AI Automation Developer  ·  Voice · SMS · LinkedIn · Email

Sovon Shaik

I build AI systems that call — while you sleep.

Voice agents, SMS agents, LinkedIn agents and end-to-end outbound engines built on n8n, Supabase, Retell, Vapi and custom API layers. Not demos — production systems that run unattended, every day, at scale.

See the systems → Book a call aimamoth.com
24/7Unattended operation
4 channelsVoice · SMS · Email · LinkedIn
n8nOrchestration core
End-to-endArchitecture to handoff
AI Voice AgentsAI SMS AgentsCustom LinkedIn Agentn8n OrchestrationSupabaseRetell AIVapiGoHighLevelAirtableCustom API IntegrationsLead GenerationCold Email Infrastructure AI Voice AgentsAI SMS AgentsCustom LinkedIn Agentn8n OrchestrationSupabaseRetell AIVapiGoHighLevelAirtableCustom API IntegrationsLead GenerationCold Email Infrastructure
By the numbers

Real output, from a real client

These figures come from live campaigns run for a small US business — not a lab test. Modest volume, but every meeting below was sourced, qualified and booked without a human touching it.

12
Qualified meetings booked autonomously
voice agent + email engine
9
Qualified meetings from the AI voice agent
Project 01 · booked into GHL mid-call
3
Meetings booked from cold email
Project 02 · fully automated sourcing
2–4%
Cold email reply rate
Project 02 · at or above industry norm
4%
SMS response rate
Project 03 · self-built infrastructure
10–15%
LinkedIn connection acceptance
Project 04 · cold, no warm intro
4
Outbound channels orchestrated
voice · SMS · email · LinkedIn
24/7
Unattended operation
retries · fallbacks · alerting

Small numbers, honestly reported. The point isn't the volume — it's that the entire path from cold list to booked calendar slot ran unattended, and every one of these systems is architected to scale on the same rails.

What I do

Autonomous systems, not automations

Most "automation" still needs a human babysitting it. I design the retry logic, state handling and escalation paths so the system keeps running when things go wrong — which is the only thing that matters in production.

AI Voice Agents

Outbound and inbound conversational agents on Retell and Vapi — call scheduling, retry cadences, live qualification, objection handling and calendar booking mid-call.

AI SMS Agents

Two-way SMS conversation engines with intent detection, human handoff and message routing into whatever inbox the operator already lives in.

Custom LinkedIn Agent

A self-built LinkedIn outreach agent — prospect discovery, connection requests, AI-written personalised follow-ups and reply detection, all sequenced with human-like pacing and daily limits.

Workflow Orchestration

n8n as the nervous system — webhooks, queues, conditional branching, error workflows and scheduled triggers wiring every tool into one pipeline.

Data & Backend

Supabase and Airtable as the system of record — lead state, call outcomes, dedupe logic, and API endpoints custom-built where off-the-shelf tools stop.

Outbound at Scale

Cold email infrastructure, deliverability hygiene, AI-personalised copy per prospect, lead generation and enrichment running in parallel across channels.

Selected work

Systems running hands-off

Each of these was built end-to-end — architecture, integrations, agent design and the failure handling that keeps them alive.

PROJECT 01 — REAL ESTATE

AI Voice Agent for Distressed Property Outreach

Voice agents · n8n · Airtable · GoHighLevel
Fully autonomous — zero human touch

An AI voice agent that calls distressed property owners three times a day on a managed cadence, holds a real qualification conversation, and books the meeting straight into GoHighLevel while the prospect is still on the line. The owner gets an instant SMS notification the moment a booking lands.

Airtable list→ 3× daily call trigger→ AI voice qualification→ GHL booking→ SMS alert
9Qualified meetings booked
3×/dayCall cadence per lead
0Human hours per booking
AI Voice Agentsn8nAirtableGoHighLevelSMS
PROJECT 02 — OUTBOUND ENGINE

Indeed-to-Inbox: Autopilot Cold Email Machine

Indeed API · Enrichment · AI copy · Email infrastructure
Runs on autopilot — no manual step

Scrapes live job listings via the Indeed API, resolves each listing to a company, identifies the right decision-maker, finds and verifies their email, then generates a genuinely personalised message per contact and sends at scale. Hiring signals become booked conversations without anyone opening a spreadsheet.

Indeed API→ Company resolve→ Decision-maker→ Email find + verify→ AI personalisation→ Send at scale
2–4%Reply rate
3Meetings booked
100%Sourcing automated
Indeed APIn8nEmail VerificationAI PersonalisationCold Email
PROJECT 03 — INFRASTRUCTURE

Custom SMS Outreach System with Telegram Control

Custom API layer · Telegram · SMS gateway
Built from scratch — own the whole stack

Rather than paying for a rigid SMS platform, I built my own. Messages go out to US prospects through a custom API layer, and every reply lands in Telegram — so the whole conversation can be handled from a phone, in a thread, with no CRM tab open. Full control over sending logic, threading and routing.

Telegram thread↔ Custom API layer↔ SMS gateway↔ US prospect
4%Response rate
<1 minReply-to-Telegram latency
$0Third-party platform fees
Custom APITelegram Bot APISMSn8nSupabase
PROJECT 04 — SOCIAL OUTBOUND

Custom LinkedIn Outreach Agent

Custom API layer · AI messaging · n8n · Supabase
Self-built — human-paced, fully sequenced

A LinkedIn agent I built rather than rented. It sources prospects against an ICP, sends connection requests on human-like timing within safe daily limits, writes a personalised follow-up per profile using their actual role and company context, then detects replies and pulls the interested ones out of the sequence into a live conversation queue.

ICP prospecting→ Connection request→ AI personalised DM→ Follow-up sequence→ Reply detection→ Handoff
10–15%Connection acceptance
0Account restrictions
100%Sequencing automated
LinkedIn OutreachCustom APIAI Personalisationn8nSupabase

…and a long tail of systems built on n8n

These four are the highlights. The rest — internal ops bots, CRM syncs, AI research pipelines, reporting automations, scraping infrastructure — live at aimamoth.com.

CRM sync pipelines AI research agents Scraping infrastructure Reporting automations Internal ops bots Webhook middleware
Explore more at aimamoth.com →
Tech stack

What I build with

Tool-agnostic by preference — I pick whatever gets the system to production fastest and keeps it maintainable afterwards.

AI & Voice

Retell AIVapiOpenAIAnthropic ClaudePrompt engineeringAgent design

Automation & Orchestration

n8nMakeWebhooksCron / schedulersError workflowsQueues

Data & Backend

SupabasePostgreSQLAirtableGoogle SheetsREST APIsCustom API calls

CRM & GTM

GoHighLevelHubSpotInstantlyTwilio / SMS gatewaysTelegram Bot APICalendar APIs

Growth Skills

Lead generationEmail marketingDeliverabilityLinkedIn outreachOutbound copywritingList building & enrichment

Engineering Basics

JavaScriptPythonJSON / data mappingAuth & API keysRate limitingLogging & monitoring
How I work

From problem to unattended system

The build is the easy part. Making it survive real-world edge cases is where the work actually is.

Map the process

Understand the manual workflow first — the exceptions, the judgement calls, the bits nobody documented.

Design the architecture

Decide what holds state, what triggers what, and where the human still needs to be in the loop.

Build & integrate

Agents, workflows, APIs and database wired together — with retries and fallbacks built in from day one.

Harden & hand off

Monitoring, error alerting and documentation so the system runs without me standing next to it.

The unglamorous part

Why these don't break

Anyone can wire a happy path. The difference between a demo and a system someone trusts with their pipeline is what happens on the bad days.

Retries & idempotency

Every external call has a retry policy and a dedupe key. A timeout on a provider doesn't double-send an email or double-book a calendar slot.

Error workflows & alerting

Dedicated n8n error workflows catch failed executions and push a readable alert with the payload — so a break is noticed in minutes, not at month-end.

Rate limits & compliance

Human-paced sending windows, per-account daily caps, suppression lists and opt-out handling built in — so volume never costs you the account or the domain.

Documentation & handoff

Architecture notes, env/credential inventory and a runbook per system — so the business isn't hostage to the person who built it. Including me.

If you hire me

What the first 30 days look like

No ramp-up theatre. Here's the plan I'd run from day one.

Week 1 — Audit

Inventory every existing workflow, integration and manual process. Find what's silently broken and what's costing the most hours.

Week 2 — Quick win

Ship one automation that removes a real, measurable chunk of manual work. Build trust with output, not with a roadmap deck.

Week 3 — Harden

Add monitoring and error handling to the systems already running. Stop the silent failures nobody's tracking yet.

Week 4 — Scale plan

Propose the next three builds ranked by hours saved and revenue touched, with honest effort estimates attached.

Let's talk

Ready to build systems that run themselves

If you're hiring an AI Automation Developer who can own a system end-to-end — architecture, agents, integrations and the boring reliability work — I'd like to hear about the problem you're solving.

📅 Book a call with me → sovon@aimamoth.com
Available for full-time or contract Remote · comfortable across AU, US & EU hours Response within 24 hours