Human Intelligence for Better AI.
AI Evaluation, RLHF, Human Feedback, Data Annotation, and Quality Assurance for modern AI teams.
Co-founders personally oversee every project baseline — not delegated to account managers.
Why AI Teams Choose YUG AI
What separates a quality partner from a data vendor.
Structured QA Framework
3-tier review — Annotator → Reviewer → QA Lead — with gold sets, IAA tracking, and full audit logs on every batch.
Founder-Led Delivery
Leadership is directly involved in every engagement. Your project is never handed off to junior managers.
Pilot-First Engagements
We earn trust through results before production commitments. Every relationship starts with a scoped pilot on your actual data.
Transparent Reporting
Every delivery includes a QA report, IAA metrics, and reviewer notes. No black boxes, no hollow metrics.
Evaluation-Focused Operations
AI evaluation and RLHF are the core of what we do — not a side service tacked on to a general annotation shop.
Human Feedback Expertise
Deep expertise in preference pair collection, calibration, and feedback pipeline design across expert domains.
Services built for scale.
End-to-end human data operations for frontier AI teams — from taxonomy design to production-scale evaluation.
Designed For
Who We Work With.
We partner with teams that take data quality seriously and need a human intelligence operation they can trust.
Language Model Development
LLM Teams
Building, fine-tuning, or aligning large language models. You need preference data, evaluation benchmarks, and safety annotation — at consistent quality and throughput.
Early-Stage AI Companies
AI Startups
Moving fast on a tight runway. You need a reliable human data partner who doesn't require enterprise contracts and can start with a focused pilot.
AI-Integrated Products
AI Product Organizations
Shipping AI features to real users. You need ongoing human evaluation, red teaming, and feedback loops to keep model behavior aligned with product expectations.
Internal Data Teams
Data Operations Teams
Running annotation workflows in-house but need a trusted QA layer, reviewer capacity, or structured process for edge case handling and audit trails.
Academic & Applied Research
Research Labs
Publishing benchmarks, studying model behavior, or annotating specialized corpora. You need domain-calibrated annotators and reliable inter-annotator agreement.
Pilot → Validate → Scale
We earn your trust before you commit. Every engagement starts with a risk-free pilot on your real data — no commitments, no guesswork.
Discovery Call
30-minute scoping session to understand your use case, data type, volume, and quality requirements.
Pilot Project
We annotate or evaluate a representative sample of your actual data through our full 3-tier QA stack.
Quality Review
You receive a complete QA package: quality report, IAA metrics, reviewer notes, and recommendations.
Production Engagement
Satisfied with pilot quality? We scale to your full production volume with the same standards.
What you receive from every pilot.
Every pilot delivery includes a full documentation package — so you can evaluate quality independently and make an informed decision before any production commitment.
Our Standard
Every Project
Includes.
These six steps aren't optional. Every engagement — regardless of size — runs this same structured pipeline.
"We built this process after seeing what goes wrong when annotation teams skip steps. The methodology isn't overhead — it's the product."
— YUG AI Co-Founders
Guideline Design
Taxonomy definition and detailed annotation instructions authored before any production work begins.
Calibration Sessions
Annotator alignment training run at project start and periodically throughout to prevent drift.
Production Annotation
Specialist annotators execute labeling under your taxonomy with continuous self-review at source.
Multi-Tier Review
Every submission reviewed by a senior reviewer before it advances to QA for final validation.
QA & Measurement
Gold sets, IAA tracking, and error-rate measurement applied to every batch before delivery.
Delivery Reporting
Export-ready datasets shipped with a QA report, IAA metrics, reviewer notes, and full audit trail.
How YUG AI Delivers Quality.
Every project follows the same structured, traceable pipeline — no shortcuts, no black boxes.
Taxonomy
Define label schema, entity types, and classification hierarchies tailored to your model's needs.
Guidelines
Author detailed annotation instructions with examples, edge cases, and calibration items.
Calibration
Align annotators through guided calibration sessions before any production work begins.
Annotation
Execute labeling at scale with trained specialists following your taxonomy and guidelines.
Review
Senior reviewer inspects every annotator submission for errors and guideline compliance.
QA
QA Lead validates the batch, measures inter-annotator agreement, and reviews gold set performance.
Delivery
FinalExport-ready datasets with QA report, IAA metrics, reviewer notes, and full audit documentation.
Zero-Noise
QA Architecture.
Founder-Led Delivery
Strategic oversight on every project baseline.
IAA Tracking
Inter-annotator agreement metrics on every batch.
3-Tier Review
Annotator → Senior Reviewer → QA Lead.
Annotator
Trained specialist executes labeling per taxonomy and guidelines. Self-reviews before submission to ensure quality from the source.
- Labeling per client taxonomy
- Self-review before submission
- Calibration trained per project
Reviewer
Senior reviewer checks every annotator submission for errors, edge cases, and guideline adherence before it advances.
- 100% submission coverage
- Edge case detection
- Guideline enforcement
QA Lead
QA Lead validates the full batch, measures IAA, reviews gold set performance, and authorises final delivery.
- IAA measurement & reporting
- Gold set performance review
- Final delivery sign-off
Tier 1 — Annotator
Every batch is measured, not assumed.
Our QA framework goes beyond manual review. Systematic measurement checkpoints run on every batch so quality is provable, not just claimed.
Gold Sets
Pre-labeled items seeded into every batch
IAA Tracking
Inter-annotator agreement on every delivery
Reviewer Audits
100% of annotator work reviewed
Escalation Process
Clear path for edge cases and disagreements
Audit Trails
Full log of every annotation and QA action
Gold Sets
Pre-labeled reference items seeded into production batches to continuously assess accuracy.
Calibration
Regular alignment sessions before and during projects to keep annotators consistent.
IAA Tracking
Inter-annotator agreement measured on every batch. Disagreement surfaces guideline gaps.
Audit Logs
Full traceability of every annotation action, QA decision, and revision on request.
The team behind YUG AI.
People buy from people. We believe in being transparent about who we are.
Krishna Samrat Bajpai
Co-Founder
Priyesh Singh
Co-Founder
Abhishek Singh
Co-Founder
Shreshth Bajpai
Co-Founder
Frequently Asked Questions
Everything you need to know before getting started.
Enterprise Security.
Ready to
Build Better?
Start with a scoped pilot on your actual data. No long-term contract, no onboarding overhead.