AI Deploy Network
Ahmad Sofian

AI Deploy Network / Builder

Ahmad Sofian

AI Engineer

IndonesiaSeniorAvailable Now

Published evidence

1 AI Deployment

1 publicly identified

1 Case Study

Published implementation narrative

01

About Ahmad

With a solid background of 7+ years in full-stack development and team leadership, I help organizations implement practical, high-impact AI solutions. I specialize in deploying custom AI chatbot systems that combine RAG, vector databases (Qdrant), and real-time data contexts.

Whether it's setting up local LLM processing (Qwen) for secure data handling or engineering real-time data pipelines with TimescaleDB and WebSockets, I build systems that scale. Having successfully delivered critical platforms for national government agencies and complex IoT environments, I focus on shipping code that is production-ready, highly optimized, and tailored to real business outcomes.

Professional profile

Location
Indonesia
Time zone
Asia/Jakarta
Work mode
Remote
Availability
Available Now
Preferred engagement
Small (individual or single team)

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 Ahmad builds

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

Specialisations

Agentic Workflows

Builder-listed deployment types

  • AI Agent
  • RAG System
  • Workflow Automation

04

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

On-Premise RAG Assistant & Real-Time IoT Dashboard for Environmental Analytics

Deployed a secure, high-performance on-premise AI assistant integrated with an IoT analytics monitoring dashboard, leveraging a localized RAG pipeline to provide real-time context and intelligent document querying

IT Services & Systems Integration · AI Agent · RAG System · 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

  • AI Agent2 records
  • RAG System2 records
  • Workflow Automation2 records

Builder declared

  • Agentic Workflows

06

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

  • Docker1 record
  • Python1 record
  • Qdrant1 record
  • Qwen 2.51 record

Builder declared

  • Fiber
  • Go
  • Laravel
  • Next.js
  • PHP
  • PostgreSQL
  • Vue

07

Industry experience

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

IT Services & Systems Integration

1 published Deployment

Cybersecurity

Builder declared

08

Case Studies

Published narratives of implementation and results.

IT Services & Systems Integration · AI Agent · RAG System · Workflow Automation

On-Premise RAG Assistant & Real-Time IoT Environmental Monitoring System

Architected and deployed a secure, fully on-premise AI assistant integrated with a real-time IoT monitoring dashboard. The system combines local LLM processing (Qwen-7B via llama.cpp) with an optimized RAG pipeline and TimescaleDB data ingestion, delivering sub-second response times for complex environmental query analytics without relying on external cloud APIs

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 Ahmad Sofian 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.