System Architecture
The system is designed as a cyclical ecosystem, not just a linear pipeline. It ensures data is ingested, processed, analyzed, and acted upon in a structured manner, with feedback loops that enable the system to learn and improve over time. The initial deployment will start with clean text, simplifying the pipeline to three core layers.
1. Core AI Layer
Ingests clean text. Performs summarization and classification via a single API call, outputting structured JSON.
2. Analysis & Action Layer
Feeds JSON output to analytics dashboards for trend monitoring and triggers automated workflows in other enterprise systems.
3. Human-in-the-Loop (HITL)
Allows human agents to validate AI outputs, ensuring quality and systematically collecting labeled data for future model fine-tuning.
Technology Deep Dive
This section provides a comparative analysis of the key technologies powering the system. The charts below visualize data from the technical report to help illustrate the trade-offs between different API providers.
Speech-to-Text (STT) API Comparison
While the initial rollout starts with text, the full architecture requires STT. This chart evaluates leading providers on Developer Experience, a key factor for rapid integration.
LLM Zero-Shot Classification Performance
This chart visualizes the trade-offs between accuracy, cost, and speed for leading LLMs, guiding the selection for the initial API-based deployment.
Phased Product Rollout Strategy
The system will be deployed incrementally across the 50-product portfolio to manage risk, validate performance, and scale effectively. This approach prioritizes rapid learning and iterative development.
Phase 1: Pilot
Deploy for **1 product**. Focus on mastering prompt engineering and validating the business case.
Phase 2: Expansion
Expand to **4 more products**. Focus on configuration, generalization, and aggregating data.
Phase 3: Full Scale
Roll out to the **remaining 45 products**. Focus on cost optimization and scalability, likely via a fine-tuned model.
Operational Excellence
The long-term value of the system comes from transforming the aggregated data into actionable business intelligence and ensuring the system operates in a robust and ethical manner.
Root Cause & Trend Analysis
The structured JSON output from the LLM is a strategic asset. When aggregated, it allows for precise quantification of complaint drivers, enabling the business to move from reactive handling to proactive issue prevention. Time-series analysis can also act as an early warning system for emerging problems.
Ethical AI & Continuous Improvement
A commitment to fairness is critical. The system must be monitored for algorithmic bias. The HITL workflow is non-negotiable, ensuring accuracy, handling ambiguity, and providing the high-quality labeled data needed to periodically retrain and improve the model, creating a self-improving asset.