Data scattered across systems is not a data problem.
It is a business constraint

We build the pipelines, warehouses and integration layers that connect your data sources, move data reliably from where it is generated to where it is needed, and make it available in the right form for analytics, operations and AI.

Your CRM, ERP, operational databases, third-party APIs and file exports are generating valuable data every hour. Without the infrastructure to connect them, clean them and route them to where decisions are made, that data accumulates as an untapped liability rather than a commercial asset. We build the infrastructure that changes that — reliably, observably, and at a scale that grows with your business.

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A pipeline that carries bad data faster is not an improvement. It is a more efficient source of bad decisions.

Data infrastructure is not just about moving data — it is about moving the right data, in the right form, with the right quality checks, to the right destination, on a schedule your business can rely on. Every pipeline we build is designed with this complete picture in mind: reliable extraction, validated transformation, tested loading, and observability that tells you exactly what is happening before your business users discover something is wrong.

60%

Of business data generated by organisations is never used in any decision — not because it is unavailable but because the infrastructure to move it, transform it and make it accessible does not exist

14 HRS

Per analyst per week spent on data preparation — extracting, cleaning and reconciling data manually — in organisations without reliable pipeline infrastructure. Time that should be spent on analysis.

Faster time-to-insight for organisations with modern ELT pipeline infrastructure compared to those relying on manual data exports and spreadsheet consolidation.

Four constraints. One root cause.

These are the operational problems that poorly-designed or absent data infrastructure creates for organisations generating data they cannot yet use. They are consistently underestimated — until the moment they become visible.

Manual Data Transfer Consuming Hours Per Week

When systems do not communicate through automated pipelines, someone on your team is exporting CSVs, copying data between systems, running manual reconciliations, and reformatting spreadsheets. This is not a small operational nuisance — it is typically consuming 10–20 hours per week of your most technically capable people, who should be using that time to do work that creates commercial value.

Reporting That Takes Days Because Data Is in the Wrong Place

The data that would answer a commercial question exists — in a CRM, an ERP, an operational database, a third-party platform. But assembling it into a coherent answer requires pulling it from multiple systems, reconciling it across inconsistent schemas, and combining it in a format nobody designed for this purpose. A question that should take minutes to answer takes days. The decision it was meant to inform is made without it.

AI and Analytics Initiatives Stalled by Unreliable Data Pipelines

The AI model is built. The analytics dashboard is designed. But neither is trusted — because the data feeding them is inconsistent, delayed, or silently wrong in ways nobody has documented. Organisations invest in analytics and AI capability without first investing in the data infrastructure those capabilities depend on. The result is sophisticated tooling operating on an unreliable foundation, producing outputs that no informed user believes.

Data Siloes Creating Irreconcilable Views of the Business

Different departments — sales, finance, operations, marketing — each have their own version of the truth, extracted from their own system, at their own point in time. None of them is wrong. None of them is the same. The energy spent reconciling these views in every cross-functional meeting is not a process problem — it is a data infrastructure problem, and it compounds every quarter the infrastructure is not addressed.

Five infrastructure capabilities.One connected data stack.

These capabilities are not independent services — they are the layers of a complete data infrastructure. Each one builds on and enables the next. Click any tile to explore the technical depth behind it.

ELT Pipeline Architecture

Modern data infrastructure runs on ELT — Extract, Load, Transform. We extract data from your source systems, load it into your warehouse in its raw form, and transform it for analytics using SQL-based transformation tools. This approach is faster to build, easier to maintain, and more adaptable as your analytical requirements evolve. Every pipeline is incremental, tested, documented, and monitored from day one.

Source connectors for all major CRM, ERP and database systems
Incremental loading logic — only process what has changed
dbt transformation models — tested, documented, version-controlled
Data testing framework — freshness, completeness, referential integrity
CI/CD deployment pipeline for safe, tested releases

Data Warehouse & Lakehouse Architecture

The data warehouse architecture you choose determines the cost, performance and flexibility of every analytical query your business runs for the next five years. We design and implement warehouse and lakehouse architectures — selecting the right technology for your data volume, query patterns and cost constraints — and build the dimensional data model that organises your data for the analytics your business actually needs, not just the reports currently being run.

Data warehouse / lakehouse technology selection and provisioning
Dimensional data model design — facts, dimensions, business logic
Data partitioning and clustering strategy for query performance
Cost optimisation configuration — storage tiers, query caching
Access control and row/column-level security implementation

Real-Time Streaming Infrastructure

Not every business problem can wait for a nightly batch run. Customer behaviour analytics, operational monitoring, clinical safety alerting, and fraud detection all require data that is current within seconds. We design and build streaming data infrastructure using event streaming platforms that move data from source to destination in near-real-time — with the reliability, error handling and backpressure management that production streaming systems require.

Event streaming pipeline architecture and technology selection
Producer and consumer architecture with schema registry
Error handling, dead-letter queuing and replay capability
Latency monitoring and consumer lag alerting
Backpressure handling and throughput scaling configuration

Data Quality & Pipeline Observability

A data pipeline that runs without monitoring is not a reliable data pipeline — it is one whose failures are unknown. We build data quality frameworks and pipeline observability into every system we design: data quality tests that run at every load, schema change detection, anomaly alerting, data lineage tracking, and operational dashboards that show your data team exactly what is happening at all times.

Data quality test suite — freshness, volume, completeness, referential integrity
Anomaly detection — statistical thresholds on key business metrics
Schema change detection and alerting
Data lineage documentation — column-level impact analysis
Pipeline health dashboard for the data team

Data Orchestration & Workflow Management

Complex data infrastructure requires orchestration — a system that schedules, monitors, retries and coordinates the jobs that move your data from source to destination. We design and implement orchestration infrastructure with dependency management, retry logic and alerting — ensuring jobs run in the right order, handle transient failures gracefully, and tell the right people when human intervention is needed rather than silently failing.

DAG design — job definitions with correct dependency ordering
Retry and failure handling logic with exponential backoff
SLA monitoring — alerting when jobs exceed expected duration
On-call alerting routing — right message to the right person
Pipeline documentation — every job, its purpose, its dependencies

What separates production data infrastructure from a working prototype

Architecture Before Any Pipeline Is Built

Data infrastructure built without an architecture design accumulates technical debt from the first pipeline run. We design the complete stack before building any of it — technology selection, data model, orchestration approach, observability strategy. Every downstream decision benefits from the context of the full system.

Observability as a First-Class Requirement

Data quality monitoring, pipeline alerting, schema change detection and lineage tracking are designed into every engagement — not added when the first production incident reveals their absence. You should know when your pipeline has failed before your business users discover inconsistent data in their dashboards.

Regulated Environment Data Engineering

Building data infrastructure for pharmaceutical and healthcare organisations requires a different approach to security, access control, data lineage and audit trail than building for an unregulated commercial environment. We understand ALCOA+ data integrity, GDP pipeline requirements, and MHRA audit expectations — and design to satisfy them from the first schema design.

Senior Data Engineers on Every Engagement

The architecture decisions that determine whether data infrastructure scales reliably — partitioning strategy, incremental loading design, schema evolution handling, cost management — require experienced judgment. These decisions are made by senior practitioners who have built production data infrastructure before, not junior engineers learning on your systems.

Connected to the Wider Data Programme

Data infrastructure does not exist in isolation. It connects the data audit findings that preceded it to the analytics, governance and AI capabilities that follow it. We design every pipeline within the context of the full Pillar 02 data programme — so the infrastructure we build enables everything downstream rather than constraining it.

Who We Build Data Infrastructure For

We do our best infrastructure work for organisations that are ready to treat data as a commercial asset — and invest in the infrastructure that makes it one.

Growth-stage businesses with disconnected systems and analysts spending their time on data extraction rather than analysis
Pharmaceutical and life sciences organisations that need data infrastructure compliant with GDP and MHRA data integrity requirements
Organisations with AI or ML initiatives that have stalled because the data feeding them is unreliable or inaccessible
Businesses whose data warehouse exists but is not trusted — producing different numbers for different people depending on how they extract it
Founders who have reached the scale where “we’ll deal with data properly later” is actively constraining growth

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Data scattered across systems is not a data problem.It is a business constraint