Data Engineering Services

Almost every stalled AI project we are asked to rescue turns out to be a data engineering problem wearing an AI costume. The model was fine. The pipeline feeding it was manual, undocumented, and owned by someone who left. AI Applied builds the data foundations that make AI, analytics and reporting dependable rather than heroic.

What we build

Ingestion and integration

We connect the systems that hold your data: line-of-business applications, CRMs, finance systems, operational databases, third-party APIs, file drops, and the spreadsheets that somehow run a critical process. Ingestion is scheduled, monitored and idempotent, so a failed run can be re-run safely instead of quietly corrupting downstream tables.

Warehouses and models

We design warehouse schemas that reflect how the business actually thinks, not how a source system happens to store rows. Transformations are written as version-controlled, tested code rather than clicked together in a GUI. That means a change can be reviewed, a failure can be traced, and last quarter numbers can be reproduced exactly.

Quality and observability

Every pipeline gets tests: freshness, row count expectations, uniqueness, referential integrity, and business rules that encode what “impossible” looks like in your domain. Failures alert a human rather than appearing three weeks later in a board pack. Lineage documentation shows which report depends on which source, so the impact of a change is known before it is made.

Governance and access

Personal and commercially sensitive data is classified, access is granted by role, and retention rules are enforced by the platform rather than by memory. We document processing purposes and legal bases alongside the technical implementation so that a data protection review does not become an archaeology project.

Unstructured and document data

A growing share of the useful information in most organisations sits in PDFs, emails, contracts, scanned forms and call recordings. We build extraction pipelines that turn those into structured, queryable records with provenance retained, so any extracted value can be traced back to the page it came from.

Why it matters for AI specifically

Retrieval systems are only as good as the freshness and permissions of the content they retrieve. Forecasting models fail when a source system silently changes a field. Assistants hallucinate confidently when the underlying document store is three months stale. Data engineering is where those failure modes are prevented, and it is consistently the highest-return work we do for clients.

Platforms we work with

• Cloud warehouses including Snowflake, BigQuery, Azure Synapse and Redshift, plus Postgres where a warehouse would be overkill.
• Transformation and orchestration with dbt, Airflow, Dagster, Azure Data Factory and native cloud schedulers.
• Streaming and event pipelines where near real-time genuinely matters, and honest advice about when it does not.
• Vector and search infrastructure for retrieval-augmented systems, with permission filtering built in.
• Business intelligence layers in Power BI, Looker or Metabase sitting on modelled, tested data rather than raw extracts.

A pragmatic starting point

You do not need a two-year platform programme. We usually start with one painful reporting or AI use case, build the pipeline properly end to end, and let that become the pattern everything else follows. It delivers something useful within weeks and creates the standards, tests and documentation that make the second pipeline faster than the first.

Data health check

If you are unsure where you stand, we run a fixed-fee data health check. It reviews your current sources, pipelines, quality controls, access model and documentation, and produces a prioritised list of what to fix, what to leave alone, and what is blocking the AI work you want to do. It is deliberately useful whether or not you engage us for the build.

Get the foundations right

Data engineering rarely makes it onto a strategy slide, but it decides whether everything above it works. If your reporting is contested, your pipelines are manual, or your AI prototype cannot get clean data, talk to us. You may also want to read about our AI implementation services and machine learning development.

Explore our services

A full index of what we do is on the AI services page. The individual services are:

• AI readiness assessment
• AI implementation services
• Machine learning development
• Data engineering
• AI automation
• AI governance and compliance

Where we work

We deliver across the United Kingdom from our Glasgow studio, with on-site time included: London, Manchester, Birmingham, Edinburgh, Glasgow.