing logic
Example:
send only between 9 AM – 5 PM
wait 3 business days before follow-up
randomize delivery timing between emails
This helps mimic natural human sending patterns.
📦 Multi-Step Campaign Architecture
Instead of one-off email blasts, the platform is designed around conversational campaign flows.
Example sequence:
Intro → Follow-up → Value Add → Re-engagement
Features include:
- unlimited sequence steps
- precision delay configuration
- automated progression logic
- reply-aware scheduling
🛑 Intelligent Stop-on-Reply Logic
One of the most important systems in the platform is the reply interruption engine.
Once a reply is detected:
all future sequence steps are halted automatically
the lead is marked for human takeover
campaign progression stops instantly
This prevents awkward double-outreach and duplicate follow-ups.
📨 Multi-Infrastructure Email Engine
The delivery layer integrates multiple providers:
- Resend
- Postmark
Zoho SMTP
The architecture supports:provider fallback routing
delivery retries
bounce handling
provider failover
If one provider fails, the system can automatically reroute delivery traffic.
🏷️ AI Reply Classification
Incoming replies are processed using LLM classification logic.
Replies are automatically categorized into:
- Interested
- Later
- Not a fit
- Booked call
This makes campaign management significantly easier.
⚡ Architecture Highlights
The most interesting part of this project has been building the distributed processing architecture.
🚀** Async-First FastAPI Backend**
The API layer is built using FastAPI with async-first patterns for handling:
- concurrent requests
- webhook ingestion
- scheduling orchestration
- AI generation workflows
⛓️ Celery + Redis Distributed Workers
Heavy operations are fully decoupled from the API layer:
- email sending
- AI generation
- scheduling
- follow-up processing
This keeps the main application responsive while workers handle background tasks asynchronously.
🏗️ Horizontally Scalable Workers
Workers are deployed independently and can scale horizontally based on workload.
This allows the system to handle:
10 emails
or 10,000 emails
using the same architecture.
⚡ Real-Time Webhook Processing
Instead of polling inboxes repeatedly, the platform uses webhook-driven event processing for:
- replies
- opens
- clicks
- delivery events
This significantly reduces latency and unnecessary processing overhead.
🛠️ Tech Stack
- Frontend
- Next.js 15
- TypeScript
- Tailwind CSS
- shadcn/ui
- Backend
- FastAPI
- SQLAlchemy
- PostgreSQL
- Celery
- Redis
- Infrastructure
- Docker
- Jenkins CI/CD
- Google Cloud Run
- Cloud Scheduler
💡 Engineering Challenges
Some of the more interesting problems included:
- designing distributed campaign progression
- building stop-on-reply orchestration
- handling multi-provider email failover
- scheduling around business-day logic
- scaling AI generation workloads asynchronously
🤝 Collaboration & Exploration
Currently exploring and collaborating around:
- AI systems & workflow automation
- scalable backend architecture
- distributed worker systems
- product engineering
- SaaS infrastructure
- operational tooling
Always interested in connecting with:
- developers
- founders
- product teams
- early-stage startups
- automation-focused businesses
Built under Aurvyz
Intelligence, Engineered.
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