AI Deploy Network
Favian Izza

AI Deploy Network / Builder

Favian Izza

Backend Automation Engineer | CI/CD, Schedulers, Event Triggers, Push Notifications

IndonesiaJakartaSeniorAvailable for Contract Projects

Published evidence

1 AI Deployment

1 publicly identified

1 Case Study

Published implementation narrative

01

About Favian

I build backend automation for production systems: CI/CD pipelines (GitHub Actions running lint, test, and build on every push, with automated dependency updates via Dependabot), scheduled jobs (cron-based retention and cleanup tasks), event-driven triggers (MQTT-based anomaly detection and BullMQ background job queues), and push notification systems (Telegram Bot API and SMTP email alerts). I use LLM coding agents such as Claude Code as part of my development workflow for implementation, testing, and code review. Example: Netflow, an IoT prepaid utility metering system streaming live MQTT telemetry from 2,444+ smart meters into a NestJS and MySQL backend, with a 12-detector anomaly engine covering tamper, reverse-flow, and sensor-fault conditions, backed by 166 Jest unit tests.

Professional profile

Location
Jakarta, Indonesia
Time zone
Asia/Jakarta
Work mode
Remote
Availability
Available for Contract Projects
Languages
English, Indonesian
Available in
Indonesia, Malaysia, Singapore

02

Proven on the network

Implementation records and publicly available work, where published.

1 AI Deployment delivered, including 1 publicly identified implementation available to inspect.

03

What Favian builds

Implementation domains listed on this profile. Evidence-supported items are identified separately below.

Specialisations

Workflow Automation

Builder-listed deployment types

  • Process Automation
  • Workflow Automation

04

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Netflow: Automated Prepaid Utility Metering with Real-Time Anomaly Detection

Backend system automating prepaid electricity and water metering across multi-tenant buildings, replacing manual monthly meter reading with real-time MQTT telemetry, automated billing, and a scheduled, trigger-based anomaly detection pipeline.

Energy & Utilities · Process Automation · Workflow Automation

View Deployment

05

Implementation capabilities

Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.

Evidence-supported

  • Workflow Automation2 records
  • Process Automation2 records

Builder declared

    No additional declared items.

    06

    Technology

    Technologies listed by the Builder, distinguished by published evidence.

    Evidence-supported

    • Claude Code2 records
    • GitHub Actions2 records
    • NestJS2 records
    • Redis2 records
    • Telegram2 records
    • TypeScript2 records

    Builder declared

    • Model Context Protocol

    07

    Industry experience

    Published Deployments support the listed counts. Other industries are Builder declared.

    Energy & Utilities

    1 published Deployment

    E-Commerce

    Builder declared

    IT Services & Systems Integration

    Builder declared

    Professional Services

    Builder declared

    08

    Case Studies

    Published narratives of implementation and results.

    Energy & Utilities · Process Automation · Workflow Automation

    Netflow: Automated Prepaid Utility Metering with Real-Time Anomaly Detection

    A 4-person capstone team built Netflow to replace fully manual electricity and water metering across a 2,444-unit Bandung apartment complex. I built the server-side monitoring, security, maintenance, and data lifecycle subsystems, cutting anomaly detection latency from a monthly inspection cycle to under 90 seconds, measured in production.

    Read case study

    09

    Trust & verification

    A closer look at how this profile and its evidence are reviewed.

    Verified Builder

    AI Case Studies verified

    Published evidence is reviewed against the AI Deploy Network Evidence Standard.

    How verification works
    Verified

    Verified Builder

    Independently reviewed by AI Deploy Labs against the platform Evidence Standard.

    • AI Case Studies Verified
    How this is verifiedWhat the platform has established, and what remains builder-declared.
    Identity and organisation
    Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
    AI Deployments
    An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
    Business outcomes
    Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
    Evidence
    Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
    Currency
    Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
    Traceability
    Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.

    The platform does not independently audit client systems or financial records. How this is verified

    Ready to discuss your AI implementation?

    Tell Favian Izza what you are trying to automate and the outcome you need. Your message goes directly to the builder, with a copy kept in your Organisation Workspace.