What we do
We organise operational data into trustworthy, well-governed systems that support reporting, automation and AI.
The service
Analytics becomes useful when people trust the definitions and can trace where a number came from. Most reporting problems aren't technical failures — they're two teams calculating 'active user' or 'revenue' differently and never noticing until a board meeting. Before building pipelines, we agree metric definitions with the people who actually use them, so the resulting numbers are trusted rather than merely produced.
Pipelines extract from source systems — application databases, SaaS tools, event streams — and transform that data into a consistent, tested shape. We favour ELT over hand-built ETL scripts where possible: land raw data first, then transform it in the warehouse using version-controlled, testable models, so changes are reviewable and a transformation bug doesn't require re-extracting from source to fix.
Warehouse and lakehouse design balances query performance against modelling complexity — dimensional models for reporting workloads that need to stay fast as data grows, simpler structures where volume and query variety don't justify that overhead. Lineage is tracked from source table to final dashboard, so when a number looks wrong, tracing it back to its origin takes minutes rather than a day of Slack messages.
Dashboards are built around decisions people actually make, not every column available in the warehouse — a smaller set of well-defined views tends to get used more than a comprehensive one nobody opens. Data-quality checks and alerting run alongside the pipelines themselves, so a broken source feed or a schema change surfaces as an alert rather than a wrong number someone eventually notices.
Capabilities
Designed for outcomes
What you receive
Technology
We stay technology-flexible and select the stack around your existing environment, security constraints, team capability and long-term cost.
Frequently asked
A focused build covering a handful of key sources and metrics typically takes four to six weeks, including definition workshops with stakeholders. Broader data platform builds spanning many source systems and a full warehouse model run two to three months, with dashboards delivered incrementally as each domain's pipelines go live.
We usually audit existing reports first to understand which metrics and definitions are actually trusted and worth preserving, rather than assuming a clean rebuild is always right. Where existing logic is sound but fragile, we rebuild it on governed pipelines; where definitions conflict across teams, that gets resolved before anything is migrated.
Pipelines include schema and data-quality tests that catch a source change before it silently breaks a downstream dashboard — the pipeline fails loudly and alerts the team, rather than loading bad data. We also document expected schemas so a source-system change is a known, testable event instead of a surprise discovered inside a report.
Yes — that's part of why we build governed, well-documented pipelines rather than one-off scripts. Clean, labelled, lineage-tracked data is the actual prerequisite for most AI work; teams that skip straight to a model usually end up rebuilding their data foundation anyway once they discover the inputs aren't trustworthy.
Either your team or ours, depending on internal capacity. We hand over documented, version-controlled pipelines that an internal data or engineering team can maintain directly; where that capacity doesn't exist yet, we offer an ongoing support arrangement covering monitoring, schema changes and new source onboarding.
How we work
Define decisions and source systems
Model and govern the data
Build pipelines and reporting
Monitor quality and expand coverage
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