Muhammad Tuntas Hizbullah | Verified AI Builder

About Muhammad

A decade running real businesses, now I build the systems that run them.

I ship production AI automation that takes real work off lean teams, so small teams run like big ones. Six systems are live in production right now, cutting manual work by 50 to 70%.

I spent 10+ years running the marketing, operations, and full P&L of real companies, so I know exactly which problems are worth automating before I build a single workflow. Then I ship the system that solves them, live, on n8n, Claude, RAG with Pinecone, Postgres, and Python.

What I build:

• Web-chat booking agents that capture bookings without a human in the loop

• AI hiring co-pilots that screen CVs and run candidate assessments end to end

• Internal operations and BI platforms that replace manual reporting

• Python ETL pipelines pulling live ad and account data daily across multiple clients

Shipped and running:

• Employee assessment and learning platform: a 250+ node n8n workflow with multi-agent Claude and RAG on Pinecone, live in production

• Volume Hire CV-screening co-pilot, live and screening real candidates

• BrightDent booking agent, live demo at brightdent-demo.vercel.app

• Boost Engine internal ops and BI, roughly 60% of manual workload removed

• Python ETL reporting across 7 client accounts daily, report time cut 50 to 70%

Selected results from the business side:

• Revenue up 40% year over year

• Member sign-ups up 251% at 79% lower cost per acquisition

• ROAS up to 20.95, organic traffic up 52%

The honest version: I am orchestration and automation first. I build with n8n, Claude, and Python (ETL), I ground agents with RAG, and I ship things that run live every day. I am currently deepening the engineering side through an AI Engineering program in 2026.

Stack: n8n, Python, Claude API, RAG, Pinecone, HuggingFace embeddings, PostgreSQL, Next.js, Vercel, webhooks and APIs.

Portfolio: muhammadtuntas.com

Professional profile

Location: Indonesia

Proven on the network

Implementation records and publicly available work, where published.

2 AI Deployments delivered, including 2 publicly identified implementations available to inspect.

Reported business outcomes

Hours Automated50 hours
period not specified
Automated 50 hours (period not specified)
Cost SavingsUSD 1,500
period not specified
Reduced operating costs by USD 1,500 (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.

What Muhammad builds

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

Specialisations

Workflow Automation

Builder-listed deployment types

AI Agent
AI Analytics
Internal Developer Tooling
RAG System
Reporting Automation
Workflow Automation

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Boost Engine: internal AI operations and BI platform

An internal AI operations and BI platform with role-based dashboards and automated KPI tracking that aggregates Meta, Google, and SEMrush data, replacing three separate tools and manual coordination at my agency.

50 hours automated · Currency not specified cost savings

Marketing & Advertising · AI Analytics · Reporting Automation · Workflow Automation

View Deployment

02 / AI DeploymentPublished record

Multi-agent AI employee assessment and development platform

A production AI platform that runs a 7-dimension employee assessment end to end and generates a personalized development plan for each person, zero-touch from a single shared link.

HR & Staffing · AI Agent · Internal Developer Tooling · RAG System · Workflow Automation

View Deployment

Implementation capabilities

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

Evidence-supported

Workflow Automation3 records
AI Agent1 record
AI Analytics1 record
Internal Developer Tooling2 records
RAG System1 record
Reporting Automation1 record

Builder declared

No additional declared items.

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

Pinecone1 record
n8n1 record

Builder declared

Claude Code
Hugging Face Inference

Industry experience

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

HR & Staffing

1 published Deployment

Marketing & Advertising

1 published Deployment

Consumer Goods (CPG)

Builder declared

E-Commerce

Builder declared

FinTech

Builder declared

Food & Beverage

Builder declared

Case Studies

Published narratives of implementation and results.

Healthcare Providers · Conversational AI

BrightDent: live AI web-chat booking agent for a clinic

A live web-chat AI booking agent for a clinic that books, reschedules, and cancels appointments end to end, grounded in the clinic's own data with RAG to prevent wrong answers.

Read case study

Marketing & Advertising · Document Processing · Internal Developer Tooling · Process Automation · Workflow Automation

Scaling agency account capacity with internal AI automation

I built internal Python ETL and n8n automation that took daily client reporting off the team, which cut report turnaround by 50 to 70 percent and let each account handler cover 6 to 8 clients instead of 3 to 4.

Read case study

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

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