
DataUpdated 12 min read
Data readiness before an LLM pilot
LLM pilots stall on messy ownership, not model choice. Fix sources, quality, and access before you buy seats.
data readinessLLMgovernancepilotquality
Reckap Team
Source map
For each field the LLM needs, name the system of record and the human owner. If two systems disagree, fix that before prompting.
Quality rules
Completeness, freshness, and allowed nulls. Write them down. Pilots that skip quality end up debugging prompts that were really data bugs.
Access and retention
Who can grant access, how logs work, and when pilot data is deleted. That belongs in the charter, not a side chat.
Where Reckap fits
See governance with the same handoff standard we use across IT, AI, and ops work.
- Write the decision and success metric
- Scope systems, owners, and allowlists
- Deliver QA evidence and runbooks
- Name the day-two support window
See the service page for scope examples, then book a call if the fit is clear.
View related servicesFAQ
- How long does readiness take?
- Depends on how many systems own the fields you need. A narrow pilot can be weeks; a sprawling data estate is longer and should be phased.
- Do you train models on our data?
- Only when contracted and controlled. Default is to keep your data in systems you run with explicit permissions.
