Service Data Readiness

Your AI is only as smart as your worst data pipe.

Nearly half of enterprises name data — findability, quality, permissions — as the thing actually blocking their AI strategy. It's rarely the models. It's the knowledge trapped in six systems that don't talk, the 'current' policy document with four competing versions, and the permissions nobody can map. I do the unglamorous work that decides whether everything built on top succeeds: making your data findable, trustworthy, and safely accessible to AI.

#1 blocker Data, not models — per every survey
Weeks 0–6 The work before the build
Reusable One foundation, every AI project

Here's how it usually goes: the RAG pilot underwhelms, everyone blames the model, and someone proposes buying a better one. Then you look at what it was retrieving from — a SharePoint with nine years of drafts, a wiki nobody pruned, PDFs of scanned faxes, and a file share where the real answers live in someone's personal folder. The model was fine. It was reading garbage with confidence.

Data readiness for AI is narrower and cheaper than a data-warehouse program — it's about the specific corpus your AI initiatives will touch. Can the system find the data? Is it current and deduplicated? Does structure survive extraction? And do the permissions actually exist in machine-readable form, so the AI only shows people what they're allowed to see? Those four questions, answered honestly, predict AI project success better than any model benchmark.

What gets fixed

  • Access and connectivity — connectors into the systems where knowledge actually lives, with authentication that respects source permissions.
  • Quality and currency — deduplication, version resolution, staleness flagging, and ownership so 'current' means something again.
  • Structure and permissions — documents parsed to preserve meaning, and access controls mapped so retrieval enforces them at query time.
Every AI project has a data-readiness phase. The only question is whether it happens before the build — or during the postmortem.

Four questions that predict AI success.

Answered with evidence in weeks — before they get answered in production.
01 / Findable
Can the AI reach it?
Inventory of where knowledge actually lives, and connectors that reach it — including the systems everyone forgot.
02 / Trustworthy
Is it current and true?
Deduplication, version resolution, and staleness detection — so retrieval surfaces the answer, not its four drafts.
03 / Structured
Does meaning survive?
Parsing and chunking that preserve tables, hierarchies, and cross-references instead of flattening them to soup.
04 / Permitted
Who may see what?
Access controls mapped and enforced at retrieval time — the difference between an assistant and a breach.
Data auditConnectorsDeduplicationChunkingPermissions mappingPipelines

Questions, answered.

The ones every buyer asks first.
How do I know if our data is ready for AI?

Four tests: can a system programmatically reach the knowledge, is it deduplicated and current, does structure survive extraction, and are permissions machine-readable? Most organizations pass one of four. The assessment grades each corpus in about two weeks and prices the gap.

Is this a full data-warehouse or MDM project?

No — that is the trap that turns AI initiatives into three-year infrastructure programs. AI data readiness targets the specific corpus your initiatives touch and fixes only what blocks them. Weeks, not years, and each fix is reusable by the next project.

Our documents are a mess of drafts and duplicates. Fixable?

Yes, and mostly automatically — near-duplicate detection, version clustering, and staleness scoring do the bulk of it, with humans resolving only genuine conflicts. The output is a canonical corpus with ownership, which is worth having even without the AI.

What about permissions — can AI respect our access controls?

It must, and it can: source-system permissions are mapped into the retrieval layer and enforced per-query, so users only get answers from documents they could open themselves. If your permissions are currently tribal knowledge, making them explicit is part of the work — and the audit will say so.

Related work.

These usually travel together.
Service / 05
Knowledge systems
Your documents, wikis, tickets, and contracts turned into one queryable, source-cited system that finally answers back — with the receipts.
Explore
Service / 02
RAG systems
Retrieval pipelines that put your institutional knowledge to work. Grounded, source-cited answers on your own data — without leaking it.
Explore
Service / 09
AI strategy & readiness
Assessment, roadmap, and business case before you build — so the first thing you ship is the right thing, with a number attached.
Explore
Service / 14
Document processing
Contracts, claims, invoices, and forms extracted, classified, and routed automatically — with accuracy you can audit.
Explore
Service / 16
AI security
Prompt-injection defense, red-teaming, and model risk controls — the attack surface nobody scoped when the pilot got approved.
Explore
Service / 08
Digital transformation
End-to-end process modernization — not a rip-and-replace. Every change tied to a number, so you know it worked.
Explore

Fix the foundation first.

If your last AI pilot underwhelmed, the model probably wasn't the problem. Two weeks of data assessment will tell you what was.

Let's talk

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