AI · Foundations

Data readiness

Pipelines, quality, structure and governance, so the models you build later have something trustworthy to learn from and retrieve. Most AI failures are data failures wearing a different hat.

Data readiness
Outcomes

The unglamorous work that decides whether AI works at all.

1 ledger
Multiple warehouses reconciled into one stock system for KhanSaab.
Versioned
Every document indexed with version history before retrieval.
Day one
Lineage and quality checks in place before any model is trained.
Where it applies

What we build with it.

The shapes this work usually takes. Yours will differ; the approach won't.

AI-readiness assessment

A clear picture of what data you have, what state it is in and what the first AI use case can realistically use.

Pipelines and warehousing

Reliable ingestion from your systems into a modelled, queryable store.

Quality and governance

Validation, deduplication, PII handling and access controls that satisfy audit.

Content structuring

Documents, records and media prepared for retrieval and training.

What you get

Deliverables, not decks.

Everything is handed over as code, data and documentation you own. Nothing depends on us staying.

  • Data inventory and readiness report
  • Pipeline architecture and implementation
  • Data model and warehouse or lakehouse setup
  • Quality checks, lineage and monitoring
  • PII and access policies implemented in code
  • Prioritised AI use-case roadmap
How it runs

The engagement, step by step.

01

Inventory

Every source, its owner, its quality and its access rules, in one document.

02

Fix the flow

Ingestion that runs on schedule, fails loudly and can be replayed.

03

Model and govern

A schema people can query, with the controls compliance needs.

04

Prove it with a use case

The first AI feature is built on the new foundation, so readiness is demonstrated rather than declared.

Tools we reach for

Chosen per project, by score and cost.

dbtAirflowPostgreSQLBigQuerySnowflakeKafkaAWS GlueGreat Expectations
Common questions
Do we need this before any AI work?

Not always all of it. The assessment tells you the minimum needed for your first use case, and what can wait.

How long does an assessment take?

Two to three weeks for most mid-sized companies, ending in a report and a roadmap you can act on with or without us.

Can you work with our existing data team?

Preferably. We embed with them, build alongside them and leave them owning the result.

Need data readiness?

Tell us the problem. We'll come back within one business day with how we'd approach it.

Enquiry

Take the
brighter path.

Tell us what you’re building. We’ll be in touch within one business day.