Case study · Early-stage AI startup, built solo
Product analytics for an AI SaaS product
Set up the data stack from scratch and gave the product team self-serve reporting on signups and usage of every AI feature.
The problem
An early-stage AI product with no data stack: the team couldn't see how many people signed up, or which AI features they actually used.
What I built
I set up the analytics platform from scratch, on my own: ingestion from the app and its services, a warehouse, dbt models and self-serve dashboards for the product team. Signups and usage of each AI feature were tracked and reported.
- Ingest dlt, Estuary
- Warehouse MotherDuck
- Transform dbt
- Orchestrate Kestra
- Self-serve Metabase
On the application side I added OpenTelemetry monitoring, CI and Docker to the SaaS app, and mentored 15+ engineers.
Stack
- dlt
- Estuary
- MotherDuck
- dbt
- Kestra
- Metabase
- OpenTelemetry