The 5-Layer System Architecture
The system is a cyclical ecosystem, not just a linear pipeline, ensuring data flows from ingestion to analysis and back to improve the model over time.
1. Ingestion
Collects emails & call recordings.
2. Transcription & PII Redaction
Converts audio to text & secures data.
3. Core AI Layer
Summarizes & classifies complaints.
4. Analysis & Action
Finds root causes & triggers alerts.
5. Human-in-the-Loop
Validates AI & provides feedback.
Technology Showdown
Speech-to-Text (STT) API Friendliness
A smooth developer experience is key for fast integration. This chart compares leading STT providers on their ease of use, based on technical reviews.
LLM Performance vs. Cost Trade-Offs
Choosing an LLM involves balancing accuracy, cost, and speed. This chart visualizes the zero-shot classification performance of top models.
The Implementation Roadmap: A Phased Approach
Phase 1: API-First Deployment
Launch quickly using a leading commercial LLM API. This delivers immediate value and begins the crucial process of data collection via the Human-in-the-Loop workflow.
- ✓ Fast Time-to-Market
- ✓ Low Initial Cost
- ✓ No Training Data Required
Phase 2: Data-Driven Fine-Tuning
Use the human-validated data from Phase 1 to train a smaller, specialized model. This boosts accuracy on niche terms and significantly reduces long-term costs at scale.
- ✓ Higher Accuracy
- ✓ Lower Cost at Scale
- ✓ Creates a Strategic Asset
The Engine's Output: Structured JSON
To be a reliable engineering component, the LLM must produce a predictable, structured JSON output. This single API call efficiently delivers all necessary data, eliminating fragile parsing and enabling seamless integration with other enterprise systems.
{
"summary": "Concise summary of issue...",
"is_complaint": true,
"sentiment": "negative",
"primary_topic": "Billing",
"secondary_topics": ["Late Fee"],
"suggested_next_action": "Route to billing dept."
}
The Critical Feedback Loop
The Human-in-the-Loop (HITL) process is the lynchpin. Every human correction is high-quality training data, feeding back into the system to make it smarter and more accurate over time. This transforms the system into a self-improving asset.