There has been a lot of discussion recently around how far AI can go in customer support systems, especially in telecom environments.

A fully automated support workflow can now realistically handle:

• Incoming messages from WhatsApp or Telegram
• Intent classification (billing, technical, sales, complaints)
• Sentiment detection (including angry/frustrated users)
• Automated response generation using LLMs
• Ticket creation and routing based on severity
• Human escalation for critical cases
• Real-time outage detection based on message spikes

What makes these systems interesting is not just the AI response itself, but the entire support pipeline around it:

• Conversation memory (previous messages used as context)
• SLA-based ticket tracking
• Priority assignment (urgent, high, medium, low)
• Multi-channel orchestration (WhatsApp + Telegram)
• Failure handling when APIs or databases fail

From a systems design perspective, this starts to look less like a chatbot and more like a full customer support infrastructure layer.

In traditional setups, these components are usually split across multiple tools:
CRM systems, ticketing platforms, monitoring dashboards, and human support teams.

Now, many of these responsibilities can be orchestrated through automation workflows combined with LLMs.

A few open questions this raises:

• Where should the boundary between automation and human support be?
• How reliable are these systems when handling edge cases or outages?
• What happens when AI becomes the first and only support layer?
• How should accountability be handled in fully automated customer support flows?

Would be interesting to hear how others are thinking about reliability, safety, and scaling in AI-driven support systems, especially in telecom or enterprise environments.

Github:https://github.com/kevorklepedjian1/TelecomGPT-AI-Support-Automation

I just built an end-to-end AI GTM Automation Engine that fully automates the outbound sales pipeline from lead generation to reply handling.

This system is designed to remove 90%+ of manual work in B2B outreach and replace it with intelligent automation.

What it does:

• Accepts incoming leads via webhook
• Enriches and finds emails using multiple providers (Prospeo, Hunter .io, Dropcontact + AI fallback)
• Validates emails automatically (NeverBounce)
• Scores leads (low / medium / high)
• Generates personalized cold emails using AI
• Sends outreach via Gmail
• Runs multi-step follow-up sequences (Day 2, 4, 7)
• Classifies replies using AI (interested / not_interested / not_now)
• Automatically routes actions based on intent
• Logs everything into Google Sheets
• Sends real-time Slack notifications

Stack:

n8n · OpenAI · Gmail API · Slack API · Google Sheets · Hunter .io · Dropcontact · NeverBounce

This is part of my deeper focus on building AI-powered revenue systems and GTM automation workflows that replace repetitive sales operations with intelligent agents.

GitHub:

https://github.com/kevorklepedjian1/N8N-GTM

Has anyone here experimented with fully autonomous AI voice agents for lead qualification and customer interactions?

Interesting use cases I’ve been seeing lately:

• automatically calling leads after form submissions
• multilingual AI conversations
• website scraping to understand a business before the call
• AI-generated responses/solutions during calls
• automated booking + CRM updates
• follow-up through SMS/WhatsApp/email

It feels like voice agents are moving beyond simple scripted bots into something closer to AI employees handling workflows end-to-end.

Curious what stack people are using for this.

A lot of projects seem to combine:

• n8n
• OpenAI
• Twilio / VAPI
• Supabase
• Calendar integrations
• memory/context systems

Also found an open-source workflow example here:
https://github.com/kevorklepedjian1/n8n-ai-voice-agent

Would love to hear:

• what’s actually working in production
• biggest limitations right now
• latency/cost issues
• how people handle interruptions and context memory
• whether users can tell they’re speaking to AI immediately

.

The idea is to take raw company lists and turn them into structured sales intelligence + personalized outreach automatically.

🧠 System flow:

• Input: CSV / list of companies
• AI research agent extracts what the company does + pain points
• Lead scoring model ranks prospects (0–100)
• High-intent leads trigger email generation
• Results pushed into Airtable CRM
• Slack alerts for qualified leads
• Orchestration handled through n8n
• LLM running locally via Ollama

⚙️ Stack used:

• FastAPI (backend agents)
• LangChain
• Ollama (local model execution)
• n8n (automation layer)
• Airtable (CRM storage)

🏗️ Concept:

CSV → Workflow Engine → AI Agents → Structured Output → CRM + Notifications

Repo (full workflow + backend):
https://github.com/kevorklepedjian1/Autonomous-SDR-System

Curious if anyone else is building similar AI GTM / SDR automation systems or has tried replacing parts of outbound workflows with LLM agents.

Disclaimer: if you want to use this you just have to change some of the apis, add parameters

A multi-layer AI Revenue Intelligence system built with n8n, Redis, PostgreSQL, and LLM agents has been developed to simulate an autonomous RevOps team.

The system connects multiple data sources including:

• HubSpot (CRM pipelines)
• Stripe (revenue and payments)
• Apollo / Hunter / ZoomInfo (lead enrichment APIs)
• PostgreSQL (historical analytics)
• Redis (real-time memory layer)
• OpenAI (decision-making + analysis layer)

System capabilities:

• Automated KPI calculation and tracking
• AI-driven revenue analysis and executive reporting
• Lead enrichment and scoring using external data sources
• Revenue risk detection (conversion drops, anomalies)
• Forecasting (7 / 30 / 90-day projections)
• Multi-channel alerting (Slack, Email, Telegram)
• Memory-based decision making using Redis + Postgres

Architecture style:

Designed as a layered AI system:

Data Ingestion → Enrichment → AI Analysis → Memory Layer → Decision Layer → Notification Layer

The goal is closer to an autonomous RevOps intelligence engine rather than a traditional automation workflow.

Repository:

https://github.com/kevorklepedjian1/revenueos-ai

recently built an AI-powered Revenue Operations system that connects sales calls and CRM data to generate real-time deal intelligence.

It’s fully open-source and built for experimentation / improvement by anyone working on RevOps, AI agents, or sales automation.

🔧 What it does:

Every hour, it:

• Pulls sales calls from Gong
• Fetches deals + contacts from HubSpot
• Normalizes and deduplicates all data
• Sends everything through an AI analysis layer (GPT-based)

🧠 AI extracts:

• Pain points
• Objections
• Competitors mentioned
• Sentiment
• Deal risk score (low / medium / high)
• Key topics

🚨 Automations:

• High-risk deals trigger Slack alerts
• All insights are stored in Supabase
• CRM (HubSpot) is updated automatically with AI-generated fields
• Structured insights are logged in Notion

🏗️ Tech stack:

• n8n (workflow automation)
• OpenAI (LLM analysis)
• Gong (sales calls)
• HubSpot (CRM)
• Supabase (database)
• Notion (notes / tracking)
• Slack (alerts)

📦 Github Repo:

https://github.com/kevorklepedjian1/ai-revops-intelligence-engine

Just passed the HubSpot Academy Revenue Operations Certification ✅
Over the past few weeks, I’ve been doubling down on how modern GTM systems actually work — not just marketing or sales in isolation, but the full lead → pipeline → revenue engine.
What stood out the most:
Revenue Ops isn’t about tools — it’s about system design
The real leverage comes from automation + data flow + alignment
Most companies don’t have a GTM problem… they have a systems problem
Alongside this, I’ve been building:
→ End-to-end GTM automation systems (lead sourcing → enrichment → outreach → reply handling)
→ Workflows using n8n, HubSpot, Salesforce, Clay, and AI agents
→ Full pipeline visibility with attribution + error handling (DLQs, alerts, etc.)
My focus:
Designing scalable, AI-powered GTM systems that actually drive revenue — not just activity.
If you're hiring or thinking about improving your outbound / RevOps infrastructure, I’d love to connect.
linkedin:https://www.linkedin.com/in/kevork-lepedjian/
#GTM #RevOps #HubSpot #Automation #AI #SalesOps #MarketingOps

I’ve been experimenting with n8n for more complex GTM workflows and ran into a problem — most setups work fine at small scale, but start breaking once there’s real volume or multiple integrations involved.

So I tried restructuring things more like a system instead of a single workflow, and I’m curious how others are approaching this.

A few things I’ve been testing:

• Handling bad data without losing it (kind of like a DLQ pattern)
• Keeping attribution clean when sending conversions to multiple platforms (Ads, etc.)
• Structuring workflows so failures don’t break everything downstream
• Making debugging easier when something goes wrong across multiple nodes/workflows

I’m especially interested in how people are:

• Designing multi-workflow architectures in n8n
• Handling retries / failed executions cleanly
• Keeping data consistent across tools like CRMs + databases

Would be great to hear how others are solving this, or if there are patterns/tools I should look into.

https://github.com/kevorklepedjian1/Revenue-OS-AI-GTM-Automation-System-n8n-HubSpot-Ads-/tree/main

I’ve been building something over the last few weeks and wanted to share it because I’m curious if others are solving this the same way.

Most “GTM automation setups” I see (Zapier, Make, basic n8n flows) look fine on the surface… but they usually break the moment you try to run them in anything close to production.

So I ended up rebuilding mine from scratch in n8n as a full revenue pipeline system, not just a workflow.

What I built

1. Lead pipeline (end-to-end)

A simple lead doesn’t just get “captured” anymore.

It goes through:
Webhook → normalize → validate → enrich (Clearbit) → score → HubSpot → Postgres → Slack

So every lead is:

• cleaned before entering the system
• enriched with context
• scored for intent
• stored properly
• and instantly visible to sales

2. Conversion tracking (real attribution)

When a deal closes or a conversion happens:

• it gets sent to Google Ads
• it gets sent to Meta
• and the team gets notified in Slack

The goal here was simple: stop guessing where revenue actually comes from.

3. Dead Letter Queue (DLQ)

This was a big one for me.

Instead of losing bad data or broken leads:

• everything invalid gets captured
• stored with the raw payload
• and logged with an error reason
• plus a Slack alert so it’s visible immediately

Nothing silently disappears anymore.

4. Global error handling

If any workflow fails anywhere in the system:

• it gets caught globally
• logged with workflow name + error message
• and sent to Slack

So debugging isn’t “hunt and pray” anymore.

What changed for me

The biggest shift wasn’t adding complexity.

It was treating GTM like infrastructure instead of automation.

Things like:

• observability
• failure handling
• clean data flow
• and traceability end-to-end

made way more difference than any “AI feature” I could’ve added.

Curious what others think

Has anyone here built something similar?

Especially around:

• structuring multi-workflow n8n systems
• handling DLQs properly
• or doing attribution without everything becoming messy over time

Would be interested to compare approaches.

https://github.com/kevorklepedjian1/Revenue-OS-AI-GTM-Automation-System-n8n-HubSpot-Ads-/tree/main

I just built an end-to-end AI GTM Automation Engine that fully automates the outbound sales pipeline from lead generation to reply handling.

This system is designed to remove 90%+ of manual work in B2B outreach and replace it with intelligent automation.

What it does:

• Accepts incoming leads via webhook
• Enriches and finds emails using multiple providers (Prospeo, Hunter .io, Dropcontact + AI fallback)
• Validates emails automatically (NeverBounce)
• Scores leads (low / medium / high)
• Generates personalized cold emails using AI
• Sends outreach via Gmail
• Runs multi-step follow-up sequences (Day 2, 4, 7)
• Classifies replies using AI (interested / not_interested / not_now)
• Automatically routes actions based on intent
• Logs everything into Google Sheets
• Sends real-time Slack notifications

Stack:

n8n · OpenAI · Gmail API · Slack API · Google Sheets · Hunter .io · Dropcontact · NeverBounce

This is part of my deeper focus on building AI-powered revenue systems and GTM automation workflows that replace repetitive sales operations with intelligent agents.

GitHub:

https://github.com/kevorklepedjian1/N8N-GTM