Data Readiness for AI
The gap between having data and AI using it.
Every stalled AI pilot has the same autopsy: the data wasn't ready. We measure the gap — quality, structure, access — and build what closes it, in the order that unblocks your first use case.
What we build
- Readiness assessment against your actual AI use cases
- Cleaning and structuring pipelines for the data that matters first
- Retrieval preparation — chunking, indexing, and evaluation where it earns its place
- Access shaped around your permissions and controls
When teams come to us
- An AI pilot stalled on data quality
- A vendor said “just connect your data” and it was never that simple
- The Discovery Sprint found data as the real first step — this is that step
How we build it
Assess against the use case
readiness is relative to what you want to build
Fix in value order
the subset that unblocks first value, first
Prepare access and retrieval
permissions, structure, indexing
Verify with the AI itself
evaluation on real queries, not assumptions
Works well with
FAQ
How long does readiness take?
Depends what the use case needs — readiness for one assistant is not readiness for everything. That’s why we scope to first value.
Is our data too messy for AI?
Messy is the default state of real companies. The question is which subset matters first.
Do we need everything ready before starting?
No. Readiness and the pilot advance together.
Find out exactly where your data stands.
The sprint includes technical feasibility and data requirements.