Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Los Angeles
Build trustworthy AI and data systems for customer-facing digital products in Los Angeles.
AI and data systems should evaluate quality against real customer journeys, edge cases and product outcomes rather than relying on aggregate model scores alone. In Los Angeles, this connects directly to cloud cost and performance controls for variable demand.
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 Los Angeles
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.
Los Angeles organizations often combine rich customer experiences with content pipelines, commerce, mobility or logistics. That mix rewards platforms that support experimentation while keeping performance, cost and operational ownership visible.
Entertainment, digital media, retail, aerospace, logistics and consumer technology create distinct needs around content, throughput and connected experiences.
AI and data systems should evaluate quality against real customer journeys, edge cases and product outcomes rather than relying on aggregate model scores alone.
- Data evidence
- Scalable media and customer-experience delivery paths
- Evaluation boundary
- Cloud cost and performance controls for variable demand
- Production decision
- Reliable integration across commerce, content and operational systems
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 cloud cost and performance controls for variable demand.
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 changing customer demand shape AI Data & Evaluation work in Los Angeles?
AI and data systems should evaluate quality against real customer journeys, edge cases and product outcomes rather than relying on aggregate model scores alone. Scope should begin with the systems, owners and evidence connected to cloud cost and performance controls for variable demand.
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 Los Angeles.
Start with cloud cost and performance controls for variable demand and the technical or organizational boundary that makes it difficult today.