Muhammad Tuntas Hizbullah
VerifiedAI Automation Engineer · Workflow Automation
IndonesiaSenior
Measured business impact
Outcomes reported against approved AI implementations, presented for executive review.
- Hours Automated
- 50 hours
- Cost Savings
- USD 1,500
- Revenue Impact
- —
- AI Deployments
- 2
- Published Case Studies
- 2
Operational hours recovered through verified AI deployments.
Verified operating cost reductions delivered for client organisations.
Revenue created or protected through deployed AI solutions.
Implementations reviewed and approved against the Evidence Standard.
Documented implementations with published business outcomes.
- VerificationVerified
- SpecialisationWorkflow Automation
- AI Deployments2
- AI Case Studies2
- Industries Served5
- LocationIndonesia
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
Top business outcomes
Reported by the builder on the record that produced them, in the original reporting period. Nothing is annualised or converted.
- 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.
Business Outcome Highlights
Measured outcomes taken from published AI Case Studies, shown with the reporting period the builder stated. Each figure links to its case study.
- Hours Automated40 hours period not specifiedThe builder has not yet stated the reporting period for this figure, so it is shown exactly as reported and is not compared with other outcomes.Verified through published AI Case Study · BrightDent: live AI web-chat booking agent for a clinic
- Cost SavingsUSD 1,000 period not specifiedThe builder has not yet stated the reporting period for this figure, so it is shown exactly as reported and is not compared with other outcomes.Verified through published AI Case Study · BrightDent: live AI web-chat booking agent for a clinic
- Revenue ImpactUSD 1,500 period not specifiedThe builder has not yet stated the reporting period for this figure, so it is shown exactly as reported and is not compared with other outcomes.Verified through published AI Case Study · BrightDent: live AI web-chat booking agent for a clinic
Capabilities: declared and demonstrated
9 of 15 listed capabilities are supported by an approved deployment or published case study on this platform. The rest are Builder-declared and are not presented as verified.
Implementation capability
- Workflow Automation3
- AI Agent1
- AI Analytics1
- Internal Developer Tooling2
- RAG System1
- Reporting Automation1
Technologies
- Claude Code
- Hugging Face Inference
- Pinecone1
- n8n1
Industries
- FinTech
- E-Commerce
- Consumer Goods (CPG)
- Food & Beverage
- Marketing & Advertising2
AI Deployments (2)
Technical implementation records: what was built, the technologies used and the scope delivered. Each was reviewed before publication.
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.
By Muhammad Tuntas HizbullahMulti-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.
By Muhammad Tuntas HizbullahAI Case Studies (2)
Business stories: the problem, the approach and the measured result for the organisation. Each is published with its supporting evidence.
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.
By Muhammad Tuntas HizbullahScaling 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.
By Muhammad Tuntas HizbullahAbout
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
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