Services · Data Readiness
Data Readiness
Is our data ready for AI?
Most AI projects do not fail because of the model. They fail because nobody validated the data underneath it before the build started.
You do not know which of your data is clean and which is a problem until something breaks in production — usually six months and a significant budget into an engagement. We find that out in week two, before anything is built on top of it.
Week 2
When we find data problems — not month six
5 dimensions
AI readiness scorecard: quality, governance, infrastructure, security, integration
Zero
Surprises after your build begins
What the first 30 days look like
Week 1
Data environment audit — we assess your operational data across every source that will feed your AI deployment. Deduplication, completeness, accuracy, consistency. We map what you have and what the gaps cost you.
Week 2
Governance and security review — retention policies, classification, DLP tagging, compliance alignment against SOC 2, PIPEDA, and where relevant FIPPA. Infrastructure and security reviewed for AI deployment requirements.
Weeks 3 to 4
AI readiness scorecard delivered across five dimensions. Where gaps exist, a specific prioritized remediation plan with your options for closing them.
Day 30 — what you have
A formal AI readiness scorecard you can take into a board or compliance conversation. A clear remediation pathway. No ambiguity about what needs to happen before any build can begin.
What we deliver
Full data quality audit
Deduplication, completeness, accuracy, consistency across every data source feeding your AI deployment. Gaps mapped, risks quantified.
Data governance assessment
Retention policies, DLP tagging, classification frameworks, SOC 2, PIPEDA, and FIPPA compliance alignment. Required documentation for regulated industries and the public sector.
Infrastructure and security posture review
A targeted review from the perspective of Claude deployment — data access patterns, credential management, network segmentation, and the controls required to operate safely.
AI readiness scorecard
Formal rating across five dimensions: data quality, data governance, infrastructure readiness, security posture, and integration maturity.
Remediation pathway
Specific, prioritized steps to close each gap. You leave with problems and the sequence of fixes — not just a list of concerns.
Right for you if
- You are in a regulated industry and data compliance is a hard prerequisite to any AI deployment
- You have been told your data is messy and want to know exactly how messy before committing to a build
- You are about to begin agent or MCP work and want to protect that investment
- A previous AI engagement underdelivered and you want to understand why
Not right for you if
- You are in early-stage discovery and do not yet know which data sources your AI deployment will require — complete Discovery first
- You have recent, validated data documentation and just need a scoped integration — we will confirm this in the first conversation
Frequently Asked Questions
Timeline
4 to 8 weeks from kickoff