Reporting your board does not have to re-check by hand.
Most reporting problems are definition problems. We agree what revenue, churn, and pipeline actually mean, encode it once, and build the pipelines and dashboards on top of that single definition.
What we deliver
Pipelines into one place
Product, finance, CRM, and marketing data landed on a schedule, with failures that alert instead of silently going stale.
A metrics layer
One definition per metric, version-controlled and documented, so two dashboards cannot disagree about last month.
Dashboards for people who act
Built for the person who has to do something about the number, not for a wall screen nobody reads.
An ML-ready foundation
Clean, historised data is the prerequisite for forecasting or churn models. Building it once serves both reporting and AI.
How we work
Agree the questions
We start from the decisions you need to make, then work backwards to the data. Dashboards built the other way round go unused.
Model the data
Definitions written down and agreed by finance and operations before anything is built on top of them.
Build the pipelines
Incremental, tested, and monitored. Every table has an owner and a freshness expectation.
Ship and document
Dashboards plus a metrics dictionary, so a new starter can read what a number means without asking.
Tech stack
Common questions
Do we need a data warehouse?
Not always. If your data lives in two systems and the questions are simple, a warehouse is overhead. We will say so, and build the simpler thing.
How long before we see anything?
First useful dashboard in three to four weeks in most engagements, because we sequence by decision value rather than trying to model the whole estate first.
Can you pull data out of OPS360 or our CRM?
Yes. OPS360 exposes a REST API and webhooks, and most CRMs do too. Where a system has no API, we work from its exports on a schedule.