Business Intelligence System
Business Analytics
Daily AI business report with actionable insights, running on local models.
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The Problem
Business metrics were scattered across multiple platforms analytics dashboards, server logs, social media, marketing tools, and spreadsheets. Getting a unified view required logging into several tools every morning and manually synthesizing the data.
The Approach
Rather than building a dashboard (which still requires someone to look at it), the system pushes a daily report that analyzes everything and tells you what matters. The AI does not just summarize data. It identifies trends and recommends actions.
Intentionally Left Out
Real-time alerting was deferred. The daily cadence covers 90% of the need. Critical alerts (site down, payment failures) are handled by existing monitoring tools.
The Solution
An automated pipeline that collects business data from multiple sources, stores it for trend analysis, and uses AI to generate a morning intelligence report with prioritized action items.
Technical Highlights
Local Ollama model analyzes collected data and generates natural language insights
PostgreSQL stores historical metrics enabling trend detection over time
Modular collector architecture makes adding new data sources straightforward
The Results
The system replaced a 45 minute daily manual process with an automated report that delivers better insights at zero marginal cost.
Lessons & Takeaways
Push beats pull for busy operators
A dashboard requires discipline to check. A morning email with the top 3 things to focus on actually gets read and acted on.
Historical context makes AI useful
An AI looking at today's numbers can describe them. An AI comparing today to the last 30 days can spot trends. The historical database was the differentiator.
Start with fewer data sources
We launched with 3 data sources and added 2 more after validating the report format. Trying to integrate everything at once would have delayed delivery significantly.