Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / New Delhi
AI and data evaluation in New Delhi for accountable decisions
Make AI quality decisions inspectable across technical reviewers and business stakeholders. XIVTech helps New Delhi teams connect data validation, LLM testing and human review so an accepted answer can be traced to its source, rubric and reviewer decision.
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.
Turn review requirements into testable evidence
Agree authorised data access, reviewer responsibilities and IST collaboration windows before work begins. Delivery is remote-first; a city page does not imply a local office or on-site staff. Evaluation provides bounded evidence, not a guarantee of accuracy or regulatory compliance.
For New Delhi organizations evaluating institutional knowledge assistants or public-facing information services, the same question can require different evidence across languages and audiences. Start with approved source material and representative English, Hindi or other required language cases. Confirm language-review capacity and acceptance authority rather than assuming one benchmark covers every user.
The scope follows your actual users, data rights and application risks, not assumptions based on a city or sector.
Define the evidence needed for the next quality decision before expanding the work.
- Source-grounded answer checks
- Test whether an answer is supported by approved, current documents. Distinguish an incorrect answer from missing context or a source that cannot answer the question.
- Language-specific data validation
- Inspect missing fields, duplicates and label consistency by language and document type. Preserve coverage gaps instead of hiding them in a single aggregate score.
- Human review with an exception path
- Define reviewer instructions, disagreement handling and escalation for uncertain answers. Retain the rubric version and decision rationale without exposing sensitive source content.
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
Start with a bounded assessment or implementation scope, authorised examples and an accepting owner. Confirm reviewer capacity and ongoing maintenance separately.
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.
Can one English benchmark validate a multilingual assistant in New Delhi?
Not on its own. Select cases in the languages and source formats the application will encounter, with reviewers able to assess them. Compare coverage, source support and failure types separately. Translation alone does not establish equivalent meaning or answer quality.
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
Scope your AI quality evaluation
Bring a redacted quality issue, existing evaluation criteria and a data or application owner. Agree an inspectable first output without sharing credentials or customer records.