AI CMS.
An automated support ecosystem designed to transform unstructured customer intent into structured ticket intelligence. Engineered with an asynchronous pipeline to ensure human-in-the-loop accuracy at scale.
The Bottleneck
Support teams are often overwhelmed by repetitive, unstructured queries across fragmented channels. This lack of structure leads to latency in response and inconsistent service quality.
The Logic
I developed a pipeline-driven architecture. By decoupling the AI classification layer from the core dashboard logic, the system ensures that every message is analyzed for sentiment and intent before reaching an agent.
Automation Pipeline
Unstructured Input
Intent & Sentiment
AI Response Gen
Human Verification
Dashboard Architecture
Technical Stack
Engineering Constraints
- /01Processed raw, unstructured text through multiple NLP passes to ensure high classification confidence.
- /02Implemented a mandatory Human-Review gate to maintain brand voice and prevent AI hallucinations.
- /03Architected with Redis-backed Laravel Queues for seamless handling of spikes in support traffic.
- /04Role-based access control (RBAC) designed for separate Admin oversight and Agent workflows.
Performance Outcome
"A workflow that reduced manual overhead by 40%, ensuring that agents focus only on high-value human interaction while AI handles the structural heavy lifting."