System Architecture
How search.click processes fragmented operational data into actionable intelligence.
Ingestion & Normalization Layer
Maps inconsistent IDs, currencies, timestamps, and business entities into a unified operational model.
Metrics Calculation Layer
Calculates MRR, CAC, LTV, churn risk, ROAS, conversion rates, runway, and operational SLAs in real-time.
AI Reasoning Engine
Uses structured prompts and business context to explain changes, detect anomalies, and recommend actions.
Command Center & Action Layer
Dashboard visualization, Search UI, Alerts, automated Slack messages, and API webhooks.
Why Normalization Matters
Fragmented operational data causes decision delay. A user in Stripe exists as `cus_123`, while in HubSpot they are `company_456`. Without a dedicated normalization layer that resolves entities across the stack, "AI" features are just blind text generators. By unifying schemas first, we give the AI Reasoning layer mathematically sound metrics to analyze.
The AI Reasoning Paradigm
We avoid generic chat interfaces. Instead, our AI layer uses highly structured, context-rich prompt templates (e.g., "Analyze CAC degradation using Meta Ads spend vs HubSpot closed-won data"). It returns deterministic JSON containing the cause, confidence score, and discrete actions, which the UI renders into safe, clickable workflows.