We build on established components
We size the stack to the company. Most businesses don't need a warehouse platform with a five-figure monthly bill, and we'll say so.
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We bring your systems into one place, modelled, tested and documented, so reports stop disagreeing and questions get answered in minutes instead of days.
Your CRM, billing system and spreadsheets each count customers differently. Nobody's wrong, and nobody agrees.
What exactly is an "active customer"? Ask three people, get three answers, and every report built on top inherits the ambiguity.
Exports, lookups, manual joins, a spreadsheet rebuilt every week. It works until that person is away.
A failed sync or a changed field goes unnoticed for weeks. The dashboard still loads. It's just wrong.
BI on top of messy sources produces fast, confident, inconsistent answers. The problem was never the chart.
Scheduled, monitored ingestion from your CRM, billing, product, ads and operational systems. When something fails, it retries and tells you, it doesn't fail silently.
Raw data transformed into tables that match how your business actually works: customers, orders, subscriptions, margin. Versioned, tested, and readable by a human.
Metrics like revenue, active customer and churn defined in code, in one place, used by every report. When the definition changes, it changes everywhere.
Automated checks on the things that quietly break: duplicates, nulls where they shouldn't be, totals that don't reconcile, rows that stopped arriving.
Dashboards and scheduled reports built on the modelled layer, so the numbers agree regardless of who opens them.
Data flowing between tools on a schedule with validation and logs, so teams stop exporting from one system to paste into another.
One weekly view of revenue, pipeline, retention and cost, built from the source systems, that nobody has to assemble by hand or defend in the meeting.
Revenue and billing reconciled against the operational systems automatically, with the exceptions listed rather than hunted for.
Spend, pipeline and closed revenue joined end to end, so channel performance is a number instead of an argument.
Live operational metrics from the systems that run the work, with alerts when something moves outside its normal range.
Both depend entirely on clean, well-modelled data. This is usually the work that has to happen first, and the reason pilots stall when it hasn't.
We map your sources, the reports people rely on, and where the current numbers diverge. You receive a written scope, a fixed price, and a clear picture of what's causing the disagreement.
We connect the systems and land the raw data reliably, with scheduling, retries and monitoring from day one rather than bolted on later.
Raw tables become business tables, with definitions agreed with the people who use them. Every transformation is version-controlled and reviewable.
Automated checks on freshness, volume, duplicates and reconciliation, so problems surface as alerts instead of as a wrong number in a board meeting.
Dashboards, scheduled reports or direct access, plus documentation of every table and metric. Your team can extend it without calling us.
Postgres, dbt, Python, your existing cloud. No proprietary layer that makes leaving us expensive.
Every transformation is code, reviewable and reversible. Nothing important lives in a UI only one person can access.
Data quality checks run on every load. A pipeline that breaks loudly is worth far more than one that fails quietly.
Every table and metric has a definition a non-technical person can read, so "what does this column mean" stops being a Slack message.
We work within your accounts, request the minimum permissions, and hand over full control at the end.
We size the stack to the company. Most businesses don't need a warehouse platform with a five-figure monthly bill, and we'll say so.
Not always. If you have a handful of sources and modest volume, a well-structured Postgres database does the job at a fraction of the cost. We recommend based on your data, not on what's fashionable.
Because BI tools visualise whatever you point them at. If two sources define "customer" differently, the chart renders instantly and disagrees with the other chart. The fix sits underneath the dashboard, in the modelled layer.
A first working version usually takes four to eight weeks: the sources that matter most, modelled and tested. Additional systems are added in stages after that, each one delivering something usable.
Something will need updating, that's unavoidable. What we control is that it fails loudly and is quick to fix, because the logic is in version-controlled code rather than buried in a UI.
Yes, if they're comfortable with SQL. That's the main reason we build on dbt and standard databases rather than proprietary platforms. Documentation and handover are part of the scope, not an extra.
Infrastructure for a mid-sized company is usually modest, often less than the BI licences already being paid for. We estimate it during the assessment so there's no surprise.
In most cases, yes. If a tool has an API or a database, we can pull from it. Where one doesn't, we'll tell you what the workaround costs before you commit.