AI Tutors That Adapt to Every Student

Tutoring agents remember learning styles, struggles, and breakthroughs. Personalized instruction at scale.

The Problem

One-Size-Fits-All

Generic content ignores individual learning patterns.

No Continuity

Each session starts fresh with no memory of progress.

Teacher Bottleneck

1:1 attention doesn't scale. Teachers are overwhelmed.

The HatiData Fix

Student Memory

Track learning progress, misconceptions, and preferences.

Adaptive Content

semantic_match() finds the right content for each student.

Learning Analytics

Track effectiveness with semantic_rank() on comprehension.

See It in Action

SELECT l.lesson_id, l.title, l.difficulty,
       semantic_rank(m.embedding, 'struggling with quadratic equations') AS fit
FROM lessons l
JOIN_VECTOR student_memories m
  ON semantic_match(m.embedding, 'struggling with quadratic equations', 0.6)
WHERE l.subject = 'mathematics'
ORDER BY fit DESC LIMIT 5;

2.3x

faster mastery

89%

student engagement

1:∞

tutor-student ratio

Related Use Cases

Customer Support

End the amnesia loop. Your support agent recalls every past interaction, preference, and resolution — across sessions.

View use case

Gaming & Virtual Worlds

Non-player characters with persistent memory create emergent narratives. Branching storylines test in isolation before going live.

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Healthcare Data Intelligence

Agents access patient history with HIPAA-grade audit trails. Column masking and row-level security enforced at query time.

View use case

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