From Data Chaos to a Single Dashboard — Building an AI Analytics Layer

The Multi-Tool Data Problem

By the time a startup reaches 20 people, their data is usually scattered across:

• Shopify or Stripe for revenue
• Google Ads and Meta for marketing spend
• HubSpot or Pipedrive for pipeline
• Intercom or Zendesk for support metrics
• Google Analytics for traffic
• Notion or Airtable for ops data

Nobody has a complete picture. The CEO asks "how are we doing?" and the honest answer is "I need 20 minutes to pull it together."

That's not a data problem. That's an architecture problem.

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What a Unified Analytics Layer Looks Like

The goal is a single dashboard — accessible by leadership at any time, always up to date — that shows every metric that matters across the business.

We build these in three layers:

Layer 1 — Data Collection

n8n workflows pull data from every source on a schedule — hourly for critical metrics, daily for everything else. Data lands in a central Postgres database or BigQuery instance.

No more manually downloading CSVs. No more waiting for someone to run the report.

Layer 2 — AI Synthesis

This is where it gets interesting. Beyond showing raw numbers, we add a Claude-powered synthesis layer that:

• Identifies anomalies automatically — "CAC spiked 40% this week, driven by Meta"
• Surfaces correlations — "Support volume rises 3 days after large marketing pushes"
• Generates a weekly narrative summary in plain English, delivered to Slack every Monday

Numbers tell you what. The AI layer helps you understand why.

Layer 3 — Visualisation

We use Metabase (self-hosted, free) or Retool for the dashboard UI. Clean, fast, accessible from any device. No Tableau licence required.

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What Gets Measured

Every dashboard is different, but the core metrics we almost always include:

Revenue layer: MRR, ARR, churn rate, new vs expansion vs contraction revenue

Acquisition layer: CAC by channel, ROAS, lead volume, conversion rates at each funnel stage

Retention layer: NPS, CSAT, churn cohorts, product usage signals

Operations layer: Support ticket volume and resolution time, fulfilment speed, error rates

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The AI Difference

The dashboards we built two years ago were static — they showed you numbers, and you had to draw your own conclusions.

The dashboards we build now have an AI layer that reads the data and writes the analysis. Leadership opens their Monday dashboard and the first thing they see isn't a chart — it's three sentences telling them exactly what changed, what caused it, and what to watch this week.

That's not a gimmick. For founders and executives who need to move fast, it's a genuine competitive edge.

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Want a single source of truth for your business metrics? Let's talk →