AI Data & Evaluation
AI Evaluation Coverage Is Not Usage Frequency
A test set can cover important failures without representing everyday usage. Learn how sampling choices change what an AI evaluation result can legitimately tell you.
AI data and evaluation practice
Connect AI data work without losing the acceptance boundary
For teams in Germany, an AI data workflow needs more than completed tasks. Prepared inputs must suit annotation; reviewed judgments must suit evaluation. XIVTech connects these capabilities around explicit exchanges, with scope and access agreed before work starts.
What this practice covers
Specify what the next workstream can actually use
Start between activities, not inside them. Record the required input shape, quality criteria and exceptions at each exchange. An accepted annotation output is not automatically a suitable evaluation reference: its labels may answer a different question or omit the context a reviewer needs.
What that gives your team
- Interface map
- Exchange acceptance records
- Dependency register
Interface map
Identify which workstream supplies each dataset, label or finding and which owner receives it.
Exchange acceptance records
Define required fields, criteria versions and exception handling for each handover.
Dependency register
Separate blocked exchanges, missing inputs and decisions needed before downstream work proceeds.
How it works
Follow one example through the chain
Interface checks add coordination work. They do not establish model performance or require commissioning every workstream. Retain existing stages that meet the receiving criteria.
01
Inventory the exchanges
Bring the task, available datasets, current workflow and receiving owners. Agree what each exchange needs to preserve.
02
Inspect a connected path
Follow an example through preparation, human judgment and evaluation. Locate incompatible assumptions rather than rebuilding every stage.
03
Assign acceptance
Name the receiving owner and record how rejected outputs, exceptions and changed requirements return to the supplying workstream.
Engagement models
Choose how we work together
Choose the working shape that best fits your ai data & evaluation priorities and team.
ConsultingADVISORY
Opinions, reviews, and focused direction.
ExploreStaff AugmentationEMBEDDED
Ongoing capacity in your engineering team.
ExploreOn-Call SupportON-CALL
Incidents, rotations, and production response.
ExploreOutsourcingDELIVERY
Roadmaps with clear delivery ownership.
ExploreSolutionsPROJECT
Plan and deliver a defined technical outcome.
ExploreSupportCARE
Ongoing engineering care and improvement.
ExploreQuestions answered
Accepting connected AI data work
A short set of practical questions to clarify the first conversation.
Must every stage be rebuilt?
No. Retain suitable existing stages and examine their exchanges. A missing field at a handover may need a narrower change than replacing the workflow.
Who accepts an intermediate dataset?
The named receiving owner uses criteria agreed for the dataset's intended use. Completing the supplier's task does not by itself establish downstream suitability.
Does acceptance cover later uses?
Not automatically. A new evaluation question may require different examples, context or review, even when the original dataset was accepted for annotation.
Next step
Make AI quality measurable
Bring the evaluation question, data workflow or feedback loop that is limiting the next production decision.
