AI Data & Analytics
Sits over Postgres + BigQuery + your data warehouse
Headline outcome
Plain-English BI for ops teams; no data team required
What the operation looked like before.
A scaling ops team had outgrown Google Sheets but couldn't justify a full data team yet. Reports lived in 14 different spreadsheets. Anyone who needed a number bothered the COO. Half the dashboards quietly broke when source schemas changed.
The bottleneck
Ops decisions were running 3–5 days behind the data because the bottleneck was always "get the right number out of the right sheet."
How the system is wired.
Connectors stream Postgres, BigQuery, Google Sheets, and Airtable into a unified semantic layer. Claude translates plain-English questions into SQL against that layer, with the SQL shown to the user before it runs. Dashboards auto-generate from frequently-asked queries. Schema drift gets flagged daily.
Where humans approve, review, decide.
Every consequential decision routes through a person on your team. Speed without abandoning judgement.
Sample approval surface
Reviews AI-generated SQL before it runs against production
Flagged- Decision frequency: Per ad-hoc query
- Routes to the operator daily review queue
Analyst
| Role | Decision | Frequency |
|---|---|---|
| Analyst | Reviews AI-generated SQL before it runs against production | Per ad-hoc query |
| Analyst | Approves a query promotion from ad-hoc to scheduled dashboard | Weekly |
| Data steward | Confirms schema-drift fixes before they merge | As surfaced |
What changed for the operation.
3
Outcomes the operating team validated. Numbers below are anonymized to client policy.
Time-to-answer for ops questions
minutes
from 3–5 days
Broken-dashboard incidents
↓ 80%
Reports auto-generated
40+
from 0
We share the underlying breakdown in a discovery call under NDA.
What runs underneath.
Talk to us
Have an operation shaped like this one?
We'll start with a 2-week Discovery, fixed-fee, against your real systems and operating people. If we're not a fit we'll say so on the call.