Training data with provenance
Prepare and annotate datasets with criteria, reviewer workflows and traceable decisions.
AI data and evaluation practice
AI Data & Evaluation
XIVTech engineers the workflows around training data, human feedback, LLM and agent evaluation, and quality controls so AI teams can understand and improve system behavior.
Delivery context
Production AI depends on data workflows and evaluation systems that make quality decisions repeatable, inspectable and useful to engineers.
- Training data with provenance
- Evaluation that can regress
- Feedback loops that improve
S / 000 Credibility and delivery context
A practice grounded in the systems your team owns
Production AI depends on data workflows and evaluation systems that make quality decisions repeatable, inspectable and useful to engineers.
Traceable data decisions
Repeatable evaluation workflows
Quality controls built into the pipeline
S / 001 Category coverage
What AI Data & Evaluation covers
Production AI depends on data workflows and evaluation systems that make quality decisions repeatable, inspectable and useful to engineers.
Evaluation that can regress
Turn application-specific quality questions into datasets, rubrics and repeatable runs.
Feedback loops that improve
Structure human judgment and disagreement so findings can guide system changes.
S / 002 Capability areas
Engineering work with a clear boundary
The practice connects decisions, implementation and operating context so the result can be maintained by the team that owns it.
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.
Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
S / 003 Workflow
How AI data work moves
The workflow connects quality criteria to the people, data and engineering decisions that use the result.
Define
Translate the quality question into criteria, rubric and decision boundaries.
Build
Create datasets, annotation paths or evaluation runs with traceable versions.
Review
Calibrate reviewers, handle disagreement and inspect quality signals.
Improve
Feed findings back into prompts, models, workflows or the next evaluation cycle.
Ways to work with us
Bring in AI Data & Evaluation the way that fits
The same technical practice can be engaged through different responsibility and delivery shapes. Choose the model that matches the work, ownership and stage you are navigating.
Consulting
AI Data & Evaluation Consulting
Consulting for AI data and evaluation systems: work across data pipelines, evaluation harnesses, quality and annotation workflows with a category-aware engineering path. Scope, ownership and acceptance are agreed with the client before work begins.
Staff Augmentation
AI Data & Evaluation Staff Augmentation
Staff Augmentation for AI data and evaluation systems: work across data pipelines, evaluation harnesses, quality and annotation workflows with a category-aware engineering path. Detailed engagement terms are confirmed during scoping; this page does not promise staffing, coverage, response time or service levels.
On-Call Support
AI Data & Evaluation On-Call Support
On-Call Support for AI data and evaluation systems: work across data pipelines, evaluation harnesses, quality and annotation workflows with a category-aware engineering path. Detailed engagement terms are confirmed during scoping; this page does not promise staffing, coverage, response time or service levels.
Outsourcing
AI Data & Evaluation Outsourcing
Outsourcing for AI data and evaluation systems: work across data pipelines, evaluation harnesses, quality and annotation workflows with a category-aware engineering path. Scope, ownership and acceptance are agreed with the client before work begins.
Availability confirmed during scoping
Solutions
AI Data & Evaluation Solutions
Solutions for AI data and evaluation systems: work across data pipelines, evaluation harnesses, quality and annotation workflows with a category-aware engineering path. Scope, ownership and acceptance are agreed with the client before work begins.
Support
AI Data & Evaluation Support
Support for AI data and evaluation systems: work across data pipelines, evaluation harnesses, quality and annotation workflows with a category-aware engineering path. Scope, ownership and acceptance are agreed with the client before work begins.
Unavailable
S / 005 Comparison
Choose the shape of AI data help
Use the category to frame the data and evaluation layer, then follow the existing service that matches the workflow.
DimensionConsulting
Scope and outcomeAI Data & Evaluation work shaped around the agreed outcome and operating context.
CollaborationAI Data & Evaluation work shaped around the agreed outcome and operating context.
Change handlingAI Data & Evaluation work shaped around the agreed outcome and operating context.
Best fitAI Data & Evaluation work shaped around the agreed outcome and operating context.
S / 006 Delivery standards
Standards that make the result usable
These practices keep engineering decisions legible after the engagement ends.
Data and evaluation workflows state what is being judged and why.
Reviewer and adjudication paths preserve context without pretending the process is automatic.
Evaluation outputs are structured for engineering follow-through.
S / 007 Keep exploring
Related XIVTech capabilities
Start from the practice, then move into the existing service and technology detail that supports it.
S / 008 FAQ
Questions teams ask before starting
Is this model training or data engineering?
The category focuses on the data, feedback and evaluation infrastructure around AI systems; model training is only in scope where it is part of that evidenced workflow.
Can you evaluate an existing LLM or agent?
The existing LLM and Agent Evaluation Systems service supports application-specific criteria, datasets, regression runs and actionable findings.
How do you handle reviewer disagreement?
Calibration, review and adjudication are explicit workflow concerns rather than hidden quality assumptions.
Is AI Data On-Call Support available?
No. The model is drafted for architectural completeness but remains nonpublic and nonroutable.
S / 009 Next step
Bring the ai data & evaluation question into focus.
Bring the evaluation question, data workflow or feedback loop that is limiting the next production decision.
Discuss AI Data & Evaluation