Technologies · Data

Data pipelines and warehouses that read from truth.

Ingestion, transformation, and modelling into a warehouse your dashboards can trust — with tested transforms, lineage, and data-quality contracts so analytics reflect reality, not guesswork.

Start a project →The data stack

Ingest → servereads from truth

◉

Ingest

batch · stream

→

⚙

Transform

dbt · tested

→

▤

Model

warehouse

→

▢

Serve

dashboards

Why it matters

Dashboards are only as honest as the pipeline behind them.

A pretty chart on bad data is worse than no chart at all. We engineer the pipeline so every number traces back to a source you can trust.

Good decisions need trustworthy data. We build pipelines that ingest from your sources, transform with tested, version-controlled logic, and model the result into a warehouse designed for analytics. Data-quality contracts catch bad rows before they reach a dashboard, and lineage shows exactly where every metric came from. The dashboards on top read from that single source of truth — the same principle behind our own fleet analytics, where fabricated or stale numbers are treated as a defect. The outcome is analytics your team can actually act on.

products with live data

platforms in production

agents feeding data

source of truth

The data stack

Ingest, transform, model, serve.

The tools and patterns behind every pipeline and dashboard we ship.

◉

Ingest

from any source

Batch & streamingAPIsDatabasesFiles & events

✦

Transform

tested and version-controlled

dbtSQL modelsData-quality testsIdempotent jobs

◎

Model

into a warehouse

PostgresWarehouse schemasDimensional modelsLineage

▤

Serve

dashboards from truth

Analytics dashboardsMetrics layerScheduled refreshAccess control

Tested transformsnot spreadsheet glue

◉

Ingestion

your sources

⚙

dbt + SQL models

version-controlled

⚠

Quality contracts

fail loudly

◎

Lineage

traceable

Tested logic, not spreadsheet glue.

We orchestrate ingestion and transformation as version-controlled, testable code — with dbt models and data-quality contracts that fail loudly when the data drifts, instead of silently corrupting a report.

Batch and streaming ingestion from your existing sources
dbt transforms and SQL models under version control
Data-quality contracts that catch bad rows before they land
Lineage so every metric traces back to its origin

Analytics you can act on.

We model data into a warehouse built for querying and serve dashboards that read from that single source of truth — with a metrics layer so ‘revenue’ means the same thing everywhere.

Postgres and warehouse schemas modelled for analytics
A shared metrics layer so definitions never drift
Scheduled, idempotent refresh with monitoring
Role-based access and per-tenant isolation on the data
Dashboards that never show fabricated or stale numbers

One source of truthdefinitions never drift

▤

Warehouse schemas

modelled

⚖

Metrics layer

shared

◉

Scheduled refresh

monitored

▢

Dashboards

no stale numbers

FAQ

Questions we get asked

We build pipelines with tested, version-controlled logic — typically dbt for transforms, SQL models, and Postgres or a warehouse for storage, orchestrated so jobs are idempotent and monitored.

Data-quality contracts test the data as it moves and fail loudly when something drifts, and lineage lets us trace every metric to its source. Dashboards read from a single source of truth rather than ad-hoc extracts.

Yes. We build the analytics dashboards on top of the warehouse, backed by a shared metrics layer so a term like ‘active user’ means the same thing on every chart.

Yes. Data access is role-based and per-tenant isolated, following the same isolation discipline we apply across every platform we build.