Service
AI Data Readiness Sprint
Most AI projects don't fail on models. They fail on data. Fix that first.
What this solves
- RAG and AI assistant projects that return wrong or outdated answers because the underlying documents are a mess
- Knowledge scattered across SharePoint, email, PDFs, scans, and legacy systems — with no inventory of what exists or what's current
- Duplicate, conflicting, and obsolete documents poisoning retrieval quality
- No honest baseline for the question "is our data actually ready for AI?"
What we deliver
- A structured audit of your unstructured data: sources, formats, freshness, ownership, and access
- A data-readiness scorecard — a clear, defensible rating of where you stand and what blocks AI adoption
- Cleaning and preparation of priority content: deduplication, structure extraction, metadata, and chunking strategy
- A retrieval foundation ready for RAG: your documents, organized so AI can actually use them
- A prioritized remediation plan for everything we didn't fix in the sprint
Duration & Investment
| Timeline | Fixed weekly format — scoped in whole weeks, so cost and calendar are known before we start. |
|---|---|
| Investment | Priced per sprint based on data volume and source count. Fixed quote after a scoping call; no open-ended discovery billing. |
Best for
Companies planning an AI assistant, knowledge base, or document-heavy automation — before they build it. Also the right move when an existing AI tool “gives bad answers” and nobody knows why. Spoiler: it's usually the data.