The AI Data & Analytics Stack

A.R.C. Freshness Analysis

Tool Stack

1. 1
   Audit Your Data Sources

   List every system generating metrics you care about: product database, payment processor, marketing platforms, support tools. Categorize by type (product usage, revenue, acquisition, retention). This audit determines which tools you need and in what order.
2. 2
   Centralize KPIs in Databox

   Connect your top 3–5 data sources to Databox using its native connectors. Build a single dashboard with your core KPI set: MRR, DAU, conversion rate, churn. Set it as your team's default morning view.
3. 3
   Add AI-Native Analytics with Dex

   Layer Dex on top of your KPI data for AI-assisted analysis. Use Dex to surface anomalies, cross-reference pipeline data, and generate natural-language summaries of what's changing and why.
4. 4
   Automate Alerts and Responses with Tines

   Use Tines to build no-code workflows that trigger when your KPIs breach thresholds: MRR drops 10% → Slack alert. New enterprise signup → auto-enrich in CRM. Churn spike → create support ticket.
5. 5
   Run A.R.C. Audit on Your Analytics Stack

   Full Stack Integrity Audit on your data and analytics layer. Architecture (source coverage, single source of truth vs fragmentation), Reliability (connector uptime, alert false-positive rate), Context (whether your KPI set maps to your current business model). Upgrade to unlock the full diagnostic.