Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Madrid
Build trustworthy AI and data systems for regulated and high-trust systems in Madrid.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make. In Madrid, this connects directly to governed cloud foundations for enterprise and regulated workloads.
What this practice covers
What AI Data & Evaluation covers
Production AI depends on data workflows and evaluation systems that make quality decisions repeatable, inspectable and useful to engineers.
AI Data & Evaluation priorities in Madrid
Organizations in Spain are modernizing customer services, industrial operations and public-facing platforms while working within European data, security and accessibility expectations.
Madrid's concentration of corporate, financial, public and technology activity creates systems with many organizational boundaries. Platform consistency and clear data ownership reduce friction across them.
Finance, telecoms, aerospace, mobility, media, public services and life sciences create varied requirements for data, resilience and integration.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make.
- Data evidence
- Governed cloud foundations for enterprise and regulated workloads
- Evaluation boundary
- Data and AI delivery with measurable quality and lineage
- Production decision
- Integration patterns for national and international operations
What that gives your team
- Training data with provenance
- Evaluation that can regress
- Feedback loops that improve
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
How AI data work moves
The workflow connects quality criteria to the people, data and engineering decisions that use the result.
01
Define
Translate the quality question into criteria, rubric and decision boundaries.
02
Build
Create datasets, annotation paths or evaluation runs with traceable versions.
03
Review
Calibrate reviewers, handle disagreement and inspect quality signals.
Engagement models
Choose how we work together
The working model can align a specific platform outcome with local decision makers while keeping delivery legible to wider European stakeholders. For AI Data & Evaluation, the initial scope should stay anchored to governed cloud foundations for enterprise and regulated workloads.
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.
How should governance and audit needs shape AI Data & Evaluation work in Madrid?
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make. Scope should begin with the systems, owners and evidence connected to governed cloud foundations for enterprise and regulated workloads.
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.
Next step
Frame the next AI data and evaluation step in Madrid.
Start with governed cloud foundations for enterprise and regulated workloads and the technical or organizational boundary that makes it difficult today.