Querlo AI Platform

Building a Universal AI Interface

Global enterprises needed a single platform capable of deploying AI-powered digital twins — branded conversational agents that could operate across languages, channels, and use cases simultaneously.

The core technical challenge was LLM orchestration at scale: routing conversations to the right model (GPT-4, Claude, Mistral) based on context, cost, and latency constraints, while maintaining coherent brand personas across every interaction.

Compliance requirements across the EU, US, and APAC markets added another layer of complexity, demanding granular data residency controls and audit logging built directly into the infrastructure.

A Modular, Model-Agnostic Architecture

Digital Design Firenze designed and engineered a React-based frontend with a real-time analytics dashboard giving enterprise clients live visibility into conversation flows, sentiment trends, and conversion metrics.

The backend introduced a proprietary LLM router that dynamically selects and chains AI models based on task type, reducing API costs by 38% while improving response quality. A vector database layer powers RAG (Retrieval-Augmented Generation) for knowledge-grounded answers.

Each digital twin is configured through a no-code studio interface, allowing marketing and operations teams to deploy and iterate without engineering involvement — drastically cutting time-to-launch from weeks to hours.

Tools & Technologies

Measured Outcomes

• Deployed in 40+ enterprise organizations across 3 continents
• 12-language support with real-time translation and locale-aware UX
• 38% reduction in AI API costs through intelligent model routing
• 99.9% uptime SLA maintained over 24 months of production operation
• Average 3× increase in user engagement vs. static FAQ systems