SCALE
2M+
Daily Requests
Conversational AI, Retail Support, & RAG Systems
SCALE
Daily Requests
UPTIME
System Availability
ACCURACY
With Multimodal Input
PIPELINE
Airflow Orchestrated
FALLBACK
Bedrock LLM Routing
Where customer volume breaks the human chain
Scaling support to millions of users often leads to fragmented context and inconsistent service quality. Human-only teams can no longer maintain accuracy or speed under the weight of repetitive inquiries.
The Intellema Design Challenge
Retail and service brands frequently struggle with high volumes of customer questions spanning pricing, locations, and orders while preserving accuracy and consistent tone. These surges create bottlenecks that overwhelm manual support chains, leading to delayed resolutions and a breakdown in user trust.
This project delivers an AI-powered RAG chatbot capable of handling over 2 million daily requests and interpreting informal, multilingual text. The architecture ensures dependable runtime behavior and high availability even during extreme peak traffic surges.
A customer-facing chatbot engineered to manage and scale beyond 2 million user requests daily.
High-availability architecture utilizing Groq LLMs alongside deterministic, rule-based paths for resilient query handling.
Advanced text cleaning and clarification threading for nuanced, multi-turn follow-ups and localized user intent.
A Retrieval-Augmented Generation system designed for contextually accurate responses rooted in curated internal data.
