AI Application Enhancement System

Core Logic

Uses GPT-4o with 10K token quota and $0.20 budget. Coordinates 7-phase DAG execution with max parallelism of 2 agents per phase. Manages agent dependencies, handles self-healing operations on failures, and tracks execution flow. Monitors token usage and cost accumulation across all agents. Provides execution timeline and phase completion status.

Core Logic

Leverages Claude 3.5 Sonnet with 50K token quota for deep essay analysis. Evaluates structure, clarity, narrative arc, and emotional resonance. Scores essays across multiple dimensions with weighted rubrics. Identifies strengths and specific improvement recommendations. Detects authenticity and voice consistency across multiple essays.

Core Logic

Uses GPT-4o with 30K token quota for activity analysis. Enhances activity descriptions with impact quantification and leadership extraction. Identifies transferable skills and career alignment. Suggests reframing to highlight achievements. Calculates activity depth scores and consistency across involvement areas.

Core Logic

Employs GPT-4o-mini with 10K token quota for efficient validation. Checks document completeness against requirements, verifies authenticity markers, and ensures compliance with submission guidelines. Extracts metadata for cross-validation. Flags missing or problematic documents with specific remediation guidance.

Core Logic

Applies GPT-4o with 20K token quota for comprehensive scoring. Implements weighted scoring system across all application components. Calculates percentile rankings against historical applicants. Generates overall grade (A+ to C-) with confidence bounds. Identifies strongest and weakest application areas with improvement potential.

Core Logic

Uses Claude 3.5 Sonnet with 15K token quota for recommendation synthesis. Aggregates findings from all analytical agents into prioritized recommendations. Estimates impact of each improvement on admission probability. Provides specific, actionable guidance with implementation steps. Ranks recommendations by effort-to-impact ratio.

Core Logic

Leverages GPT-4o for ML-powered enrollment analysis. Processes engagement signals (campus visits, portal activity, email opens, webinar attendance). Assesses financial fit through aid package alignment. Analyzes geographic proximity and competitor school applications (Stanford, MIT, Carnegie Mellon). Generates yield optimization strategies with implementation steps.

Core Logic

Uses Claude 3.5 Sonnet for autonomous bias detection. Analyzes decisions for 7 bias categories: gender, socioeconomic, geographic, racial/ethnic, first-generation, and legacy. Calculates fairness score (0-100), demographic parity, and equalized odds metrics. Verifies compliance with Title VI, Title IX, ADA, and FERPA. Generates mitigation strategies with implementation priorities.

Core Logic

Employs GPT-4o for comprehensive aid optimization. Performs need analysis against expected family contribution. Assesses merit aid eligibility across 3+ scholarship options. Generates recommended aid package with total value calculation. Provides alternative package scenarios for negotiation. Calculates 4-year ROI including projected earnings and payback period.

Core Logic

Uses GPT-4o-mini for efficient scheduling. Recommends interview type (Alumni, Officer, Faculty) based on applicant profile. Matches interviewers by shared interests and expertise with match scores. Generates 3+ scheduling slot options respecting availability. Provides preparation resources (guides, videos, practice questions). Predicts interview success with confidence intervals.

Core Logic

Applies GPT-4o for ML-powered peer comparisons. Calculates overall percentile ranking against historical admits. Creates 3 peer group comparisons with applicant positioning. Analyzes 5+ metrics (GPA, SAT, activity hours, essay scores, leadership). Identifies success predictors with correlation scores. Matches similar historical admits (3+ profiles) for outcome reference.

Core Logic

Leverages Claude 3.5 Sonnet for deep sentiment analysis. Evaluates recommendation letter sentiment per recommender with relationship context. Analyzes essay sentiment across dimensions (emotional depth, self-awareness, growth mindset, resilience, authenticity). Creates tone profile (confidence, humility, passion, professionalism, creativity, leadership). Flags potential concerns with severity levels.

Core Logic

Uses GPT-4o for comprehensive credential verification. Verifies transcripts against institution databases including GPA and course validation. Performs international credential assessment with country-specific equivalency and GPA conversion. Validates test scores by type and testing center. Conducts document authenticity checking with fraud pattern detection and risk scoring.

Core Logic

Employs Claude 3.5 Sonnet for comprehensive decision explanation. Generates decision summary with confidence level classification. Documents 5+ decision factors with weights (30%, 25%, 20%, 15%, 10%), contribution scores, and supporting evidence. Creates reasoning chain (7+ steps) with agent IDs and input/output tracking. Produces counterfactual scenarios for what-if analysis. Generates audit trail with timestamps for compliance.