Role
Product Designer
Responsibilities
Branding, Design System, Low and High fidelity prototyping Usability testing, visuals
Research- Amrita University
Tools
Figma, Miro, Bolt AI, Claude AI, Chat GPT
While global governments create AI safety frameworks, existing solutions are generic and fail to capture India's diversity of languages, cultures, and socio-economic need
Track LLM is a context-aware evaluation framework that enables developers, testers, and GRC teams to evaluate their LLM applications for bias, fairness, toxicity, and truthfulness, specifically within India's diverse context.
Problem Space
Problem Space Research
Global AI Safety Context:
Governments worldwide creating AI safety frameworks
Focus on risk assessments, bias checks, accountability
Generic solutions don't address India's unique context
India-Specific Challenges Identified:
Linguistic Diversity: 22+ scheduled languages underrepresented in LLMs
Cultural Context: Caste, religion, regional biases not addressed
Socio-Economic Disparity: Rural vs urban, economic class variations
Domain Criticality: Healthcare, Finance, Education require highest safety
Key Insight- Existing LLM evaluation tools test models (e.g., 'Is GPT-5 biased?') but don't evaluate real-world applications (e.g., 'Is my Tutoring Bot safe for Indian students?')
Tools Analyzed:
HELM, Latitude, LangWatch, LM Eval Harness
OpenAI Evals, PromptBench, MT-Bench
Competitive Advantage Identified:
First platform to evaluate LLM applications vs. models
Domain-specific risk assessment for India
Actionable analytics that show how to fix problems
Pain Points Discovered:
Developers:
"We don't know if our chatbot is safe to deploy"
"Generic bias tests don't catch India-specific issues"
"We need domain-specific evaluation for healthcare apps"
"Current tools are too technical for our team"
GRC Teams:
"Need compliance documentation for AI governance"
"Can't explain AI risks to non-technical stakeholders"
"Lack standardized evaluation frameworks for India"
Researchers:
"No datasets covering Indian cultural contexts"
"Western benchmarks miss caste, regional biases"
"Need reproducible evaluation methodology"
Based on the technical architecture, the system has 5 core components:
Track LLM Interface - User-facing web application
Recommendation Engine - Suggests domain-specific evaluations
Evaluation Engine - Runs tests against LLM apps
Dataset Repository - Indigenous Indian datasets
Results Dashboard - Analytics and reporting
Track LLM Color System
A modern, accessible color palette for India's AI evaluation platform
Primary Colors
Primary Lavender
#A7ABF6
Main brand color for headers, navigation, primary actions, and key UI elements.
Represents innovation and approachability.
✓ WCAG AA
Accent Olive
#819337
Main brand color for headers, navigation, success states, positive metrics. Represents growth, balance, and natural intelligence.
✓ WCAG AA
Semantic Colors
Success Green
7FD798
Grade A, excellent performance,
completed states, positive outcomes.
Warning
#FFD19C
Grade B/C, moderate risk, areas
needing attention and review.
Critical Red
#F55E5E
Grade D/F, critical issues, errors
requiring immediate attention.
Information Blue
#7C7EEB
Informational alerts, neutral highlights.
Derived from primary lavender.