AI Automation Pipeline
The Problem
Analyzing hundreds of unstructured customer support files manually created bottlenecks and high response latency.
The Challenge
Parsing messy text formats from various document templates while keeping vector search latency and LLM token usage costs low.
The Solution
Designed a pipeline to automate unstructured file analysis and route support tickets to agent queues using OpenAI GPT-4.
Technical Architecture
LangChain agents configured with RAG (Retrieval-Augmented Generation) pipeline over a Pinecone Vector Database. Document uploads are queued in a serverless AWS Lambda pipeline.
Key Results & ROI
85% faster ticket handling times.
Client Value & Business Impact
Drastically lowered support agent fatigue and guaranteed sub-minute answers for common tier-1 inquiries.
Lessons Learned
Prompt structuring using few-shot training examples proved more robust for classification tasks than attempting to fine-tune base models.