I sit between Account Managers and Project Engineers — converting unstructured client business requirements into structured AI logic, so AI agents are configured correctly before they ever reach the client's hands.
📍 Jakarta, Indonesia
📅 May 2026 – Present
🏢 SaaS · Agentic AI · CX Automation
🎯 Scope: Phase 1–4 of 6-phase pipeline
30
Active B2B Projects Managed Simultaneously
24+
B2B Client Accounts Onboarded
~95%
Onboarding Completion Rate
5–14
Working Days Time-to-Value (TTV)
My Core Workflow
4-Phase AI Implementation Pipeline
What I own end-to-end — from the moment AM closes a deal, to a fully configured AI agent ready for UAT
Entry Point
Client closed & Kickoff completed
→
Phase 01
Post-Kickoff Alignment
Review AI kickoff transcript — map business flow, industry constraints & automation scope
Cross-check with AM/PE on remaining data gaps
Analyze client "character type" to tailor call vs chat approach
↓ OutputValidated requirement checklist + communication strategy
→
Phase 02
Requirement Gathering & SLA Management
Manage project tracker dashboard for data-gathering status
SLA-based structured comms templates to chase client docs
Proactive escalation (H-2 alignment call) to prevent SLA slippage
↓ OutputComplete client data package, collected on-SLA
→
Phase 03
KB Structuring & Guardrail Definition
Simulate end-to-end AI–customer conversations to find gaps
Define AI guardrails so data is machine-readable & hallucination-proof
↓ OutputFinalized Knowledge Base with validated logic, e.g. dental case: schedule params, service duration,
"1 Calendar = 1 Branch" double-booking prevention
→
Phase 04
Technical Coordination (Business → Engineering)
Handover finalized KB + updated timeline to Project Engineer
Translate API/ERP integration (Accurate, Jubelio, Shopify) into client-friendly instructions
Monitor progress with PE to clear operational blockers
Deep-dive breakdowns of how I executed the 4-phase pipeline on real client projects
🦷
Healthcare · Dental Clinic
Behind the Scene AI Implementation SaaS: Bridging Business Needs With Techmical Logic
A full phase-by-phase breakdown of how an AI appointment system was built for a dental clinic — from post-kickoff alignment to Go-Live, including the Google Calendar guardrail architecture that prevents double-booking.
Real problems encountered during implementation — and how I resolved them systematically
Challenge 01
Data Ambiguity After Kickoff
⚠ Challenge
Ambiguity during requirement gathering — risk of re-requesting data that clients already shared with AM or PE at kickoff (via private chat, links, or files), causing unnecessary friction and eroding client trust.
✓ Solution Applied
Always review AI meeting transcriptions from kickoff sessions to map exactly what was discussed and what data was already shared — then align with AM/PE before requesting anything from the client again.
Challenge 02
Unclear AI Reference Chat Scripts
⚠ Challenge
Clients providing vague roleplay scripts instead of absolute logic, which causes the possibility of AI agents hallucinating prices or policies during live conversations.
✓ Solution Applied
Encourage clients to fill out a structured "Trigger & Reaction Matrix" — If condition A is met → Give Answer B → Else, Answer C — converting narrative scripts into deterministic logic the AI can execute precisely.
Challenge 03
Client Slow Response or No Response After Follow-Up
⚠ Challenge
Clients stalling the timeline by taking too long to provide required data, causing the data gathering phase to exceed its scheduled window and delaying downstream configuration.
✓ Solution Applied
Apply Anchoring (provide prefilled examples so clients only fill in the blanks), Feature Selling (explain why the data directly benefits their business), and A/B Closed Questions to reduce cognitive load and accelerate decision-making.