// LEARNING.INFRASTRUCTURE
AI Evaluation for LMS and ERP Platforms — Built for Institutional Scale
Your LMS stores grades, manages teachers, and tracks submissions. But evaluation runs outside your system — manual, inconsistent, unstructured. No signal is captured. No learning intelligence is built.
signals: {
}
// FIELD.VALIDATION
// SYSTEM.INTELLIGENCE.GAP
Your platform records learning — but cannot understand it
What your LMS stores
✓ Grades and scores
✓ Submission timestamps
✓ Teacher comments
✓ Report card totals
What your system cannot see
× Why a student lost marks on each question
× Conceptual gaps behind wrong answers
× Evaluator variance across classrooms
× No question-level learning signals stored
× No foundation for personalization or adaptive learning
Evaluation is the control point where learning becomes structured. CrazyGoldFish converts this moment into evaluation infrastructure — not just grades.
// LMS.INTEGRATION.LAYER
Embed evaluation into your LMS / ERP core
CrazyGoldFish integrates directly into your evaluation layer — triggered via Evaluation APIs, embedded into your existing workflows.
Inside your system
Assignment submissions
Written responses and answers submitted across subjects and grades
Exams and assessments
Subjective and structured exams evaluated with rubric enforcement
Internal assessments
Class tests, unit evaluations, and teacher-set assessments at scale
Teacher evaluation workflows
Standardised marking flows with human review and override built in
Student submits response
Assignments, exams, written responses across any subject
Evaluation (control point)
Rubric-based scoring via API. Policy-aware evaluation logic enforced per institution
Signal capture
Conceptual understanding · Step-wise correctness · Partial credit · Learning gaps
Structured outputs returned
Machine-readable learning signals · Question-level breakdown · Feedback with reasoning
Downstream usage
Gradebooks with structured signals · Detailed report cards · Personalization engines · Explainable feedback · Structured learning intelligence
// HUMAN.IN.THE.LOOP
Evaluation must be controlled, auditable, and policy-aligned
Human-in-the-loop
Teachers remain final authority. Review and override capabilities built-in.
Deterministic flagging
Low-confidence cases flagged. Edge cases routed for review.
Auditability
Every mark linked to response and rubric. Full traceability across system.
Policy-aware evaluation
Configurable grading rules. Institution-specific evaluation logic.
Compliance with academic policies. Transparent and defensible evaluation.
// BUILT.FOR.YOUR.CONTEXT
Which type of organization are you?
CrazyGoldFish is built for organizations that deliver learning at scale. Find your implementation context.
EdTech Platforms
Adaptive learning products that need evaluation signals at product scale.
See how it works →Coaching & Test-Prep Networks
High-stakes exam preparation where scoring precision determines outcomes.
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
Common Questions
What is the difference between an LMS recording evaluation data and understanding it?
Most LMS and ERP platforms record what happened: submission timestamps, scores, pass/fail flags. CrazyGoldFish enables LMS platforms to understand what happened: conceptual errors, step-wise correctness, knowledge gaps — structured at the point of evaluation, before information collapses into a summary field or is lost entirely.
How does CrazyGoldFish solve the inconsistent grading problem in multi-teacher environments?
Teacher-to-teacher grading variation is one of the least acknowledged reliability problems in academic records. CrazyGoldFish applies standardised rubric-based evaluation consistently across every response, regardless of which teacher is assigned to which classroom — producing academic records that reflect learning rather than evaluator variance.
What structured outputs does CrazyGoldFish add to LMS gradebooks and report cards?
Beyond a numeric score, CrazyGoldFish returns conceptual understanding flags, step-wise correctness maps, learning gap identifiers, and structured feedback strings. These power report cards with specific learning detail, inform personalisation engines, and give administrators reliable data to aggregate across cohorts and terms.
How reliable is CrazyGoldFish for academic record purposes?
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). Deployment demonstrated up to 60% reduction in teacher evaluation time (Phase 1 validated; India AI Impact Summit 2026 Compendium, MeitY).
How does CrazyGoldFish integrate with an existing LMS or ERP system?
CrazyGoldFish provides REST Evaluation APIs that embed into your existing submission flow — no schema migration, no data warehouse changes. Evaluation requests are sent at the point of submission; structured outputs are returned in the same request lifecycle and written directly into your gradebook schema.
// RELATED · RESEARCH & PLAYBOOKS
// EVALUATION.INFRASTRUCTURE
Turn your LMS into a system that understands learning
See how CrazyGoldFish integrates into your platform. Book a call with the team.