AI Data Readiness Sprint
Many AI projects don’t fail because of the model. They fail because the data isn’t ready. Fix that first.
We audit and prepare your unstructured data so your AI initiatives stand on a solid foundation instead of a document swamp.
When this sprint is the right fit
Companies planning an AI assistant, knowledge base, or document-heavy automation — before they build it.
- 01RAG and AI assistant projects that return wrong or outdated answers because the underlying documents are a mess
- 02Knowledge scattered across SharePoint, email, PDFs, scans, and legacy systems — with no inventory of what exists or what's current
- 03Duplicate, conflicting, and obsolete documents poisoning retrieval quality
- 04No honest baseline for the question "is our data actually ready for AI?"
Also the right move when an existing AI tool "gives bad answers" and nobody knows why. Spoiler: it’s usually the data.
What we assess
We assess the quality, structure, access, and governance of your data, then deliver a practical plan to make it usable for AI.
Data quality
Duplicate, conflicting, and obsolete documents — and how much of that is poisoning retrieval quality.
Structure & formats
The sources and formats your knowledge is scattered across, and what it takes to extract usable structure from them.
Accessibility & ownership
Where your knowledge actually lives — SharePoint, email, PDFs, scans, legacy systems — who owns it, and how current it is.
Governance
What blocks AI adoption today, measured against an honest, defensible data-readiness baseline.
How the sprint works
Inventory
We audit your unstructured data across sources, formats, freshness, ownership, and access.
Review
We assess data quality — surfacing the duplicate, conflicting, and obsolete documents poisoning retrieval quality.
Gap analysis
We rate where you stand on a defensible data-readiness scorecard and identify exactly what blocks AI adoption.
Readiness plan
We clean and prepare your priority content, then hand off a prioritized remediation plan for everything else.
What you receive
Data audit
A structured audit of your unstructured data: sources, formats, freshness, ownership, and access.
Data-readiness scorecard
A data-readiness scorecard — a clear, defensible rating of where you stand and what blocks AI adoption.
Priority content cleaned & prepared
Cleaning and preparation of priority content: deduplication, structure extraction, metadata, and chunking strategy.
RAG-ready retrieval foundation
A retrieval foundation ready for RAG: your documents, organized so AI can actually use them.
Prioritized remediation plan
A prioritized remediation plan for everything we didn't fix in the sprint.
Fixed weekly format — scoped in whole weeks, so cost and calendar are known before we start.
Priced per sprint based on data volume and source count.
Fixed quote after a scoping call; no open-ended discovery billing.