Ingestion
Batch and streaming connectors for products, SaaS tools and third-party APIs, with replay when something goes wrong.
Data engineering
Reliable pipelines, organised data, AI-ready knowledge
Disconnected sources become reliable pipelines, an organised data platform, and a knowledge layer that analytics, automation and AI can all query.
Overview
Most AI and analytics problems are data problems. We connect the sources, build the pipelines, model the warehouse and put quality checks in front of the numbers people make decisions with — then expose it all as a knowledge layer your products and models can query.
- Ingestion & pipelines
- Warehouse & data modelling
- Data quality & observability
- AI-ready knowledge layers
Problems we solve
- Numbers disagree
- Two dashboards, two answers, and no agreed definition of the metric underneath them.
- Pipelines break silently
- A source changes shape and nobody finds out until a report looks wrong a week later.
- AI has nothing to stand on
- Models cannot ground answers in data that is undocumented, duplicated and scattered across systems.
What we build
Transformation & modelling
Version-controlled transformations and a warehouse model with definitions your business actually agrees on.
Quality & observability
Tests, freshness checks and alerting on the pipeline, so failures surface before decisions are made on them.
Activation & AI preparation
Serving layers, APIs and embeddings that make the same trusted data available to reports, products and models.
Typical use cases
- Consolidating product, billing and CRM data into one warehouse
- Replacing spreadsheet reporting with governed metrics
- Preparing a retrieval layer for AI assistants
- Migrating a legacy ETL stack without downtime
How we deliver
- 01
Discovery
A scoped session to agree the problem, the constraints and the metric that will move.
- 02
Design & architecture
The smallest structure that solves it, written down before anyone writes code.
- 03
Build
Two-week increments with something reviewable at the end of each one.
- 04
Run & improve
Monitoring, iteration and a handover your own engineers can carry.
Technology we use
- Python
- dbt
- Airflow
- PostgreSQL
- BigQuery
- Snowflake
- Kafka
- Metabase
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- Do we need a warehouse before AI?
- You need trustworthy, documented data. That is often a warehouse, but for a first AI use case a focused pipeline into a retrieval store can be enough.
- Can you work with our existing stack?
- Yes. We work with what is already in place and replace pieces only where the cost of keeping them is higher than the cost of changing.
- Who owns the platform afterwards?
- You do. Everything is version-controlled, documented and handed over with your team involved in building it.
Related capabilities
Keep going
Where to go next
- Read the case studiesChallenge, solution and measured outcome from delivered engagements.Continue
- Calculate project costA guided technical discovery that returns a budget range, timeline and team shape.Continue
- Book a strategy callForty-five minutes with the engineers who would actually build it.Continue
Data engineering
Start a data engineering conversation
Tell us the problem and the deadline. You will speak to an engineer, and get a considered reply within two working days.
