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§ Definite in action · Vol. 1
From raw data to AI-assisted analytics in three minutes.
A short walkthrough of how Definite connects your sources, loads them into a lakehouse, and lets Fi answer questions in plain English.
Hey, I'm Mike, founder of Definite. Contact me for a personalized walkthrough.
§ Chapters
5 sections · 8:10 total
01
0:00
Connect every source
Postgres, HubSpot, Stripe, QuickBooks — built in. For internal APIs and niche SaaS, describe it in English and the agent writes the pipeline. No Fivetran bill, no Python.
→500+ built-in sources
→Custom APIs from a description
→No ETL tool to license
02
2:12
Data storage and modeling
Synced tables land in a managed lakehouse on your schedule. Column profiles, row-level lineage, and a semantic model the agent maintains as your schema changes.
→Managed lakehouse + storage
→Lineage back to source
→Agent-maintained semantic layer
03
2:30
Ask Fi in plain English
Natural-language Q&A across every connected source. The same agent runs on Definite's MCP, so you can drive your warehouse from Claude Code, Cursor, or ChatGPT.
→Plain-English questions
→Auto-discovers the right tables
→Works in Claude Code, Cursor, ChatGPT
04
4:30
Ship dashboards in one prompt
Describe the app you want and the agent builds a multi-tab dashboard with drill-downs and chart components. Hover any number and ask "how is this calculated?" — Fi traces it back to source.
→Multi-tab apps from a prompt
→Customizable drill-downs
→Contextual follow-ups on any chart
05
5:50
Automate the long tail
Scheduled Python pipelines run on Definite. Slack alerts on cron, agent decision steps mid-run, and a built-in KV store so pipelines remember between runs.
→Scheduled pipelines + Slack alerts
→Agent steps inside the workflow
→KV store for pipeline memory
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