AZMUTH converts handwritten answer sheets into multidimensional cognitive profiles. Automatically. At scale.
See How It Works →Analysis: Strong conceptual grasp of theory, but struggles significantly with procedural execution.
Analysis: Excels at procedural execution, but fundamentally lacks core conceptual understanding.
Single-dimensional scores that hide cognitive reality
"Needs improvement" — but in what, and why?
Per class of 40 students — manual, exhausting labor
Teachers upload photos or scans of handwritten answer sheets. Multi-page support for comprehensive exams.
Advanced AI reads and interprets handwriting — extracting content, structure, and error patterns from every answer.
Each response is decomposed into sub-steps, identifying what the student did — and where the cognitive breakdown occurred.
11 cognitive metrics are computed using the 15-rule classification system. Each student gets a unique cognitive fingerprint.
Comprehensive cognitive report with archetype classification, causal chains, and actionable pedagogical recommendations.
Understands why things work but stumbles on execution. Gets the theory, fumbles the method.
Structured practice drills with worked examples. Build procedural fluency alongside conceptual strength.
Applies formulas like recipes — correct steps, zero understanding. Breaks down on novel problems.
Conceptual questioning before method. Ask "why does this work?" before "how do you solve it?"
Brilliant on some questions, baffling on others. Performance depends on mood, topic, or complexity.
Metacognitive strategies — self-checklists, error journals, and regular low-stakes assessments.
Shuts down when complexity increases. Attempts easy questions confidently, abandons hard ones.
Scaffolded difficulty progression. Break complex problems into micro-steps with early wins.
Never gives up but keeps making the same mistakes. Effort without strategy. Heart without method.
Error analysis sessions. Channel persistence into deliberate practice with targeted feedback loops.
Matches question templates to memorized solutions. Fast on familiar patterns, helpless on unfamiliar ones.
Novel problem exposure. Mix question formats, remove pattern cues, and require explanation of reasoning.
Strong across all dimensions. Conceptually clear, procedurally accurate, and self-correcting.
Advanced challenges, peer teaching opportunities, and enrichment beyond curriculum boundaries.
Azmuth doesn't just list weak metrics — it maps why failures cascade through connected cognitive dimensions.
Production-grade AI backbone. Proven accuracy on cognitive profiling.
Proprietary model trained on structured cognitive assessment data.
structured parameters · per student · per exam
| Metric | Current (Claude) | Native AI Target |
|---|---|---|
| Mistake Classification Accuracy | ~85% | 95%+ |
| Handwriting Recognition | ~90% | 98%+ |
| Cost Per Analysis | ₹8–12 | ₹1–2 |
| Latency Per Student | ~30 sec | <5 sec |
5 pilot schools
₹1.5L MRR
25 schools
₹7.5L MRR
100 schools
₹30L MRR
1,000 schools
₹90Cr ARR target
22-component structured prompt architecture. Not a ChatGPT wrapper — a precision instrument.
Every analysis generates 191 structured parameters. This data compounds into an irreplaceable asset.
Once teachers build cognitive histories for students, switching costs become prohibitive.
Each student develops a unique cognitive fingerprint that evolves over time.