01
Use-case data mapping
Identify the exact sources, fields and documents the AI use case depends on.
AI data readiness assessment
AI data readiness assessment: know if your data is ready before you build.
A data readiness check tied to one AI use case - not a generic data audit.
We assess the data behind a specific AI use case - ownership, quality, access, lineage and integration - and show what must be fixed before a pilot.
Clear go, fix or stop viewKnown data gapsPrioritised fixesPilot-ready scope
BYOND BOUNDRYS CONSULTINGApplied AI
Capability focusAI Data Readiness
01Target use case04Readiness lenses05Core deliverables
Why this matters
Why data readiness decides whether an AI project succeeds.
Most AI projects that stall do so because of data, not models: unclear ownership, inconsistent definitions, missing history, restricted access or documents nobody can reliably retrieve. A readiness assessment surfaces these issues while they are still cheap to fix.
What we build
What the AI data readiness assessment covers.
We align experience, intelligence, data and operations around the job your team needs to complete.
02
Quality and coverage testing
Profile completeness, consistency, freshness and history against what the model needs.
03
Access, privacy and ownership
Confirm who owns each source, who may use it and which controls apply.
04
Integration and retrieval review
Check how data can be extracted, joined, chunked and retrieved in production.
How we make it dependable
How we assess data readiness for AI.
01Assess data against a specific use case, not in the abstract.
02Test real samples instead of relying on system descriptions.
03Name an owner for every source the AI will depend on.
04Separate must-fix blockers from nice-to-have improvements.
How it comes together
How the data readiness assessment runs.
The sequence keeps scope, evidence and ownership clear from the first conversation to the next release.
- 01Scope
Agree the use case, decision and data in question.
- 02Profile
Sample, profile and test the relevant sources.
- 03Score
Rate readiness, risk and remediation effort.
- 04Recommend
Prioritise fixes and define a pilot-ready scope.
Delivery workflowDefine the use case → Map sources and owners → Sample and profile data → Score readiness and risk → Recommend fixes and next phase
What you receive
Data readiness deliverables you receive.
Every engagement is shaped around practical outputs that help your team make a decision, start a build or operate the next version.
01Data source and ownership map
02Data quality and coverage findings
03Readiness scorecard by source
04Risk, privacy and access notes
05Prioritised remediation plan
Technology layer
Tools we use to profile and test your data.
We choose tools for fit, control and maintainability - not because a logo is fashionable.
Python
Pandas
NumPy
PostgreSQL
MongoDB Atlas
MySQLPydantic
AWS S3
FAISS
BM25FAQ
AI data readiness: common questions
What is AI data readiness?
AI data readiness is whether the data a specific AI use case depends on is available, owned, accurate, complete, accessible and retrievable enough to produce reliable results. It is judged per use case: the same data can be ready for one workflow and not for another.
What does a data readiness assessment for AI projects include?
A data source and ownership map, data quality and coverage findings, a readiness scorecard for each source, notes on risk, privacy and access, and a prioritised remediation plan that separates blockers from improvements.
When should we assess data readiness?
Before funding a GenAI, RAG or machine-learning build, when choosing between candidate AI use cases, or when a proof of concept has stalled on data quality or access.
Do you need full access to our systems?
No. We work from representative samples, exports and conversations with data owners. Access is agreed up front and sensitive data can stay in your environment.
What happens after the assessment?
You receive a remediation plan and a pilot-ready scope. From there we can run an AI proof of concept, build the data pipelines, or hand the plan to your own team.
How is this different from the AI Discovery Engagement?
The AI Discovery Engagement compares several candidate workflows to find the one worth funding first. The data readiness assessment goes deeper on the data behind a chosen use case. Many teams run discovery first, then readiness for the selected workflow.
Ready for the next step?
Bring us the workflow behind the requirement.
We can help shape the first release, architecture and evidence needed to move forward.