Aura Conversational AI
Optimizing customer support by deploying smart semantic AI agents and document search systems.
Aura AI is a conversational customer intelligence system designed to automate customer support pipelines. By parsing hundreds of corporate knowledge PDFs, it answers customer questions with precise, verified facts using Retrieval-Augmented Generation (RAG) and keeps the user's tone matched to corporate standards. Our artificial intelligence team trained local sentence embedding models, designed high-speed vector database index lookups, and built visual conversation monitoring dashboards, reducing customer wait times from hours to fractions of a second.
Project Details
Aura Agent AI
ONLINELive Prototype Simulator
To demonstrate key platform mechanics without connecting to sensitive private production data, we engineered this sandboxed interactive widget. Feel free to interact with it and test the UI responsiveness!
Real-time State Logging
Check interactive metrics, calculate transactional margins, verify forms, or dynamically test style modifications in real time.
Isolated Staging Logic
Emulates custom backend API triggers, WebGL animation frames, RAG vector context fetches, and micro-ledger databases.
Key Deliverables & Value Provided
How RionexTech solved challenges and delivered outstanding technical features.
Semantic Document Search
Parsed massive PDF databases, generated dense chunk embeddings, and indexed vectors for swift contextual search.
Tone Adjustment Filters
Built custom prompt pipelines that check AI text against strict language standards to prevent hallucination errors.
Live Chat Hand-off Hooks
Designed socket bridges that pass conversations to human service agents if client confidence falls below a preset threshold.
Token Usage Dashboards
Wrote real-time usage aggregations to track prompt costs and billing values across department heads.
Project Execution Roadmap
A chronological walkthrough of the phases undertaken to guide the project to success.
Information Review
Sorting through company support documents and identifying main query classes.
Vector Pipeline Setup
Configuring document chunks and testing vector databases.
Agent Logic Sprints
Programming orchestrator chains and checking context retrieval quality.
Interface Design
Building chat widgets and management dashboard grids in React.
Production Hosting
Deploying Python microservices in cloud servers with GPU optimizations.
Information Review
Sorting through company support documents and identifying main query classes.
Vector Pipeline Setup
Configuring document chunks and testing vector databases.
Agent Logic Sprints
Programming orchestrator chains and checking context retrieval quality.
Interface Design
Building chat widgets and management dashboard grids in React.
Production Hosting
Deploying Python microservices in cloud servers with GPU optimizations.
Technology Architecture
The languages, tools, and infrastructure services chosen to execute this deployment.
Core Development
Agent Orchestration
Language Models
Vector Databases
API Framework
Dashboard UI
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