Data Infrastructure
AI needs data. Not just any data — clean, accessible, well-governed data. Most mid-market businesses have data scattered across SaaS platforms, legacy databases, spreadsheets, and email inboxes. We build the infrastructure that brings it together: ingestion pipelines that pull from every source, transformation layers that clean and standardise, warehousing that makes it queryable, and governance frameworks that keep it trustworthy. This is the unglamorous work that makes AI actually function.
What you get
Real examples
Data pipeline architecture
Design and build ingestion pipelines that pull data from SaaS platforms, databases, APIs, and file systems into a central warehouse — with transformation, deduplication, and quality checks built in.
Data warehouse modernisation
Migrate from legacy reporting databases or scattered spreadsheets to a modern warehouse or lakehouse architecture that supports both analytics and AI workloads.
Common questions
We have data everywhere — where do we start?
We start with a data audit: what systems hold what data, how it flows, where the gaps are. Then we prioritise based on your business goals — usually the data that feeds your highest-value AI use case or reporting need comes first.
What’s the difference between a data warehouse and data lake?
A warehouse stores structured, cleaned data optimised for queries and reporting. A lake stores raw data in any format. A lakehouse combines both — raw storage with a structured query layer. We recommend based on your use cases.
How does this connect to AI?
AI models need training data, context data, and operational data. Without reliable pipelines and clean storage, your AI features will produce inconsistent or wrong results. Data infrastructure is the prerequisite for production AI.
Do we need clean data before starting AI?
Not perfectly clean, but accessible and understood. We can run AI projects in parallel with data infrastructure work — but the AI will only be as good as the data feeding it. We’ll tell you where the gaps are.
What tools do you use?
Snowflake, BigQuery, dbt, Airflow, Fivetran, and custom Python pipelines — depending on your scale, team, and existing stack. We recommend what fits your situation, not what we’re certified in.
Ready to get started?
Tell us about your project and we'll tell you honestly how we can help.
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