Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Bangalore
AI and data evaluation in Bangalore for repeatable product tests
Give model and application changes a repeatable quality check before release. XIVTech helps Bangalore product and platform teams build evaluation datasets, LLM regression tests and feedback workflows that explain what changed, which cases failed and what remains uncertain.
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.
Make AI experiments comparable across releases
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 Bangalore SaaS and AI product teams iterating across prompts, retrieval and model providers, a score is useful only when comparison conditions are stable. Separate development examples from held-out evaluation cases, version the application configuration and retain failure evidence that engineers can reproduce.
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.
- Versioned model and LLM benchmarks
- Record dataset, rubric, prompt, model and retrieval versions for each run. Compare answer accuracy, task completion and reliability on the same representative cases.
- Training-data evaluation
- Inspect provenance, duplicate and near-duplicate records, label balance and overlap with test cases. Flag uncertain lineage rather than declaring an untracked dataset clean.
- Actionable regression evidence
- Connect failed cases to engineering review, with traceable inputs and expected behaviour. Calibrate automated judges against human review before relying on their scores.
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 automated LLM judges replace human review for Bangalore product teams?
They can help scale repeatable checks, but their decisions need validation against the task and a reviewed sample. Track disagreement, judge versions and ambiguous cases. Retain human adjudication for consequential or disputed outputs instead of treating judge scores as ground truth.
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.