Dashboards people actually open, built from a warehouse that doesn't lie.
Data-warehouse and BI programmes that take raw operational data to decisions leadership trusts — from enterprise-wide KPI reporting to a single dashboard your ops team checks every morning. Built for growing businesses and enterprise reporting standards in Nepal.
Six capabilities, one accountable team
Warehouse design
Dimensional models built around the decisions your leadership actually makes.
Dashboard engineering
Looker, Power BI, or Metabase dashboards designed for the person who opens them daily.
KPI framework design
Metrics defined once, in one place, so two teams stop arguing about whose number is right.
Self-serve analytics
Governed semantic layers that let analysts explore without breaking the warehouse.
Automated reporting
Scheduled reports delivered where decisions happen — Slack, email, or the boardroom deck.
Data literacy enablement
Training so your team can extend the dashboards without waiting on us.
Four phases, each with a named deliverable
Define
KPI framework agreed with stakeholders before a single dashboard is built.
Build
Warehouse models and dashboards built against the agreed metric definitions.
Validate
Numbers reconciled against source systems until stakeholders trust them.
Operate
Dashboard maintenance and new-metric requests under a support cadence.
# reconciliation run · revenue_kpimetric = "monthly_recurring_revenue"source_sum = 482910.00dash_value = 482910.00variance = "0.00%"status = "reconciled"
Every metric defined once, traced to its source
- One metric definition — no more two teams reporting different numbers for the same KPI.
- Reconciled to source — every dashboard figure traceable back to the raw transaction.
- Designed for daily use — not a report nobody opens after the kickoff meeting.
Built into every engagement
Analytics engagements on the record
Manufacturing group — plant-floor operations BI
Real-time yield dashboards cut decision latency from weekly to same-shift.
Retail chain — unified customer analytics
Single customer- 10 warehouses replaced four disconnected reporting tools.
Questions we get about BI & dashboard programmes
Why start with a KPI-definition sprint instead of building dashboards right away?
Because two teams reporting different numbers for the same metric is the most common reason dashboards get abandoned. Definitions are agreed with stakeholders first, so the warehouse and dashboards are built against numbers everyone already trusts.
What BI tools do you build in?
Looker, Power BI, or Metabase — the choice depends on your team's existing tooling and who's actually opening the dashboard daily, decided during the define phase.
How do you know the dashboard numbers are correct?
Every figure is reconciled against source systems during the validate phase before stakeholders are asked to trust it — the goal is dashboard-to-source variance at zero, not an approximate match.
Can our analysts build their own reports afterward?
Yes — a governed semantic layer lets analysts self-serve without breaking the underlying warehouse, and data-literacy enablement is part of the hand-over so your team can extend dashboards without waiting on us.
How are reports delivered?
Scheduled automated reports land where decisions actually happen — Slack, email, or the boardroom deck — rather than sitting in a BI tool nobody opens between meetings.