One number, one source of truth.
Pipelines, warehouses and BI that turn raw events into decisions — so finance, ops and leadership stop arguing about whose spreadsheet is right.
Content reviewed
A warehouse full of maybes.
Most companies do not have a data problem — they have a trust problem. Numbers live in six tools, three exports and one heroic analyst’s laptop. Every meeting starts with a debate about whose figure is correct, and every decision ships a week late because of it.
We build the boring, load-bearing layer that fixes this: pipelines that do not silently fail, warehouses modelled around how your business actually works, and dashboards people check before their first coffee. Governance and access control included — so the right people see the right numbers, and auditors stop sweating you.
From raw events to decisions.
The full path from ingestion to insight, engineered as one system with one owner.
Data pipelines & ELT
Reliable ingestion from your apps, SaaS tools and devices — orchestrated, tested and alerting before you notice.
Warehouse & modelling
A warehouse modelled around your business entities, with dbt-style transformations your team can read and extend.
BI & dashboards
Power BI, Metabase or Superset dashboards built for the questions people actually ask each Monday.
Predictive reporting
Forecasts and anomaly detection layered onto clean, governed data — the on-ramp to serious ML work.
Automation & reverse ETL
Clean data pushed back into your CRM and ops tools, so insight turns into action automatically.
Governance & compliance
Access control, lineage and retention policies — GDPR-aware by default, audit-ready by design.
Trust is built incrementally.
We earn adoption one reliable number at a time, starting where the pain is loudest.
01Map the questions
We start from the ten decisions your team makes weekly, then work backwards to the data they need.
02One golden metric
The first sprint ships a single automated number, usually revenue or inventory, that everyone can check against source. Each sprint after that adds the next metric.
03Model & document
Entities, definitions and lineage written down so "active customer" means one thing everywhere, forever.
04Operate & extend
Monitoring, alerting and a senior team on call. New questions become new models — on the same foundation.
Your product could be the next case on this page.
Start with a free 4-hour workshopThe pipes under the prediction.
Forecasting only works when the data layer underneath is trustworthy. This platform is both — and we built each half.
01Every B2B order as input, tagged by product type
Forecasting Model
Live sales, seasonality and operational signals flow through engineered pipelines into a forecasting platform teams actually plan with — inventory, staffing and spend.
Picked for the long run.
dbt, Airflow and Postgres: your team inherits these choices, so we make them conservatively.
Where the data goes next.
Clean numbers are rarely the end of it. These are the builds that usually come after.
Where most data projects start.
Whether you need a warehouse at all, what happens to the costs, and who maintains it after us.
Put it in a brief. A senior engineer reads it and replies within one business day.
Our data is a mess. Where do we even start?
Everyone’s is. We start with a two-week audit: what exists, where it lives, what is reliable. You get a prioritised map and a fixed-price proposal for the first golden metric — no platform rebuild required upfront.
Do we need a data warehouse, or is Postgres enough?
Often Postgres is plenty — we will tell you when it is. Dedicated warehouses earn their cost at scale or with heavy analytical load. We size the architecture to the data you actually have, and nothing bigger.
Can you work with our existing BI tool?
Yes. Power BI, Metabase, Superset, Looker — the tool matters less than the modelling underneath it. We fix the foundation first, then make whatever sits on top finally show consistent numbers.
How do you handle GDPR and data residency?
EU-region hosting on request, PII minimisation in the pipeline design, role-based access on every layer and documented lineage. We design this in at the schema level, so an audit finds it already in place.
How do you stop dashboards from contradicting each other?
The usual cause is everyone defining 'active user' or 'revenue' slightly differently in their own queries. We fix it with a single modelled semantic layer in dbt, where each metric is defined once and reused everywhere, so the same number shows up in Metabase, in the sales tool and in the board deck.
How do you keep warehouse costs from spiralling?
Costs blow up from full-table scans, models rebuilt from scratch every run, and dashboards hammering raw tables. We use incremental models, partition and cluster large tables, materialise the heavy aggregations, and set warehouse auto-suspend. Snowflake and BigQuery both bill on compute scanned, so the savings come from scanning less — typically 30–50% post-audit.
When is real-time streaming worth the complexity over batch?
Rarely, honestly. Batch every few minutes covers most analytics needs at a fraction of the operational cost. Real-time (Kafka, Materialize, ClickHouse) earns its keep for fraud scoring, live-ops dashboards, and ad bidding — where a decision in seconds has real value. We'll push back if 'real-time' is a want rather than a measurable need.
Will my team be able to maintain it?
Yes. Every pipeline ships with documentation, runbooks, and a one-week pairing handover. We can stay on retainer for evolution, or hand back fully and exit gracefully.
Tired of arguing about
whose number is right?
Tell us which decisions are stuck waiting on the data. You hear back from a senior data engineer inside one business day, with where we would start.