// SIGNAL.INFRASTRUCTURE
AI Evaluation for EdTech Platforms
— Built for Scale
CrazyGoldFish converts the evaluation moment into structured learning signals — at question-level granularity, inside your product.
ASSIGNMENT.INPUT
SIGNAL.OUTPUT
— apply F=ma
ε evaluation → signal → action plan → memory stored → reusable across terms
// FIELD.VALIDATION
// SIGNAL.LOSS
Your system captures activity — but loses learning
Responses are evaluated inconsistently. Scores are stored, but reasoning is lost. No question-level intelligence is retained.
WHAT YOUR PLATFORM STORES
WHAT GETS LOST
Evaluation is the control point of your entire learning system. Every student response passes through it — understanding interpreted, mistakes identified, partial knowledge visible. This is why evaluation infrastructure must be treated as a first-class system.
// EVALUATION.AS.INFRASTRUCTURE
Plug evaluation directly into your product workflows
CrazyGoldFish sits exactly where evaluation happens — triggered via APIs, embedded directly into your platform.
HOW IT INTEGRATES
Student submits response
Handwritten, typed, image, or multimodal — format-agnostic capture.
Evaluation triggered via API
Rubric-based evaluation executed using policies configured for your platform. See Evaluation APIs for integration details.
Signal capture
Step-wise correctness, conceptual gaps, partial credit logic, reasoning patterns.
Structured outputs returned
Machine-readable signals, question-level granularity, feedback and scoring breakdown.
Downstream usage unlocked
Structured learning intelligence, personalization, remediation, dispute resolution with full audit trail.
WHAT YOU GET
Consistent evaluation at scale
Standardised rubric enforcement. Reduced teacher variance across your evaluator pool.
Faster turnaround
AI-first evaluation. Human review only on flagged and low-confidence cases.
Auditability and trust
Every mark traceable. Full evaluation history stored — critical for disputes and compliance.
Personalization layer
Learning signals at question level, not just scores. Enables real adaptive learning.
Product intelligence
Identify weak concepts across cohorts. Improve content and pedagogy with structured learning signals.
// HUMAN.IN.THE.LOOP
Evaluation must be reliable, auditable, and controlled
Human-in-the-loop
Teacher remains the final authority. Override and edit capabilities built in to every evaluation workflow.
Deterministic flagging
Low-confidence responses routed automatically for human review. Hotspot questions flagged across cohorts.
Auditability
Every evaluation decision logged. Full traceability from response to score — available for disputes and compliance.
Policy-aware evaluation
Rubric enforcement and configurable logic. Scoring adapts to your institutional policies, not the other way around.
This is not blind automation. It is governed evaluation at scale.
// BUILT.FOR.YOUR.CONTEXT
Which type of organization are you?
CrazyGoldFish is built for organizations that deliver learning at scale. Find your implementation context.
Coaching & Test-Prep Networks
High-stakes exam preparation where scoring precision determines outcomes.
See how it works →LMS / ERP Platforms
Infrastructure providers embedding evaluation into existing institutional workflows.
See how it works →Institutions & Public Programs
Government programs and NGOs delivering learning outcomes at district or national scale.
See how it works →// COMMON.QUESTIONS
CrazyGoldFish is AI evaluation infrastructure for EdTech platforms. Instead of returning only a score, it captures structured learning signals per response — rubric-aligned marks, step-wise correctness, and conceptual gaps. It integrates via REST API with no platform restructuring required, achieving 93% scoring reliability (ICC). Validated in live school environments.
How does evaluation become a bottleneck at scale for EdTech platforms?
Most EdTech platforms can handle 10,000 or 100,000 students submitting assessments — but evaluation doesn't scale with them. Human review creates turnaround delays, inconsistent marking across teachers distorts learning data, and feedback generated at scale is unstructured, making it unusable downstream for personalisation or reporting.
What does CrazyGoldFish return from an evaluation — beyond a score?
CrazyGoldFish returns structured learning signals: a rubric-aligned mark, step-wise correctness flags, conceptual gap identifiers, and a structured feedback string. These outputs are directly consumable by your gradebook, recommendation engine, or reporting layer — no additional processing required.
Is the evaluation reliable enough for a live product?
Yes. CrazyGoldFish achieved 93% reliability in total scoring alignment (ICC), with 89.7% adjudicated correctness vs teacher 82.8% (+6.9pp). 87% of responses fall within tolerance (±6 marks). Platforms using CrazyGoldFish have demonstrated up to 60% reduction in teacher evaluation time (Phase 1 validated; India AI Impact Summit 2026 Compendium, MeitY).
Does integrating CrazyGoldFish require changes to our existing platform architecture?
No major architectural changes. CrazyGoldFish integrates via REST API — you pass the question, rubric, and student response; the API returns structured evaluation outputs. It sits between your submission layer and your gradebook, requiring no schema changes to your existing platform.
What human oversight does CrazyGoldFish provide?
CrazyGoldFish is human-in-the-loop by design. The system deterministically flags responses that fall outside expected scoring distributions for mandatory human review. Every evaluation is auditable — full reasoning traces are stored alongside scores so your team can inspect or override any output.
// RELATED · RESEARCH & PLAYBOOKS
// EVALUATION.INFRASTRUCTURE
Make evaluation a system — not a bottleneck
See how CrazyGoldFish integrates into your platform. Book a call with the team.