Data Engineering and AI Readiness
The foundation every AI project either stands on or falls through. Ten engagements covering architecture, pipelines, storage, quality and the readiness work that decides whether models are buildable at all.
AI projects rarely fail for AI reasons.
The model is almost never the obstacle. What stops the work is that the data lives in four systems that disagree, nobody is accountable for any of them, the field everyone depends on has been quietly null since a form change in March, and the only person who understands the overnight correction job left last year. None of that is fixed by a better algorithm.
So this pillar exists to make the rest of the site buildable. We profile before we propose, we name an accountable owner for every significant dataset, and we instrument quality checks so the improvement survives us. Where the honest finding is that your existing warehouse is fine and the migration is not worth its cost, that is what the report says.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
Data Strategy and Architecture
A target data architecture and a sequenced roadmap, derived from the decisions you need to make rather than from a vendor shortlist.
Read more →Data Pipeline Development
ETL and ELT pipelines that survive schema drift, late data and reruns, with tests and monitoring from day one.
Read more →Data Lakehouse and Warehouse Build
A governed lakehouse or warehouse with a modelled core layer, access control and predictable cost.
Read more →Vector Database Design and Implementation
Vector stores designed around chunking, filtering and reindexing, tuned against measured retrieval quality.
Read more →Data Labelling and Annotation
Labelled datasets with a written guideline, measured annotator agreement and audited quality at every batch.
Read more →Data Cleaning, Quality and Enrichment
Profiling, deduplication, standardisation and enrichment, with automated checks that stop quality decaying again.
Read more →Feature Store Implementation
One definition per feature, point-in-time correct training data and low-latency serving from the same logic.
Read more →Real-Time Streaming Data Engineering
Event-time streaming with exactly-once semantics and late-arrival handling, built only where latency changes a decision.
Read more →Legacy Data Migration and Modernisation
Migration with profiled sources, signed-off mapping, rehearsed cutover, parallel running and a tested rollback.
Read more →Web Data Extraction and Enrichment Pipelines
Lawful, resilient web extraction with change detection, validation and enrichment into your own systems.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
Decide the shape
Data strategy and architecture worked backwards from the decisions you make badly today, and a lakehouse or warehouse build with a core model your domain experts actually agree on.
Move and clean the data
ETL and ELT pipelines that survive schema drift and reruns, quality and enrichment that fixes causes rather than symptoms, and legacy migration with a reconciliation pack that stands up to audit.
Make it AI-ready
Vector database design for retrieval and RAG, labelling and annotation with measured agreement, and a feature store once several models share features.
Bring in what you do not have
Real-time streaming where latency genuinely changes a decision, and web data extraction and enrichment with the terms of use assessed before anything is fetched.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
Do we need all of this before starting an AI project?
No, and treating it that way is how AI programmes get postponed for two years. The readiness work is sequenced against the specific initiative: a retrieval assistant needs governed content and a vector store but not a feature store; a churn model needs clean history and point-in-time correctness but no streaming. We scope to the initiative rather than to the architecture diagram.
What is the difference between a data warehouse and a lakehouse?
A warehouse is a managed structured analytical database — simpler to operate, excellent for reporting. A lakehouse adds warehouse-like transactions and schema enforcement on top of cheap open-format object storage, so unstructured content and machine learning workloads share one governed place with your tables. Lakehouse costs less per terabyte and asks more of your platform skills.
Our data quality is poor. Where do we start?
With profiling, because the defects people assume are rarely the defects that matter. Quality work ranks problems by the decisions they affect rather than by how bad they look, fixes those, and instruments automated checks so the improvement does not decay. Trying to fix everything is the most common way these efforts run out of budget.
How is this different from AI strategy and consulting?
AI strategy decides which AI initiatives are worth doing and in what order. This pillar decides and builds what the data estate must be for those initiatives to exist. They are frequently run together, and where only one is affordable first, resolving ownership and access usually unblocks more than another prioritisation exercise.
Do we keep the code and the models?
You do, in full, on final payment: pipelines, transformation code, migration tooling, schema contracts, tests, runbooks, labelled datasets, annotation guidelines and documentation. Everything is built so your own engineers can operate and extend it without us, which is deliberate rather than generous.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.