AI Deploy Network
Rahmat Akbar

AI Deploy Network / Builder

Rahmat Akbar

Senior AI Software Engineer

IndonesiaSenior

Published evidence

1 AI Deployment

1 publicly identified

01

About Rahmat

Senior AI Software Engineer with a background in Aerospace Engineering, currently focused on building AI-powered platforms for software development.

I work on AI-driven systems that support the full Software Development Life Cycle (SDLC), including agentic and multi-agent architectures, LLM-oriented system design, and AI application architecture for production environments.

Read more

My experience spans from hands-on engineering to leading AI-focused initiatives, with a strong emphasis on turning AI capabilities into reliable, scalable systems rather than one-off demos. I care deeply about system robustness, clear architecture, and real-world usability.

I strongly identify as a problem solver and troubleshooter. Across my roles in software and AI, I often take ownership of complex, ambiguous problems and drive them to practical solutions through structured thinking and execution.

Professional profile

Location
Indonesia

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.

Reported business outcomes
  • Hours Automated9 hours

    period not specified

    Automated 9 hours (period not specified)

Builder-reported outcomes, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.

03

What Rahmat builds

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

Specialisations

Agentic Workflows

Builder-listed deployment types

  • Chatbot
  • Conversational AI

04

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Internal fraud-analysis chatbot

Internal fraud-analysis chatbot combining SQL-based analytics, document embeddings (FAISS), and LLM reasoning. Supports natural-language queries over tabular fraud data and fraud reports, powered by a modular Python agent architecture.

9 hours automated

Professional Services Ā· Chatbot Ā· Conversational AI

View Deployment

05

Implementation capabilities

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

Evidence-supported

  • Chatbot1 record
  • Conversational AI1 record

Builder declared

  • Agentic Workflows

06

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

    No published evidence linked yet.

    Builder declared

    • LangChain
    • LangGraph
    • Langfuse
    • LiteLLM
    • Pydantic AI
    • Python

    07

    Industry experience

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

    Professional Services

    1 published Deployment

    IT Services & Systems Integration

    Builder declared

    Software & SaaS

    Builder declared

    08

    Trust & verification

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

    Verified Builder

    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.

    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 ā€” 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

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