Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Mumbai
AI and data evaluation in Mumbai for reliable business outputs
Assess whether an AI answer preserves the facts that a business workflow depends on. XIVTech helps Mumbai teams evaluate extracted data, generated summaries and LLM responses against controlled examples, explicit error severity and human acceptance criteria.
What this practice covers
Separate plausible output from correct business information
For Mumbai teams assessing finance, commerce or customer-service applications, a fluent answer may still contain a wrong amount, date or product condition. Scope evaluation around the actual workflow and permitted data. Use redacted or synthetic records where appropriate, and keep business approval and compliance decisions with the client.
What that gives your team
- Field-level accuracy checks
- Risk-weighted output review
- Comparable model benchmarks
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
From a quality question to reviewable 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.
01
Select representative business cases
Choose a bounded extraction, summary or support task. Agree authorised examples, expected facts and error severity with the business owner before designing the benchmark.
02
Validate data and test outputs
Check source completeness and training-data overlap. Run field checks and human review on ambiguous outputs; distinguish source defects from model or retrieval failures.
03
Publish a decision-ready report
Show accuracy by field and case type, unresolved reviews and failed runs. Document acceptance limits and a regression set for subsequent model or prompt changes.
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.
Does a high benchmark score make an AI workflow safe for financial decisions?
No. A benchmark describes performance on specified cases under recorded conditions. Review consequential errors, coverage gaps, human approval and operational controls separately. Evaluation does not replace the client's financial, legal or compliance responsibilities.
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.