memsolus
Use Case — Education

Tutor that remembers
every student,
every session

Adaptive tutors that know each student's progress, difficulties and pace — and use that knowledge to personalize every session in real time.

+42%
Engagement per session
−33%
Module dropout
+55%
30-day retention
Perfil adaptativo — MemSolus
Memória ativa
P
Pedro Vieira
ENEM 2025 · 22 sessões · última há 1 dia
🔥 12 dias seguidos
O que o tutor sabe sobre Pedro
Interpretação de textoLogaritmos — revisarCiências NaturaisMudança de baseGeometria planaFunções quadráticas
Progresso por área
Interpretação de texto91%
Ciências Naturais84%
Matemática geral68%
Logaritmos30%
Sessão de hoje — gerada pela IA
1Revisão de mudança de base com exemplos práticos
2Exercícios contextualizados em pH e decibéis
3Simulado ENEM — Questões de matemática nível médio
Problem

The cost of AI
education without memory

01

Zero memory between sessions

Every session starts from scratch. The tutor doesn't know what the student learned, where they struggled, or what the next step is — forcing students to repeat context every time.

02

No intelligent review

Without a history of when each concept was learned, spaced repetition is impossible. Students complete modules but don't retain content — the forgetting curve acts freely.

03

Disconnected modules

The advanced module tutor doesn't know what the student learned in previous modules. Each stage starts from zero, losing the accumulated context of the learning journey.

Features

Three ways memory transforms learning

01

Intelligent Adaptive Tutor

Identifies learning gaps and adapts content to each student's pace.

Memsolus stores every student interaction with the tutor: attempted exercises, mistakes made, explanations that needed repetition, and mastered concepts. With this history, the tutor adapts not just the content, but the pedagogical approach — each student gets a unique experience.

Use case scenario

Pedro, 17, uses an exam prep platform. The tutor recorded that he misses 70% of logarithm questions — especially base changes. In today's session, without Pedro saying anything, the tutor starts with practical examples connected to pH and decibels he mentioned finding interesting.

Perfil adaptativo — Pedro
P
Pedro Vieira — ENEM 2025
22 sessões · 🔥 12 dias seguidos
Interpretação de texto91%
Matemática geral68%
Logaritmos30%
Ciências Naturais84%
Próxima sessão gerada pela IA
Revisão de mudança de base em logaritmos com exemplos de pH e decibéis — baseado nas dificuldades das últimas 3 sessões.

Assistente de Pesquisa — Fernanda
ResNet + AUC > 0.90 + Alzheimer
3 resultados encontrados nas suas memórias
ResNet-50 for Alzheimer MRI classificationAUC 0.94
Deep learning + ADNI dataset — early detectionAUC 0.91
Transfer learning for neuroimaging biomarkersAUC 0.93
Nota da pesquisa: Todos usaram ADNI dataset. Metodologia de pré-processamento diverge entre os três — vale mencionar na seção de limitações.
02

Academic Research Assistant

Maintains the context of references, papers and discoveries between research sessions.

Memsolus eliminates the friction of reintroducing context each session. References are stored with their theses and methodologies. The assistant identifies contradictions between sources, suggests connections between papers, and resumes writing from the exact point where the researcher left off.

Use case scenario

Fernanda is writing her thesis on machine learning and Alzheimer's diagnosis. Over two months, she processed 47 papers with the assistant. Today she asks: "Which studies used ResNet and reported AUC above 0.90?" The assistant responds in seconds — no Notion, Zotero, or manual search needed.


03

Continuous Learning Platform

Preserves learning history across different courses and modules.

Memsolus enables personalized spaced repetition based on each student's real history. The platform knows when each concept was learned, how the student performed on related exercises, and when the ideal moment to reinforce is — according to individual forgetting curves, not generic schedules.

Use case scenario

Rodrigo completed the SQL basics module three weeks ago. The assistant knows he mastered JOINs but struggled with correlated subqueries. When explaining Pandas, it connects merge to the JOIN concept Rodrigo knows and avoids subqueries as analogies — pedagogically informed by design.

Trilha de aprendizado — Rodrigo
Bootcamp de Dados — Semana 7
SQL Básico
Python para Dados
Pandas e NumPyEm progresso
🔒Machine Learning
Ponte automática: Rodrigo dominou JOINs mas teve dificuldade com subqueries. Pandas merge será explicado como analogia a JOINs — evitando subqueries.
Measurable impact

Results that
transform platforms

Platforms implementing adaptive tutoring with persistent memory report significant improvements in key learning and engagement metrics.

View Documentation →
Average engagement per session+42%
Students spend more time in sessions with tutors that know their history and adapt the content.
Module dropout−33%
Reduction in dropout when content is personalized to the exact point of the student's journey.
Retention after 30 days+55%
Improvement in content retention with spaced repetition based on each student's real history.
Context rebuilding time−85%
Reduction in time spent by students updating the tutor at the beginning of each session.
TypeScript
Technical integration

Works with your current stack

REST API

Complete endpoints to create, search and manage memories per student, session and agent. Compatible with any backend stack.

Any stack
Native MCP

MCP server to connect Memsolus directly to compatible agents without additional code. Integration in minutes.

Claude · GPT-4 · Gemini
Webhooks

Real-time notifications when learning milestones are reached — trigger certifications, alerts for human tutors or dashboard updates.

Real time
SDKs

TypeScript for React/Next.js and Node.js. Python for FastAPI/Django and AI pipelines with LangChain and LlamaIndex.

SOC-2 · HIPAA
Why MemSolus

Memory that turns every
student into a personalized learner

Dynamic profiles

Each student has a profile that grows with every session — competencies, difficulties, pedagogical preferences and progress on the learning path.

Contextual feedback

The tutor knows what the student learned 3 weeks ago and reinforces automatically at the right moment — spaced repetition based on real history, not generic calendars.

Efficient at scale

Sub-150ms latency for context retrieval. Individual personalization for thousands of simultaneous students without performance degradation.

Any framework

REST API, TypeScript SDK, Python SDK and native MCP. Integrates with LangChain, LlamaIndex, CrewAI and any AI agent platform.

Curricular memory

Knowledge accumulated in previous modules feeds into subsequent modules. Long learning paths with continuous context across courses, modules and sessions.

SOC-2 & HIPAA

Secure and isolated storage per student. Compliance with data protection regulations — essential for educational platforms and academic institutions.

The tutor that knows every student — every session.

Persistent memory for educational platforms — more engagement, more retention, less dropout.