The data exists. The layer that makes it reliable, fast, and usable does not.
Data engineering for organisations that need a data layer they can build on. Greenfield builds, pipeline remediation, query optimisation, vector databases, and ML data preparation. EU hosting as standard. On-premise where the context requires it.
TWO ENTRY POINTS
Where the engagement starts.
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Building from scratch
No data layer exists, or what exists is too fragile to build on. The engagement designs and builds the full data infrastructure: schema, pipelines, storage, and the governance layer that makes it maintainable. Built to last, not to be replaced in eighteen months.
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Fixing what exists
The data layer exists but it is slow, unreliable, or both. Queries that should run in seconds take minutes. Pipelines that should be stable fail without warning. The engagement finds the root cause, remediates it, and leaves a data layer the organisation can rely on.
WHAT IMAGEPLUS BUILDS AND REMEDIATES
The components of a working data layer.
FIG · 01 · DATA LANDSCAPE
THE TOOLING
What we work with.
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Relational databases
PostgreSQL and MS SQL as the primary stack. MySQL where the existing environment requires it. Schema design and query optimisation that holds under production load.
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Vector databases
Pinecone and equivalent for AI and knowledge base workloads. The storage layer that makes semantic search and retrieval-augmented generation reliable at scale.
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Data preparation
Alteryx and equivalent for data cleansing, transformation, and preparation. The unglamorous layer that decides whether the data that reaches a model or a dashboard is any good.
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Visualisation
Tableau and equivalent where the engagement includes the layer that makes data readable for the people who need to act on it.
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MLOps data layer
The data preparation infrastructure that machine learning models train on. Classification, extraction, and the pipeline that keeps models fed with clean, current data.
THE FOUNDATION
Every data engineering engagement ships with the same operational baseline.
Each engagement inherits what it requires. EU hosting is the default. The cryptographic audit trail comes on for regulated work. The SLA comes on where uptime is the commitment. The rest is standard.
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EU hosting as standard
Data stored in the EU by default. On-premise deployment supported where the regulatory context requires it. Data sovereignty and residency addressed from the start.
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Security and access control
Who can read, write, and modify the data layer. Enforced at every level.
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Cryptographic audit trail
Where the engagement calls for it, every data operation is cryptographically signed and traceable.
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GDPR compliance
Data handling designed to meet regulatory requirements from the start.
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Monitoring
The data layer is watched. Pipeline failures and performance degradation surface before they become business problems.
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SLA
Service level agreements available on all engagements, subject to separate arrangement.
HOW IT CONNECTS
Data engineering sits under the cognitive layer.
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Data strategy
Data strategy defines what the organisation needs the data to do. Data engineering builds the layer that makes it possible. The two often run in sequence.
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RAG and knowledge base AI
Vector databases and knowledge base infrastructure built here feed directly into retrieval-augmented generation and AI knowledge systems.
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AI orchestration
Clean, well-structured data is what makes AI systems reliable in production. The data engineering layer is the foundation the cognitive layer runs on.
Tell us what the data layer needs to do and where it is failing.
We will tell you what the engagement would look like and what it would take.
COMMON QUESTIONS
Asked before starting.
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When should we build a data layer from scratch versus fixing what exists?
When the existing data layer is too fragile or too slow to build on reliably, starting from scratch is often faster and cheaper than remediating. When the structure is sound but performance or reliability is the problem, remediation is the right call. The engagement starts with an honest assessment of which situation applies.
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What does data governance have to do with a performance problem?
Often more than the organisation realises. Slow queries and unreliable pipelines frequently trace back to undocumented schemas, inconsistent data quality, and lineage that nobody has mapped. When it surfaces, it is documented and flagged clearly. What the organisation does with that is their call.
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Where do you host the data?
EU hosting is the standard. Where the regulatory context requires it, on-premise deployment is supported. Data sovereignty and residency requirements are addressed from the start, not retrofitted.
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What databases do you work with?
PostgreSQL and MS SQL are the primary relational databases. Vector databases including Pinecone for AI and knowledge base workloads. MySQL where the existing stack requires it. The database choice follows what the data layer needs to do, not a preferred vendor.
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What is the difference between data engineering and data strategy?
Data strategy defines what the organisation needs the data to do for the business. Data engineering builds the layer that makes it possible. The two often run in sequence: strategy first, engineering after. Where the engineering engagement surfaces governance or structural questions, the strategy conversation follows.
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Is an SLA available?
Yes. Service level agreements are available on all data engineering engagements, subject to separate arrangement.