Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice
Make AI data and evaluation decisions traceable.
For Canadian teams comparing AI data services, start with the decision the work needs to support. XIVTech's data and evaluation practice connects training data, annotation, human feedback and application-specific quality checks. Choose a bounded workflow, its acceptance evidence and the team that will own it after handoff.
What this practice covers
Choose the workflow before choosing a tool
An annotation pipeline, a preference dataset and a regression suite answer different questions. Agree which inputs, reviewer decisions and evaluation outputs belong in scope before choosing software or treating an aggregate score as a release decision.
What that gives your team
- Dataset preparation
- Annotation and human feedback
- Evaluation infrastructure
Training data pipelines
Prepare and transform text, image, audio and multimodal data for controlled use.
Annotation workflows
Design labeling, review, calibration and adjudication around quality criteria.
LLM and agent evaluation
Create rubrics, datasets and regression workflows for application behavior.
Human feedback
Capture comparisons, demonstrations and preference signals in structured form.
Data quality
Make disagreement, review outcomes and quality thresholds visible.
How it works
From the quality question to reviewable evidence
Agree inputs, responsibilities and acceptance criteria before implementation. Delivery capacity, access and any continuing review are confirmed for the particular engagement.
01
Define the decision and permitted data
Bring a task definition, authorised sample data, known failure examples and access constraints. Name the owner who can decide whether the proposed scope is useful.
02
Build reviewable evidence
Record dataset and rubric versions, sampling limitations, reviewer roles and reproducible run settings. Keep preparation, annotation and evaluation responsibilities explicit.
03
Accept and transfer the workflow
Review failure cases, decision records and known coverage gaps. Agree the receiving owner and any separately scoped continuing review before handoff.
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
AI Data & Evaluation questions, answered
A short set of practical questions to clarify the first conversation.
Does evaluation guarantee model accuracy?
No. An evaluation describes behaviour against a particular dataset and set of criteria. Coverage gaps, changing inputs and reviewer disagreement must remain visible. A result is evidence for an agreed decision, not a guarantee about every future input.
What should a useful engineering handoff include?
Record dataset and criteria versions, run settings, failure cases, reviewer decisions and known limitations. Name the receiving owner and distinguish completed implementation from any separately agreed continuing review or support.
How is work for a Canadian team scoped?
XIVTech's Canadian data-quality and evaluation offering covers criteria, calibration, review and versioned evidence. Confirm project availability, communication hours, authorised data access and delivery arrangements before starting. A city audience does not imply a local office, on-site staff or a previous local client.
Next step
Make AI quality measurable
Bring the evaluation question, data workflow or feedback loop that is limiting the next production decision.
