The Problem
English Benchmarks Don't Test Real Competence
Translation is Not Evaluation
Hindi benchmarks translated from English test translation ability, not language understanding. Real evaluation needs native test sets.
Code-Mixing Ignored
Real Indians speak Hinglish, Tanglish, Benglish. Current benchmarks only test pure languages that nobody actually uses.
Hallucination Detection Fails
Existing tools can't detect hallucinations in vernacular content. Fabricated names, places, and facts go unnoticed.
Cultural Context Missing
Does the model understand Indian festivals, social dynamics, regional customs? No benchmark tests for this.
Live Demo
Language Evaluation Dashboard
See how different models perform on Indian language tasks. This simulation shows real evaluation metrics.
Languages
22 Languages, Comprehensive Coverage
North & Central India
South & East India
Northeast & Classical
Capabilities
What We Evaluate
Six dimensions of Indian language AI competence.
Native Fluency
Does the output read like it was written by a native speaker? Not translated English, but natural vernacular.
Code-Mixed
Hinglish, Tanglish, Benglish. Script switching. Transliteration. How people actually communicate.
Factual Accuracy
Verify claims about Indian geography, history, current events, and institutions. Catch fabrications.
Cultural Context
Festivals, customs, social dynamics, regional practices. Does the AI understand India, not just Indian languages?
Domain Expertise
Legal, medical, government, education domains. Technical vocabulary and domain-specific accuracy.
Safety & Sensitivity
Communal sensitivity. Political neutrality. Harmful content detection calibrated for Indian context.
Code-Mixed
How People Actually Communicate
| Mix Type | Example |
|---|---|
| Hinglish | "Mujhe ek meeting schedule karni hai tomorrow afternoon" |
| Tanglish | "Naan office-ku late-a vanthen because of traffic" |
| Benglish | "Ami tomar email-ta receive korechhi" |
| Script Switch | "आज office में meeting है, will be late" |
Why Code-Mixed Matters
68%
of urban Indian digital communication is code-mixed. Pure language AI misses the majority of real usage. Test what matters.
How It Works
From Submission to Report
Submit
Connect your model via API or upload responses
Evaluate
Run against native benchmarks across selected languages
Analyze
AI + human raters score on all six dimensions
Report
Detailed analysis with specific improvement recommendations
Use Cases
Who Uses Indic Eval
We're building an Indic LLM
Benchmark your model against comprehensive standards. Track improvement. Compare with competitors.
Vendor claims Hindi support
Verify claims before deployment. Get objective scores. Make informed procurement decisions.
Deploying AI for Indian users
Ensure your vernacular AI actually works before going live. Avoid embarrassing failures.
Evaluating AI vendors for government
Objective evaluation criteria for RFP responses. Verify vernacular capabilities.
Translated Benchmarks
- English questions machine-translated
- No code-mixed evaluation
- Cultural context completely missing
- Easy to game with translation layer
- No native speaker validation
Indic Eval
- Native speaker-created test sets
- Full code-mixed evaluation suite
- Cultural accuracy testing built-in
- Anti-gaming methodology
- Multi-rater native validation
Integration
Run in Your Workflow
- API Access: Programmatic evaluation endpoints
- CLI Tool: Command-line evaluation runner
- CI/CD Integration: Automated testing in pipelines
- Custom Benchmarks: Add your domain-specific tests
- Dashboard: Visual performance tracking over time
Output Formats
Leaderboard scores for comparison. Language breakdown by dimension. Categorized error analysis. Specific improvement recommendations. Audit-ready documentation.
You can't improve what you can't measure.
Indic Eval measures what matters.