Multimodal Assessment Engine

Scaffold Learning at Scale.
Empower Teaching.

Gradence provides educators with the tools to deliver world-class formative feedback.

We don't replace the teacher; we amplify their ability to guide student growth.

🎓

"In fee-paying private academies, assessment is often shaped by parental expectations... resulting in grading practices that do not always reflect the developmental needs of learners."

Practitioner-led research informing system design (2025)

Total Input Versatility

We don't just grade essays. We digitize the entire assessment workflow.

Intelligent OCR

We bridge the analog-digital divide. Our computer vision pipeline extracts handwritten text from paper exams with high fidelity, distinguishing between student writing and printed prompts.

JPG PNG Handwriting

Audio & Video Analysis

From IELTS speaking tests to classroom presentations. We perform automated transcription followed by rhetorical analysis to grade fluency, pronunciation, and coherence.

MP4 MP3 WAV

Format Agnostic

We support the files schools actually use. Native processing for HWP (Hangul Word Processor), Microsoft Word, and PDFs ensures no student is left behind by compatibility issues.

HWP DOCX PDF

Under the Hood

High-Throughput
Inference Architecture.

We leverage a distributed, event-driven architecture designed for continuous use in school environments, handling bursts of concurrent submissions during exam periods alongside sustained background analysis for progress tracking.

Cloud credits will support our active pilot deployments and scale testing across multiple classrooms, ensuring we maintain real-time feedback performance as user load increases.

100k+
Context Tokens
99.9%
Queue Reliability
pipeline_core.py
async def process_submission(file_stream):
    # 1. Async Ingestion (Cloud Storage)
    blob = await storage.upload(file_stream)
    
    # 2. Celery Task Distribution
    job = analyze_task.delay(blob.id)
    
    # 3. Multi-Stage Inference
    if blob.is_video:
        transcript = await speech_engine.transcribe(blob)
        analysis = await llm.analyze(transcript, context=history)
    
    # 4. Real-time Push
    socket.emit('update', {'status': 'complete', 'data': analysis})
Python / Flask
Celery Workers
Redis Cluster
PostgreSQL
Google Vertex AI
Socket.IO

Supported Frameworks

Native and rubric-aligned support for leading international assessment frameworks.

IB

International Baccalaureate

MYP Criteria A-D & DP

IGCSE

Cambridge IGCSE

9-1 Grading Scale

CEFR

European Framework

A1 - C2 Proficiency

ETS

TOEFL & IELTS

Standardized Exam Prep