At the core of the platform is an OpenAI GPT-powered analysis engine that transforms raw cloud billing data into actionable financial intelligence. What previously required days of manual spreadsheet analysis now happens in minutes, with recommendations that finance and engineering teams can act on immediately.
Savings Analysis
The AI engine evaluates Reserved Instance utilization, Savings Plan coverage gaps, and rightsizing opportunities across all connected accounts. It identifies underused commitments, flags expiring reservations, and recommends optimal purchasing strategies based on actual usage patterns rather than static thresholds.
Cost Analysis
Spending is broken down by account, service, and category with AI-generated narratives that explain cost movements in plain language. Instead of teams manually comparing month-over-month figures, the system surfaces what changed, why it changed, and whether it needs attention.
Anomaly Detection
The platform continuously monitors spending patterns and flags unusual cost spikes or drops before they become budget surprises. It learns from historical data to distinguish between expected seasonal changes and genuine anomalies that require investigation.
Usage Optimization
Provider-specific optimization recommendations are generated for both AWS and Azure, covering compute rightsizing, storage tier adjustments, and idle resource cleanup. Each recommendation includes estimated savings and implementation effort, helping teams prioritize high-impact changes.
Custom Prompt Engineering
We built a custom prompt engineering layer that translates cloud billing structures into context-rich queries for the AI model. This ensures recommendations account for the specific nuances of each provider's pricing models, discount programs, and commitment types rather than producing generic suggestions.