Hands-on insights from projects we actually run: data analytics, conversational analytics, AI workflow optimisation, ads agents, and MCP in marketing.
LLM 0.32 gave Claude server-side WebSearch, WebFetch, CodeExecution and MCP. Less glue to maintain, and the same old question: is the data underneath any good?
Andrii Krutko··9 min
Waiting on engineering to change a schema is why analytics projects stall. Migrations put the shape of your GA4 data back in the analyst’s hands.
Andrii Krutko··7 min
There is no best AI model — only the best one per task. A routing matrix for marketing: generation, analysis, automation, and what each should cost.
Andrii Krutko··7 min
Your GA4 BigQuery export is a raw, event-level data model. Feature engineering — now doable with an LLM — turns it into signals your reports hide.
Andrii Krutko··7 min
OpenAI split GPT-5.6 into three tiers. Route models by job — cheap tiers for volume, reasoning for money decisions — or your cost per campaign multiplies.
Andrii Krutko··6 min
Everyone knows what they spent on AI tools. Almost nobody knows what those tools earned. A GA4 + BigQuery framework to close the gap.
Andrii Krutko··9 min
Real examples of AI agents running Ads reports, warehouse queries, and tag management through MCP — plus what we learned shipping our own tracking stack this way.
Andrii Krutko··6 min// first articles shipping soon
We're writing up what we've learned running analytics and AI workflows in production.