AI-Assisted Decision Workflow for Reliable Operations
crystal zhu
AI-Assisted Decision Workflow for Reliable Operations
🔹 Overview
I designed a backend workflow demonstrating how AI can assist operational decisions while maintaining reliability, auditability, and deterministic control.
Instead of letting AI make final decisions, the system extracts signals and combines them with rule-based scoring and workflow routing.
🔹 What It Demonstrates
✔ AI signal extraction (intent, sentiment, urgency)
✔ rule-based risk scoring with transparent breakdown
✔ workflow routing (auto approve / manual review / escalation)
✔ idempotent processing to prevent duplicate actions
✔ audit-friendly decision trace
🔹 Why This Matters
Many real-world systems require:
• compliance & auditability
• reliable integration with external systems
• explainable decisions
• safe workflow escalation
This architecture reflects how AI is safely adopted in production environments.
🔹 Example Applications
• payment dispute & refund workflows
• fraud & risk screening
• customer support automation
• operational approval workflows
• fintech decision pipelines
🔹 Tech Stack
Java • Spring Boot • LLM Integration • Ollama • REST APIs • Workflow Automation
🔹 Live Demo / Code
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Posted Mar 2, 2026
AI-assisted decision workflow integrating AI signals, risk scoring, workflow routing, idempotent processing and audit traceability for reliable operations.
