Michael Carter
Founder & CEO, Chartcastr
Building Chartcastr — an AI analyst that lives in Slack instead of behind a dashboard.
Michael founded Chartcastr after a decade of watching teams build dashboards nobody opens. He writes about push analytics, the economics of reporting, and why AI changes the shape of internal communication.
Writes about
- push analytics
- Slack-first reporting
- AI for business intelligence
- product-led growth
- SaaS metrics
Elsewhere
Posts by Michael Carter
Managed vs Branded WhatsApp Delivery: Which Should You Use?
Chartcastr now sends charts via WhatsApp two ways: from our shared business number (Managed, one-click) or from your own (Branded, starting at $85/mo per account, lead-gen onboarding). Here is when to pick each.
What Cadence Should You Use for Google Sheets Reports in Slack?
Daily, weekly, or monthly? How to choose the right schedule for a Google Sheets report in Slack so the channel becomes a habit instead of noise — a practical cadence playbook.
Context documents are the most underrated feature in AI analytics
You spent three hours writing a quarterly planning doc in Notion. Your AI analytics tool does not know it exists. Link one document and the output transforms.
Cross-source context: the semantic layer feature no one talks about
When your Shopify revenue dips, the AI searches your Notion workspace for the campaign brief that explains it. That is cross-source context, and no dashboard does it.
dbt semantic layer vs. the AI interpretation layer: solving different problems
dbt translates business terms into SQL. An AI interpretation layer translates business meaning into analysis. Both are valuable — they solve different problems at different points in the stack.
Institutional memory in analytics: when your AI remembers what the team discussed
Your analyst left and the context for why revenue dips every second Tuesday left with them. Institutional memory in analytics is a new capability — and most tools do not have it.
The metric intelligence gap: why your AI analytics tool just narrates charts
Most AI analytics tools fail because the AI has no metric intelligence. It does not know that rising AOV can mask declining order volume, or that a 2.5% conversion rate is average for e-commerce.
Semantic layer, metrics layer, knowledge layer, context layer: what do they all mean?
Four terms, overlapping definitions, different communities. A clear map of what each term means, where it comes from, and which one you actually need.
A semantic layer for the 90% who do not have a data warehouse
The semantic layer conversation has been captured by warehouse-adjacent tools. Most business teams will never build a warehouse. They still need their AI to understand what the numbers mean.
The semantic layer you already have (and the one you are missing)
Every team has a semantic layer. It lives in people heads, Slack threads, and the meeting notes your PM took on Tuesday. The problem is your AI cannot access it.
What is a semantic layer, and why does your AI analytics need one?
The semantic layer concept started in data engineering as a SQL abstraction. For AI-powered reporting, the version that matters is different — it is the layer that tells the AI what your numbers mean.
Reading the org through its data: how cross-tool correlation surfaces problems before anyone notices
The most important business signals never live in one tool. Four anonymized case studies of cross-tool patterns — ticket volume as a churn signal, ad spend as a support driver, time-to-first-pulse as an activation signal — and the framework for finding your own.
We tested 12 AI Slack analytics bots side-by-side on eight real questions
Most "AI for Slack" tools claim to answer business questions. We bought them, connected the same dataset to each, and ran eight realistic prompts. Three handled most prompts well, two answered confidently with wrong numbers, and five mostly failed.
The anatomy of a daily Slack update people actually read (with three annotated examples)
Most daily metric updates get muted within a week. The ones that survive share six traits. Three real (anonymized) examples annotated line by line, and a template you can copy.
Why most AI-generated insights are useless, and the four patterns that aren't
Most "AI insights" are commodity rephrasing dressed up as analysis. After shipping AI summaries on tens of thousands of pulses, four patterns consistently deliver value. Four don't.
The Push Analytics Manifesto: why dashboards are losing the next decade of work
Dashboards are a pull model in a world that has moved to push. The case for replacing the dashboard ritual with scheduled, AI-narrated deliveries into the tools people already use.
Slack vs. Dashboards: Why the Best Data Teams Push, Not Pull
Dashboards require people to go look at data. Slack delivers it where they already are. Why push analytics is winning, and what it means for how teams make decisions.
10 Things That Make an AI Slack App Great
What separates an AI Slack app people actually use from one they uninstall in a week. A framework for evaluating AI bots, with specific examples of what good looks like.
