Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / New York
Build trustworthy AI and data systems for regulated and high-trust systems in New York.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make. In New York, this connects directly to traceable data and access controls across regulated workflows.
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 New York
Organizations operating in the United States often need engineering systems that can span large customer bases, distributed teams and varied regulatory obligations without fragmenting delivery ownership.
New York teams frequently connect customer-facing products to regulated data, time-sensitive transactions and mature enterprise platforms. The engineering challenge is often coordination across those boundaries rather than a single isolated technology choice.
Financial services, media, healthcare, commerce and professional services each place different demands on latency, auditability and release confidence.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make.
- Data evidence
- Traceable data and access controls across regulated workflows
- Evaluation boundary
- Observable services that explain failures during high-demand periods
- Production decision
- Delivery paths that let product teams move without weakening controls
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 begin with a focused technical decision, expand into a defined delivery outcome or add experienced capacity around an existing US-based team. For AI Data & Evaluation, the initial scope should stay anchored to traceable data and access controls across regulated workflows.
ConsultingADVISORY
Opinions, reviews, and focused direction.
ExploreStaff AugmentationEMBEDDED
Ongoing capacity in your engineering team.
On-Call SupportON-CALL
Incidents, rotations, and production response.
OutsourcingDELIVERY
Roadmaps with clear delivery ownership.
SolutionsPROJECT
Plan and deliver a defined technical outcome.
ExploreSupportCARE
Ongoing engineering care and improvement.
Questions 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 New York?
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 traceable data and access controls across regulated workflows.
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 New York.
Start with traceable data and access controls across regulated workflows and the technical or organizational boundary that makes it difficult today.