Multimodal Assessment Engine

Make Student Progress Visible.
Credible. Shareable. Tracked.

Gradence is more than just feedback. We provide the complete infrastructure for assessment: precise scoring, structured analysis, and shareable reports that prove long-term growth.

We standardize the heavy lifting. You stay in control.

90s
Avg. Turnaround
Full Spectrum
Speaking • Writing • Listening
Engine Architecture
OCR • Transcription • RAG
Willing Academy
Official Pilot Partner

Willing Academy, South Korea

Supporting 130+ students across 13 active classes (elementary to high school), with planned scale-up to 800+ students.

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

The 10-Year Journey

Gradence is architected for longitudinal growth. We track student performance from Elementary (A1) to University Entrance (C2), providing schools with a 'Golden Dataset' of student progress that spans a decade of academic development.

Holly AI Data Assistant
How is Class 3B performing on the 'Future Tense' module compared to last month?

Analysis: Class 3B has shown a 14% improvement in Future Tense accuracy.

Querying 45 submissions from Jan 1st - Feb 1st...

Key strengths identified in speaking tasks, though written application remains consistent with previous baselines.

Identify students at risk of falling behind in vocabulary.
Retrieval-Augmented Generation

Talk to Your Data.
Instant Pedagogical Insights.

Forget complex pivot tables and stale spreadsheets. Our RAG Engine indexes every student submission, score, and feedback comment, allowing you to query your entire institution's performance using natural language.

  • Forensic Recall: Instantly locate specific errors or patterns across thousands of assignments.
  • Trend Analysis: Ask "Why did grades drop this week?" and get evidence-backed answers.
  • Intervention Planning: Automatically generate grouped lesson plans based on identified gaps.

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+
Monthly Inference Capacity
99.9%
Fault-Tolerant Queue Management
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
Technical Review

Infrastructure, Scale & Data Stewardship

Architectural justification for high-performance compute and enterprise-grade cloud resources.

GPU & Compute Justification

  • Multi-Stage Inference Pipeline

    Our pipeline performs 14 distinct LLM calls per document to ensure granular analytical depth, far exceeding standard "one-shot" grading systems.

  • Accelerated Compute Requirements

    We utilize NVIDIA-accelerated compute for Handwritten OCR (HWP & PDF) and Multimodal Video Processing to keep feedback latency under 10 seconds for our 800-student pilot.

  • RAG Reranking

    Our "Holly" AI agents utilize cross-encoders for pedagogical precision, requiring dedicated inference workers for real-time vector retrieval.

Scaling Metrics

Current Phase
Active Pilot
130 Students
13 Active Classrooms
Throughput Capacity
Engineered Load
100,000+
Monthly Inferences
Data Stewardship
Longitudinal Tracking
10 Years
Elementary to High School

30 / 60 / 90 Day Infrastructure Roadmap

30 Days

Google Vertex AI Migration

Migrate to Gemini 1.5 Pro's 2M context window for deep-document analysis. Establish baseline cost-per-submission monitoring.

60 Days

Real-Time Oral Exams

Deploy NVIDIA-accelerated Whisper-v3 for real-time oral assessments. Launch secondary pilot in Vietnam.

90 Days

Proprietary Fine-Tuning

Evaluate transition to self-hosted Llama-3 (70B) on NVIDIA-accelerated instances for proprietary pedagogical fine-tuning.

Security, Compliance & Data Stewardship

Enterprise Encryption

All data is encrypted with AES-256 at rest and TLS 1.3 in transit, ensuring banking-grade security for student records.

PII Redaction Layer

Automated scrubbing of student data (PII) from artifacts before model inference to ensure anonymity.

Regulatory Compliance

Architected for FERPA, GDPR, and Korean PIPA compliance.

Regional Sovereignty

Support for region-locked data residency in South Korea and Vietnam.

Supported Frameworks

Our models are calibrated against the IELTS 9-band scale and CEFR proficiency levels, providing validated pedagogical feedback for international school standards.

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

Powering the Future of Education with NVIDIA Inception Program