AI-ready data is the difference between a pilot and a product
Organisations often discover the state of their data only once an AI project is already underway, by which point the timeline has already slipped. We start with an honest assessment of your data landscape, covering quality, accessibility, structure and ownership, then design an AI-ready data architecture that serves the work you’re planning rather than the reporting needs of a decade ago.
Our AI data readiness services
We work across assessment, architecture and engineering, and you can start wherever your gaps are.
AI data readiness assessment
We audit your data sources, quality, structure and governance, and give you a clear picture of what’s usable today, what needs work, and what it will take to close the gap.
AI data architecture design
We design the architecture that supports AI workloads, covering storage, processing, vector infrastructure and the access patterns your models and applications will actually need.
Data quality and remediation
We identify the quality issues that break AI systems, including inconsistent formats, missing values, duplication and drift, then build the processes that keep them from returning.
Pipeline and integration engineering
We build the pipelines that move data reliably from source systems into the places your AI applications can use it, with the monitoring to tell you when something breaks.
Metadata, cataloguing and lineage
We put in place the cataloguing and lineage tracking that lets your teams find the right data, understand where it came from, and trust what they’re building on.
Platform and infrastructure selection
We assess and recommend the data platforms and infrastructure that fit your requirements, your existing stack and your regulatory position, without vendor bias.
Data technologies and platforms
We work across warehousing, orchestration and retrieval infrastructure, and we recommend based on your requirements rather than a partnership agreement.
AI data readiness cases
We’ve built data foundations for organisations in regulated, data-heavy sectors where quality, lineage and access control aren’t optional.
Our AI data readiness process
Mapping your data landscape
We work through your sources, systems and ownership to build a picture of what data exists, who controls it, and what condition it’s in.
Assessing readiness against your AI ambitions
Readiness only means something relative to a goal, so we assess your data against the specific AI use cases you’re considering rather than a generic maturity model.
Designing the target architecture
We design the data architecture you need, with a clear view of what changes, what stays, and where the migration effort actually sits.
Building the foundation
Our data engineers build the pipelines, storage and access layers, working in stages so useful capability arrives before the whole programme is finished.
Establishing ongoing practice
We hand over with the documentation, monitoring and working practices your team needs to keep the foundation healthy as data volumes and sources grow.
Enabling users to be part of the design and delivery process in such an artful way makes the leaders of this business quite unique.
The WeAreBrain team stood out because they understood the problem we were trying to solve, and how we aimed to solve it.
Their speed and their attitude were impressive — the speed of their work is limited by your speed only!
FAQs
Questions about data readiness for AI? Find the answers here.
What does AI-ready data mean?
AI-ready data is data that’s accessible, sufficiently clean, well documented and governed enough to train or ground AI systems reliably. The bar is higher than for conventional reporting, because AI systems tend to amplify whatever quality problems exist underneath them.
How do we know if our data is ready for AI?
The quickest way to find out is a readiness assessment against a specific use case. Common warning signs include data spread across systems with no single source of truth, inconsistent definitions between teams, and nobody being certain who owns a given dataset.
Do we need a data warehouse or lakehouse before we can use AI?
Not necessarily. Some AI use cases work perfectly well on data in existing systems, particularly retrieval-based applications working with documents. We’d rather scope the platform to the use case than build infrastructure you may not need.
How long does data readiness work take?
An assessment typically takes three to five weeks. Remediation and architecture work varies widely depending on the starting point, though we phase it so AI projects can begin on the parts of the estate that are ready first.
Can we start AI projects while data work is still in progress?
Often yes, and it’s usually the better approach. Running a focused AI use case alongside the foundational work keeps momentum and tends to surface the data problems that matter most.
How does data readiness relate to data governance?
Readiness is about whether the data can support AI. Governance is about the rules, controls and accountability around using it. The two overlap and we usually address them together, which is covered on our data governance for AI page.
