AI Data Engineering
The Data & Evaluation Layer for Production AI in Doha
Engineer the data and evaluation layer around production AI. XIVTech builds training-data workflows, LLM and agent evaluations, human feedback loops and quality infrastructure so teams can trace data decisions and diagnose system behavior.
- Training datasets
- LLM and agent evaluation
- Human feedback
Capabilities for AI data engineering
Training datasets
Preparation and annotation with quality controls.
LLM and agent evaluation
Application-specific tests and failure analysis.
Human feedback
Structured preferences, scores and expert judgments.
Quality infrastructure
Calibration, review, adjudication and traceability.
Buyer challenges
What makes AI data engineering work necessary?
Start with the constraints affecting AI data requirements, evaluation criteria and feedback loops, then choose the work that addresses them.
Data quality is hard to explain
Dataset decisions and review findings are disconnected.
Evaluation misses application behavior
A single score hides retrieval, reasoning or tool-use failures.
Feedback is difficult to reuse
Human judgments lack consistent criteria and structured outputs.
From the first discussion to reviewed delivery
Shape the engagement around AI data requirements, evaluation criteria and feedback loops, with scope and technical responsibilities agreed before implementation.
Clarify the challenge
Review AI data requirements, evaluation criteria and feedback loops and the decisions your team needs to make.
Agree the work plan
Identify the first AI data engineering tasks, dependencies and acceptance criteria with your team.
Implement with reviews
Deliver the agreed changes and review representative evaluate behavior work before expanding adoption.
Validate and hand over
Review data workflow configuration and the remaining operating responsibilities together.
Working practices throughout delivery
Visible technical progress
Use demonstrations and written updates to review AI data engineering work and surface blockers.
Documented decisions
Record choices affecting AI data requirements, evaluation criteria and feedback loops so the team retains the implementation context.
Explicit scope changes
Discuss changed priorities and their delivery implications before proceeding with additional work.
Where AI data engineering fits
AI data engineering connects the data used to train a system with the evaluations and feedback used to improve it. Dataset preparation, human review and application-specific evaluation need shared quality definitions without being collapsed into one model score.
The service workflow
Define requirements
Identify data and application quality needs.
Prepare data
Structure datasets and review tasks.
Evaluate behavior
Run criteria-based application and model checks.
Capture feedback
Record human judgments and disagreement decisions.
Improve & repeat
Version findings and feed the next data or evaluation cycle.
Data preparation and annotation systems
Pipelines and interfaces support controlled dataset work.
Evaluation harnesses
Rubrics and test datasets capture application-specific behavior.
Reviewers and quality practices
Calibration and adjudication connect judgments to reusable findings.
Useful outputs and practical value
The deliverables follow the agreed AI data engineering scope; priorities are reviewed against your current environment.
Traceable data operations
Connect definitions and reviewer decisions with dataset versions.
Data workflow configurationDiagnosable evaluation results
Categorize failures around application requirements.
Evaluation datasets and reportsReusable improvement feedback
Structure human judgments for subsequent data and evaluation work.
Feedback schemas and review guidance
Ways to work on AI data engineering
Choose a working arrangement around the decision or implementation work needed, rather than a list of tools.
Project-based data or evaluation initiative
Deliver a defined dataset, pilot or evaluation program.
When it fits: A bounded AI data requirement needs implementation.
Embedded data engineering collaboration
Work against a recurring data and evaluation backlog.
When it fits: The AI team has continuing engineering requirements.
Managed AI data operations
Manage agreed preparation, review and reporting workflows.
When it fits: The operational data layer needs sustained coordination.
Scope, responsibilities and delivery expectations are agreed before work begins.
Delivery standards you can review
These are AI data engineering delivery practices, not customer endorsements or measured customer outcomes.
- Delivery standard
Define quality before production
Establish criteria and examples before scaling data work.
Supports: Consistent review decisions
- Delivery standard
Record versions and findings
Trace changes to datasets, rubrics and evaluation outputs.
Supports: Repeatable improvement
- Delivery standard
Review ambiguous cases
Use explicit escalation and adjudication practices.
Supports: Auditable human judgment
Keep exploring
Follow the related services, technical context and working practices around AI data engineering.
Related services
Tools and practices
Industry context
Resources and working practices
Questions about AI data engineering
Clarify scope, fit and adoption before choosing the next step.
What can AI data engineering cover?
The offering includes training datasets, LLM and agent evaluation, human feedback, quality infrastructure. Discovery determines which capabilities address your priorities and belong in the agreed scope.
Can this work fit our existing environment?
We review AI data requirements, evaluation criteria and feedback loops before recommending changes. Existing systems and practices inform the approach and implementation boundaries.
How do we choose the first piece of work?
Deliver a defined dataset, pilot or evaluation program. Discovery identifies the dependencies and acceptance criteria needed to agree the first milestone.
What will our team receive at handoff?
Handoff follows the agreed scope and can include data workflow configuration, evaluation datasets and reports, feedback schemas and review guidance. Remaining responsibilities are reviewed with your team.
AI Data Engineering
Discuss your AI data engineering needs
Tell us about AI data requirements, evaluation criteria and feedback loops and the next decision or improvement your team needs to make.