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Chartcastr Engineering
Engineering team
Engineers writing about the integrations, AI patterns, and infrastructure that power Chartcastr.
The Chartcastr engineering team writes deep-dives on the systems that move data from sources to Slack — auth flows, AI insight generation, MCP servers, and the rendering pipeline that turns rows into charts.
Writes about
- MCP (Model Context Protocol)
- OAuth integrations
- AI insights
- chart rendering
- BigQuery
- webhook architecture
Elsewhere
Posts by Chartcastr Engineering
How domain expertise changes AI analysis: Shopify vs. HubSpot vs. Google Search Console
The same AI model produces fundamentally different output when it has domain-specific knowledge. Here is what that looks like across three real data sources.
MCP for analysts: connecting Chartcastr to Claude Code, Cursor, and ChatGPT
A practical guide to using the Model Context Protocol for ad-hoc analytics — what MCP is, how the Chartcastr MCP server works, three setup walkthroughs, and five example prompts that actually work.
