Updates, tutorials, and use cases
A full walkthrough of the 9 steps in Tablize's Deep Analysis mode. With concrete examples of what each step catches that a naive agent would miss.
Connect Shopify, type one sentence, get a working dashboard. Walkthrough of using Tablize to skip the dashboard-authoring step entirely.
Step-by-step: connect a read-only Postgres role to Tablize, ask your first question in plain English, see the generated SQL, and save the analysis as a reusable Report.
ChatGPT with a CSV upload is a real workflow. It also has five clear gaps that a Data Agent fills. Honest comparison from someone who uses both.
Stockouts, dead stock, replenishment math. Most DTC inventory problems are solvable with one good agent and a connection to Shopify. Here's the playbook.
Most cold-chain monitoring tools cost $200/month and need a developer. Here's how to set up a working MQTT temperature alert in Tablize in about 10 minutes.
Backtest exploration, factor research, regime detection — the daily work of a quant researcher is mostly asking the same SQL questions with different filters. Here's how to do it in English.
An honest comparison from someone who's used both. Metabase is the open-source dashboard standard. Tablize is a Data Agent. Here's the line between them.
AI is great at confident-sounding answers and bad at being right about numbers. Here's a 9-step protocol we built into Tablize's Deep Analysis mode to fix that.
A new category is forming between BI tools and AI chatbots. Here's what a Data Agent actually does — and why it doesn't try to be either.
Five product domains, a Python sandbox, a streaming agent runtime — all in a single binary that runs from one Docker image. Here's the reasoning, the constraints, and what we'd do differently.
Introducing Tablize — the platform where you say what you need, the agent does the work, and the results stay forever as reusable assets.