I help small data teams build a semantic layer and an AI agent on top of it in eight weeks, so leadership can ask questions in Slack and get numbers everyone agrees on.
metrics: - name: net_revenue label: Net revenue description: > Gross bookings minus refunds and credits. Excludes tax. type: simple type_params: measure: net_amount filter: | {{ Dimension('order__is_test') }} = false meta: owner: finance # agreed in week 1
What was net revenue in September?
$1.42M, up 6% on August. I used net_revenue (owner: Finance), which leaves out tax and test orders.
And churn by region?
Nobody has defined churn yet, so I won't guess. I've flagged it for the data team.
Example data. The second answer is the point: the agent can say "I don't know." It never makes a number up.
Every build follows the same plan, and your team works alongside me from the first week.
On the questions from week 1, the agent gets at least 90% right and is never confidently wrong. If it can't answer from a definition you agreed on, it says so.
Fixed price and fixed scope, quoted after the free review. Your team builds it with me and owns all of it at the end.
If three of these sound like a normal Tuesday, let's talk.
Your team could build this alone. With me, you build it in eight weeks, you keep the skills, and you get your time back for the work only your team can do.
Works on dbt with Snowflake, BigQuery, Databricks, Redshift, Postgres or Trino.
We spend 90 minutes on your dbt project and I tell you what stands between you and answers you can trust. You keep the diagnosis whether we work together or not.
Reviews are open now. The next build starts summer 2027.
Email me about a review