Metrics defined once. Trusted everywhere.
Modelled, tested, versioned analytics with a BI layer your team self-serves. One definition of MRR, one definition of activation, one source of truth your CEO and CFO agree on.
The problem we solve
Most analytics fail not because data is missing but because nobody trusts it. Every team has a slightly different definition of the same metric. Dashboards contradict each other. Leadership meetings start with a reconciliation argument. We fix that by treating metrics as code — defined once, tested, versioned, reviewed and rolled out.
What we ship
- 01Semantic / metrics layer: dbt-metrics, Cube, MetricFlow, SDF
- 02Star-schema and dimensional modelling discipline
- 03BI tooling setup: Metabase, Lightdash, Hex, Looker, Mode
- 04Self-serve dashboards your team can extend
- 05KPI trees mapping business goals to underlying metrics
- 06Cohort analysis, funnel analysis, retention curves
- 07Embedded analytics inside your product
- 08Tooling for non-technical users to ask questions safely
- 09Data documentation and discoverability
What you receive
- A semantic layer with every key metric defined once
- Dashboards your team uses (not just glances at)
- Documentation written for the people who'll read it
- Training session for your analytics and ops teams
Stack we reach for
Ideal for
- → Companies where every dashboard tells a different story
- → Leadership teams trying to standardize KPI definitions across the org
- → Product teams wanting self-serve analytics for their PMs
- → Companies embedding analytics in their product for customers
How an engagement runs
- 01
KPI workshop
We sit with leadership to define the metrics that actually matter — and the definitions everyone signs.
- 02
Semantic layer
Metrics modelled in code, tested, versioned. One source of truth, reviewable like any other code.
- 03
BI rollout
Dashboards built on the semantic layer. Self-serve patterns set up. Training delivered.
- 04
Adoption
Pairing with your teams until self-serve is the default, not the exception.
How to engage
KPI Sprint
Workshop and written KPI tree with definitions every team can agree on.
Semantic Layer Build
Modelled metrics, BI rollout, training and adoption support.
Analytics Retainer
Continuous extension and care of the analytics stack as the business evolves.
Frequently asked.
01Which BI tool should we use?
Metabase or Lightdash for most companies — open, dbt-native, low ceremony. Hex when notebooks and ad-hoc work matter. Looker when you need governance. We'll match the tool to your team.
02Do we need a semantic layer?
If more than one team queries the data, yes. The cost of definitions diverging is higher than the cost of the layer.
Have a problem worth solving well?
Tell us the outcome you want. We'll tell you what it takes — honestly, within a week, in writing.
Start a conversation