Eazli

Challenge

Eazli's early prototype could handle only simple, single-service requests. It could not break complex needs into tasks, activate the right specialists, or give management visibility into service quality and AI costs.

Solution

We designed a central AI concierge that interprets each customer's needs, plans and delegates tasks to specialist agents through standardised interfaces, and shares context through RAG, with built-in monitoring of performance, quality, and per-query cost.

Results

• Enabled new services to launch without rebuilding the core platform
• Cut the time and cost of integrating new specialist capabilities
• Opened new cross-selling opportunities across verticals
• Gave management real-time control over quality and AI spend

Technology

Python · LLM Orchestration · RAG · AWS