Business Intelligence
BI is the layer where data finally meets decision-makers. Done well, it's the reason your exec team can run the business from a tab in their browser; done badly, it's the reason they ignore the data altogether.
We build BI that the C-suite actually uses — fast, trusted, opinionated where it should be, and quietly alerting them when something has gone wrong.
What we do
Does this sound familiar?
Executives still ask for the spreadsheet
You shipped the dashboards. The board still asks for the same Excel export, the same slide, the same Friday email. The dashboard exists; nobody opens it.
Dashboards die when they're a wall of charts without a headline, when they load like 2009, or when the number disagrees with the one the CEO already has in their head. Trust is the product — and you haven't shipped trust yet.
We rebuild BI as narrative-first, sub-second, reconciled to the source of truth. Boardroom-grade design, opinionated about the metric that matters, performance-tuned so the dashboard opens at the speed of an exec's patience.
Diagnosis:A dashboard the C-suite doesn't open is just an expensive screensaver.
Someone burns a week assembling the board pack
The monthly board pack is a manual rebuild. The investor update is slides exported from dashboards. The ops report is a Friday-afternoon spreadsheet sent by email. Every cycle, the same person, the same week.
Without an automated reporting layer, you're paying an analyst to copy-paste-format-attach — and quietly absorbing the typos and stale numbers that creep in each cycle.
We automate the distribution layer end to end: scheduled, branded, audience-segmented outputs to PDF, Slides, Sheets, Slack, and email. The reporting tax disappears and the analyst gets their week back.
Diagnosis:If a human assembles the board pack each month, you're paying salary for copy-paste.
You only find the breakage at Monday's review
Revenue dropped Thursday. Paid efficiency collapsed Friday. The exec team finds out at Monday's WIP — and by then four days of budget decisions have already been made on a broken assumption.
Human vigilance is the wrong control system for a metric that breaks at 3am. You need machines watching the machines.
We instrument every material metric with statistical and ML-based anomaly detection wired into Slack, PagerDuty, or email — with thresholds tuned to your seasonality. Breakage gets flagged the hour it happens, not three days later in a meeting.
Diagnosis:If a human has to spot the dip on a chart, you'll always find out three days late.
The data team has become a ticket queue
Operations asks a question, the data team builds a one-off dashboard, the question gets answered, the dashboard is never reused. Repeat sixty times a quarter. Strategic work never starts because the queue never empties.
Without a semantic layer and certified self-serve content, every operational question becomes a custom build — and the data team's senior people end up brokering SQL instead of compounding analytics IP.
We stand up governed self-serve analytics on Looker, Mode, Hex, or Lightdash — semantic layer, certified content, an enablement programme — so operations answers its own questions and the data team gets to do the work they were hired for.
Diagnosis:Treating the data team as a ticket queue guarantees they never do the work that compounds.
How we run BI
Three principles for BI people actually use
Narrative
Dashboards open with the story — what changed, why, what to do — not with twelve charts in a 4×3 grid. Information density tuned to the decision the user is about to make.
Fast
Sub-second query performance is non-negotiable. We tune the semantic layer, the warehouse, and the caching so dashboards open at the speed of thought.
Trusted
Reconciled to source of truth, monitored for breakage, versioned for change tracking, and documented for governance. Trust is earned over months; broken in seconds.
The greatest value of a picture is when it forces us to notice what we never expected to see.
Frequently asked questions
BI, demystified
Looker for governance and embedded analytics. Power BI for Microsoft-heavy shops. Tableau for visualisation depth. Mode/Hex for technical teams. We pick based on your team's skills, your stack, and your governance requirements — not on vendor preference.
Through a semantic layer (LookML, MetricFlow, Cube, dbt Semantic Layer) that centralises metric definitions. Every BI tool and every consumer pulls from the same definition — no more arguments about whose CAC is right.
Yes — Tableau-to-Looker, Looker-to-Power-BI, and the other combinations are routine for us. We typically run the new tool in parallel, validate dashboard-by-dashboard, and migrate users in cohorts.
Three things: narrative design (open with the story, not the data), speed (sub-second queries), and reconciliation (numbers match what the CEO already believed). We diagnose which is missing in your current setup and fix it.
Looker, Sigma, Mode, and Cube all have strong embedded-analytics offerings. We implement customer-facing analytics with proper tenancy, RBAC, and white-labelling — including for clients selling analytics-as-a-product.
Ready to start with business intelligence?
Tell us where you are today and what you're trying to fix. We'll show you exactly how we'd plan, execute, and measure.
- No commitment required
- Speak to a senior architect
- Get a rough timeline estimate
