Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Birmingham
Build trustworthy AI and data systems for regulated and high-trust systems in Birmingham.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make. In Birmingham, this connects directly to cloud modernization sequenced around operational continuity.
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 Birmingham
UK organizations often operate across regulated services, digital commerce, research and industrial supply chains where reliability and clear technical accountability matter together.
Birmingham's manufacturing, automotive, logistics and professional-service economy depends on long-lived systems as well as new digital products. Incremental modernization can reduce risk while improving data flow.
Automotive, advanced manufacturing, logistics, construction and professional services depend on connected systems and controlled change.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make.
- Data evidence
- Integration patterns for industrial and enterprise platforms
- Evaluation boundary
- Reliable delivery around supply-chain and mobility workflows
- Production decision
- Cloud modernization sequenced around operational continuity
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
Work can be structured as targeted consulting, owned project delivery or added engineering capacity that fits established UK governance and review cycles. For AI Data & Evaluation, the initial scope should stay anchored to cloud modernization sequenced around operational continuity.
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 Birmingham?
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 cloud modernization sequenced around operational continuity.
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 Birmingham.
Start with cloud modernization sequenced around operational continuity and the technical or organizational boundary that makes it difficult today.