AI Readiness

Most organisations jumping into AI have the same problem: they don’t know what their data actually looks like. They know they have a CRM, a warehouse, a few databases, some S3 buckets. What they don’t know is whether any of it is clean enough, complete enough, or safe enough to put in front of a model.

That’s what an AI Readiness Assessment answers.

Data estate inventory

We map your sources — what exists, where it lives, what format it’s in, how fresh it is, who owns it. Most organisations are surprised by what they find. Data assumed to be available turns out to be locked in a deprecated system. Sources that should be consistent aren’t. The inventory is the foundation everything else sits on.

Data quality

A model fed bad data gives bad outputs — confidently. We assess completeness, consistency, and accuracy across your key sources. Where quality issues exist, we identify root cause and remediation effort so you know what you’re working with before the build starts.

PII and regulatory risk

Can you put this data in front of a model? For most organisations, the honest answer is: some of it yes, some of it definitely not, and for a lot of it we’re not sure. We identify where PII sits across your estate, what your exposure is under GDPR and sector-specific regulation, and what masking or classification work is needed before any AI project can proceed.

AI use case fit

Not every use case is equally ready. We score your target use cases against your current data state — which are achievable now, which need 4–8 weeks of remediation, which are 6+ months out. This prevents the common failure mode of committing to an AI project the underlying data can’t yet support.

Infrastructure readiness

Do you have the compute? The API access? The right cloud setup? We check whether your existing infrastructure can support the model deployment pattern your use case requires — whether that’s a managed API, a self-hosted model, or a RAG pipeline against internal documents.

What you get

A written assessment covering: source inventory with data quality scores, PII risk map, use case readiness scores (now / remediate first / longer term), infrastructure gap analysis, and a prioritised remediation roadmap with effort estimates.

The assessment is a standalone deliverable. There’s no obligation to continue with us for the build. If the readiness work surfaces gaps that need filling, we can help — but the report is yours either way.

Who this is for

Enterprises who have an existing data estate and want to know whether it can support the AI use cases their leadership is now asking about. Funded startups who need to know quickly whether their current setup can support their AI product, or whether they need to invest in the data layer first. Typically takes 2–4 weeks depending on the number of sources.