From the warehouse to the dashboard to the decision, we build production-grade BI on a single, governed source of truth, so the number on the executive deck matches the number on the operations dashboard.
Most organisations don't have a "data problem." They have a data agreement problem: marketing's number doesn't match finance's number doesn't match the board deck's number. Our Business Intelligence practice exists to fix that, with a governed warehouse, a versioned metric layer, and dashboards every team can trust.
We start at the warehouse: Snowflake, BigQuery, Redshift, or Postgres, modelled with dbt, tested for quality, and refreshed on a schedule your operators can rely on. From there we build the metric layer, canonical definitions of revenue, retention, churn, margin, NPS, so the same metric returns the same answer no matter who queries it.
On top of that we build the dashboards, alerts, and embedded analytics your business runs on: executive briefings, operational consoles, customer-facing in-product reports, and automatic anomaly detection that pages the right team before customers notice. The end deliverable isn't a dashboard library, it's a measurable improvement in how your organisation decides.
A complete BI stack, from raw data ingestion through to embedded customer analytics. We can build the whole stack or plug into the layers you already have.
Cloud-native warehouses (Snowflake, BigQuery, Redshift, Databricks) with ELT pipelines built in dbt. Source-of-record schemas, dimensional models, slowly-changing dimensions handled correctly, the data foundation everything else stands on.
One definition of revenue, retention, churn, NPS, margin, modelled once, used everywhere. We build the semantic layer that makes "what's our MRR?" return the same answer in Tableau, Looker, and the board deck. No more reconciling numbers in the meeting.
Tableau, Power BI, Looker, Metabase, we build the dashboards and the governance around them. Design grammar that's consistent across the org, performance tuning that keeps load times honest, and a publishing workflow that doesn't break on Monday morning.
White-labelled, multi-tenant dashboards inside your customer-facing product. Row-level security, SSO, theming, and a self-serve query layer your customers can actually use without filing a support ticket.
Statistical anomaly detection on your KPIs, conversion drops, latency spikes, fraud signals, routed to the right Slack channel or pager. No more discovering a problem in the Monday review when it started on Friday.
What data exists, where it came from, who can see it, and what changed last week. We set up data catalogs (DataHub, Atlan, Collibra), automated lineage tracking, and access controls that satisfy compliance without strangling the analysts.
Three representative engagements, the chaos at the start, and the operating cadence that replaced it.
A multi-channel retailer had separate teams reporting separate numbers from separate spreadsheets. We built a unified warehouse on Snowflake, modelled a single customer-and-order semantic layer in dbt, and rebuilt the executive dashboard so the CMO and the COO finally saw the same revenue, the same returns, and the same NPS.
A B2B SaaS company couldn't tell which features actually drove retention. We built the product event pipeline, joined it to the billing system, and shipped a retention & expansion dashboard that segments customers by feature usage, plan, and cohort, with anomaly alerts on every metric the revenue team cares about.
Plant managers were drowning in PDFs and waiting until end-of-week for OEE numbers. We built a real-time operations dashboard fed from the MES and ERP, throughput, downtime, defect rate, energy, with drill-down to the individual workcell and a pager when any KPI breached its tolerance band.
BI fails for predictable reasons: ungoverned definitions, ungoverned access, dashboards no-one trusts. These five operating rules are how we stay on the right side of that line on every BI engagement.
Tell us the question your last meeting couldn't agree on. We'll come back with a one-page audit: where the disagreement starts, what it would take to fix it, and the dashboard you'll have on the other side.