We are a data analytics consultancy that works on the business side, not only the technology side. From tidying up metric definitions and building reliable pipelines to forecasting models that operations teams use.
140+
Analytics projects
since 2022, across 9 industries
9
Industries understood
retail, FMCG, finance, public
4 years
Years in practice
since 2022 in Jakarta
96%
Dashboard adoption
weekly active users
Most organisations do not lack data — they lack definitions, ownership, and a workflow that connects numbers to action.
Sales reports differ between divisions because metric definitions were never agreed on and tested.
Up to 60% of an analyst's hours go into pulling, cleaning, and manually reconciling data.
The project finishes, there is a handover ceremony, then the dashboard gathers dust because it never entered the daily workflow.
You do not have to take all of them. Most projects start with an assessment and continue into the most urgent area.
We assess your organisation's data maturity and lay out a realistic 12-month roadmap, not technology promises.
Dashboards that operations teams open every morning, not just during the monthly review.
Pipelines that are idempotent, monitored, and quietly run in the background without manual babysitting.
Answering cause-and-effect questions: who will churn, which products are thinning out, how much stock next month.
One definition for one metric. No more three different numbers for "sales" across three divisions.
From notebook to production: models that are monitored, retrained, and have a clear owner.
No surprises midway: one workflow, one stream of information, and results you can see every two weeks.
We sit down with the owners of the business problem, not just the IT team. The goal is one thing: define the question worth answering with data and estimate its value.
We audit data sources, quality, access, and infrastructure readiness. The output is an honest list of gaps, including what cannot be done yet.
Architecture, data models, metric definitions, and user experience design come together into one blueprint we approve together.
Iterative work in two-week sprints. Every iteration produces value that real users can see and test.
Documentation, training, and coaching until your internal team can run and extend the solution on its own.
We work with the modern data stack the industry commonly uses, but we never force a new tool when the existing one is already enough.
Columnar warehouse, dimensional modelling, and versioned transformations with automated testing.
A single metric layer that supplies dashboards, exports, and APIs with the same definitions.
Statistical and machine learning models with drift monitoring and a retraining schedule.
Data quality tests, lineage, access controls, and auditable documentation.
Not answered yet? Send your question, we reply within one business day.
An assessment and the first dashboard usually take 6-10 weeks. Data engineering or machine learning projects typically run 3-6 months and are split into two-week iterations.
No. About a third of our projects start from spreadsheets and operational databases. We design a phased path so infrastructure investment happens only when it is genuinely needed.
Common options: fixed scope for an assessment, a monthly retainer for ongoing support, or a dedicated squad for continuous development. All of them are transparent about hours and outcomes.
Absolutely. We work alongside your team in one workflow, with code reviews and weekly knowledge-transfer sessions. Without that, the solution will not last.
We sign an NDA, use isolated working environments, and apply data minimisation. For personal data, our approach follows Indonesia's PDP Law and GDPR practices.
A free 30-minute session to map the problem, assess data readiness, and estimate the impact. You will get a written summary, whatever you decide next.
