// MULTIMODAL.EVALUATION
Multimodal Evaluation for Systems That Need More Than Scores
Go beyond single-format evaluation at scale. Capture learning across every input type.
WITHOUT.CGF
CGF.OUTPUT
// EVALUATION.INFRASTRUCTURE
// TRUSTED.IN.PRODUCTION
// FIELD.VALIDATION
// THE.PIVOT
Most evaluation systems are limited by format.
They evaluate text or MCQs separately, ignore diagrams and structure, and fail to unify inputs into one system.
Evaluation systems collapse rich learning into a single number. That number cannot be reused, audited, or built upon.
// SIGNALS.LOST
Capture what the score hides — across all modalities.
// FAILURE.MODES
Evaluation today is fragmented across formats.
01
Formats evaluated in silos
Text, MCQs, diagrams, and structured responses are evaluated in isolation. No unified system exists. This is why evaluation breaks at scale.
02
Learning signals lost across formats
Every format loses signals at evaluation. No system captures what was understood across input types.
03
No persistent memory across assessments
Evaluation ends at the score. No learning record is built. Personalisation systems break.
Education has no memory of learning.
// evaluation.infrastructure
CrazyGoldFish is not a multimodal evaluation tool.
It is evaluation infrastructure.
Evaluation is the control point. This is where learning becomes structured and reusable. CrazyGoldFish is evaluation infrastructure — not another scoring layer.
Signal Capture
Extracts reasoning from handwritten, typed, and visual inputs.
Signal Structuring
Converts multimodal responses into structured learning signals.
Persistent Memory
Stores signals across formats, attempts, and time.
Memory Layer
Builds a continuous learning record across modalities.
{ "response_id": "resp_8k3m_eval_091", "modality": "handwritten_diagram", "rubric_alignment": 0.91, "icc_score": 0.93, "confidence_band": "high", "feedback_payload": "Correct structure. Sign error at step 3.", "memory_id": "stud_4f1a_session_017" }
Output is not scores. It is structured learning intelligence.
// WHY.THIS.WORKS
Built for Platforms Where Evaluation Decisions Matter.
HUMAN.IN.THE.LOOP
Teacher is the control point.
AI handles volume. Teachers govern outcomes. Every evaluation is human-in-the-loop by design — not an optional setting, a structural requirement built into the pipeline. The rubric is yours. The override is yours. The publish gate is yours.
FULL.AUDIT.TRAIL
Every evaluation is auditable.
Full signal trail on every response. Every override logged with reviewer ID, timestamp, and delta. Every decision traceable to the evaluation that triggered it. Nothing moves through the system without a record.
INSTITUTIONAL.VALIDATION
Validated at India AI Impact Summit 2026.
Published in the MeitY Compendium. Results independently reviewed and presented at national level — not self-reported benchmarks. Third-party institutional credibility with a public record.
HUMAN.IN.THE.LOOP
Policy-aware. Human-verified. Infrastructure-grade.
CGF's evaluation system is human-in-the-loop by design. Teachers remain the control point — AI handles volume, humans handle governance. This is not an automation layer. It is evaluation infrastructure with built-in human authority at every control point.
// continue.exploring
See how multimodal evaluation works across your platform.
Evaluation System
How signals are captured, structured, and stored as persistent learning memory.
View Evaluation System →LMS / ERP Platforms
How multimodal evaluation integrates with learning management systems.
See LMS / ERP Platforms →EdTech Platforms
How EdTech platforms deploy multimodal evaluation at scale.
See EdTech Solution →Institutions & Public Programs
How institutions evaluate handwritten, typed, and visual responses.
See Institutions Solution →// COMMON.QUESTIONS
Multimodal evaluation captures learning signals across text, handwritten, and visual responses — not just scores. CrazyGoldFish converts every format into structured learning signals and stores them as persistent learning memory. With 93% scoring reliability (ICC) and up to 60% reduction in evaluation time, it builds evaluation infrastructure across all input types.
What is multimodal evaluation?
Multimodal evaluation assesses student responses across text, handwritten answers, diagrams, and structured inputs within a unified system. CrazyGoldFish captures learning signals from every format and structures them as persistent learning memory — not just scores. This builds a complete picture of learning across all input types.
How does multimodal evaluation capture learning signals?
CrazyGoldFish extracts reasoning from handwritten, typed, and visual inputs using multimodal AI. Each response is structured into learning signals — what the student understood, where reasoning broke down, and evaluation confidence. These signals are stored persistently across assessments, not discarded after scoring.
Is multimodal AI evaluation reliable for handwritten and visual responses?
Yes. CrazyGoldFish achieves 93% reliability in total scoring alignment (ICC) across handwritten and structured responses. AI evaluation reaches 89.7% adjudicated correctness versus teacher 82.8% — exceeding the human baseline. Every evaluation is human-in-the-loop: the teacher remains final authority and all overrides become learning signals.
How does CrazyGoldFish differ from other multimodal evaluation tools?
Most multimodal tools produce scores across formats. CrazyGoldFish captures learning signals from every format and structures them as persistent learning memory — reusable, auditable, and buildable across assessments. It is evaluation infrastructure, not a scoring tool.
How does multimodal evaluation integrate with existing LMS platforms?
CrazyGoldFish integrates with LMS, ERP, and EdTech platforms via evaluation APIs. Any platform handling text, handwritten, or visual responses can capture learning signals without replacing existing infrastructure. The system layers underneath assessment workflows and returns structured learning intelligence per response.
// RELATED · RESEARCH & PLAYBOOKS
// next.step
Capture learning signals across every format.
Multimodal evaluation infrastructure that converts every input type into structured learning intelligence.