I Build Operational
Systems That Scale
Automation & Operations Systems Specialist.
Built systems processing 1,000+ automated messages/month.
Processed 5,000+ resumes in full-cycle recruitment.
Acquired 2,000+ users in 3 months.
Most teams don’t have a talent problem.
They have a system problem.Manual work.
Scattered tools.
No visibility.
No ownership.I design automation infrastructure that eliminates repetitive workflows, centralizes data, and turns operations into something measurable.Less chaos.
More signal.
No babysitting required.(Yes, I replace daily stand-up that pretend to be systems.)
Case Studies...
Case Study #1 - Unified Ops Dashboard
Role
Fractional Operations Systems LeadTools
Node.js · Express.js · n8n · GA4 · Google Looker Studio · Supabase · Google Sheets · Kitchen.co API · Publer API · Cron · PM2Background
My boss needed one simple dashboard:
- Operational health
- Social performance
- Revenue signals
- Campaign trackingBefore:
Data was scattered across platforms.ContributionPhase 1:
Pulled data using Google Apps Script.Phase 2:
Migrated to local Node.js + Express server for:
- Better stability
- Cron-based scheduled pulls
- API orchestration
- Centralized transformation layerIntegrated:
- GA4
- Kitchen.co
- Publer
- Internal systemsVisualized everything in Looker Studio.Impact
- Unified fragmented operational data
- Reduced manual reporting
- Improved decision clarity
- Increased visibility on campaign performance
- Built scalable reporting infrastructure
Case Study #2 - ATS Signal Hunter
Role
Automation SpecialistTools
n8n · Node.js · Puppeteer (real-browser) · Supabase · Cron · PM2 · Webhooks · LLM API · Google Sheets · Telegram Bot API · Linux local serverBackground
We were running marketing campaigns targeting specific ICPs.
One strong hiring signal: when those companies opened new roles.Problem?
We were checking manually.
Too many job boards. Too many filters. Too many tabs.
We missed early signals.
We reacted late.
Cognitive load was high. ROI was low.Humans are bad at checking 10 sources every day. Especially before coffee.Contribution
So I built a hiring signal automation engine.First layer:
Found a workaround inside ATS systems so we could detect job postings faster than job aggregators. We were early sometimes hours early.Second layer:
Built multi-source scraping system (big four of ATS provider)Third layer:
Integrated n8n + LLM to:
- Filter relevant jobs
- Remove noise
- Classify ICP match
- Generate structured summariesOutput:
Clean signals delivered to Telegram + Sheets dashboard.Team no longer scans chaos.
They review decisions.Impact
- Cut signal discovery time from manual daily scanning → automated near real-time
- Increased early outreach speed (we were ahead of aggregators)
- Reduced cognitive load for team
- Turned job postings into predictable outbound triggers
- Built reusable hiring intelligence infrastructureWe stopped guessing.
We started reacting before competitors.
Case Study #3 - Content Handoff Automation
Role
Automation & Systems DesignerTools
n8n · Google Sheets (Advanced) · Telegram Bot API · Webhooks · LLM (light classification) · SlackBackground
Content workflow was messy.When content was updated, my boss had to manually notify the team so it could be migrated to project management.Inventory file?10+ tabs.
Unstructured.
Redundant fields.
High friction.It wasn’t a system.
It was a spreadsheet jungle.Contribution
Step 1 - Clean the foundation
Reduced 10+ tabs into 3 structured tabs.Step 2 - Automate the handoff
Built n8n workflow that:
- Detects status change
- Extracts structured content data
- Sends formatted summaryNow content updates trigger project execution automatically.Impact
- Eliminated manual content notifications
- Reduced operational friction
Increased workflow clarity
- Created structured content database ready for automation
- Saved leadership time daily
- Boss stopped acting like a notification server.
Case Study #4 - VA Job Hunt Bot (MimiVA)
Role
Founder & Automation EngineerTools
Node.js · Puppeteer · n8n · Telegram Bot API · Cron · PM2 · Rate-limit handling · LLM filteringBackground
Many VAs in Indonesia struggle to find remote US-based work.Problem:
- Platforms overloaded
- Manual searching exhausting
- Opportunities missed
- No personalization
So I built MimiVA.Daily relevant remote jobs.
Direct to Telegram.Contribution
Built multi-source scraping system:
- Upwork
- Indeed
- Custom filters
Integrated filtering layer:
- Relevance classification
- Keyword matching
- DeduplicationDelivered personalized daily job feed to users.Impact
- Helped VAs access global remote jobs daily
- Reduced search friction
- Created scalable distribution bot
- Automated 1,000+ message deliveries monthly
- Built replicable job-discovery infrastructure
Case Study #5 - UGC Video Generator
Role
AI Automation ArchitectTools
n8n · Node.js · Kie.ai API · LLM · HTTP Webhooks · Asynchronous Polling Logic · Box API · Cron · JSON Prompt TemplatingBackground
I built a system that turns structured marketing copy into ready-to-publish AI-generated UGC videos.Contribution
Built a full AI video production engine.
1. Script Compiler Engine
Input structure:
Hook → Problem → Solution → CTA
System:
- Splits long sections into scenes
- Applies deterministic environment selection
- Maintains character consistency
- Enforces selfie-style UGC constraint
- Generates image + video prompts2. Deterministic Scene Generation
- Auto aspect ratio (9:16 / 16:9)
- Environment selection via script hashing
- Visual continuity across scenes3. Asynchronous Job Handling
- POST task submission
- Polling loops
- Retry logic
- Success validation
- Automatic media retrieval
- Reliable automation > blind optimism.4. Media Pipeline
- Inject image URLs into video generation
- Batch scene processing
- Retrieve binary files
- Upload final MP4 to Box
- Auto file naming logic
- End-to-end automation.Impact
- Reduced production time from hours → minutes
- Removed need for manual editing
- Enabled scalable short-form video generation
- Created reusable AI media infrastructure
- Turned scripts into deployable assets automatically
Case Studies #6 - Konekseed
Role
Founder & CEOBackground
Finding romantic partners is easy.
There’s Tinder.Finding business co-founders?In Indonesia, aspiring entrepreneurs lacked structured networking tools.So I built one.Think Tinder.
But for builders.Contribution
- Defined vision and product strategy
- Built MVP with matching logic and onboarding flow
- Secured angel funding (affiliated with Google, Maersk, Grab)
- Acquired 2,000+ users in 3 months
- Screened 1,200+ applicants
- Built internal culture, reporting, compliance
- Led product validation and go-to-marketImpact
- Proved demand for structured founder matching
- Built funded startup from scratch
- Created ecosystem for early-stage collaboration
- Developed leadership across product, ops, hiring, fundraisingFrom idea → funded → shipped.
