INDUSTRY · EDTECH
200,000 concurrent learners during peak exams. Zero degradation.
We build LMS infrastructure, adaptive assessment engines using item response theory, and FERPA-aware data platforms that handle the synchronous load spikes education creates every day.
WHY
Learning platforms fail when infrastructure can't handle the synchronous demand patterns that education creates. Every student in the same time zone hits the platform at 8am. Every assignment deadline triggers a submission spike. We build the infrastructure that handles these predictable spikes without over-provisioning for off-peak hours.
Adaptive learning requires real-time learner modeling. Item response theory, knowledge component mapping, and mastery estimation all run on clean, low-latency event data. We've built the assessment pipelines that feed adaptive engines with the right signal: response time, answer confidence, attempt patterns, and historical performance by concept.
FERPA compliance and COPPA requirements shape every architectural decision in edtech. We build systems with data minimization by design, parental consent workflows, and audit logs that satisfy legal review without bolting compliance onto a non-compliant data model after launch.
WHAT WE BUILD
Relevant capabilities
CAPABILITY · 01
Custom Platforms
LMS backends, content delivery systems, student dashboards, and instructor tooling.
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CAPABILITY · 02
AI & Machine Learning
Adaptive assessment engines, knowledge mastery estimation, and learning path personalization models.
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CAPABILITY · 03
Data Engineering
Learner event pipelines, xAPI and LTI data integration, and educational data warehouse infrastructure.
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CAPABILITY · 04
Infrastructure & DevOps
FERPA-aware cloud infrastructure with synchronous load handling, CDN optimization, and COPPA-compliant data flows.
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CAPABILITY · 05
Automation & Integration
SIS integrations, rostering automation, and third-party LTI tool connectivity.
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CAPABILITY · 06
Algorithms & Optimization
Content sequencing algorithms, spaced repetition engines, and engagement scoring models.
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COHORT ANALYTICS
Student-cohort analytics query patterns
Education analytics splits cleanly into two query shapes: longitudinal (one student across a term or year) and cross-section (one assessment across a cohort at one time). They have different storage and indexing needs. Longitudinal lives on a per-student timeline with monotonic event IDs, indexed by student_id and time. Cross-section lives in a star schema with assessment_id as the fact key and student demographics, school, and term as dimensions. We ship both materializations from the same xAPI event stream. Cohort definitions are versioned so 'fall 2025 grade 6 math' returns the same set every time the dashboard reloads.
Longitudinal shape
Per-student timeline, time-indexed
Cross-section shape
Star schema, assessment fact + cohort dims
Source
Single xAPI stream, dual materialization
Cohort versioning
Frozen definitions, reproducible queries
Query latency target
<500ms p95 dashboard
PII boundary
Aggregates only outside FERPA scope
SAMPLE WORK
What we've shipped
LMS infrastructure handling 200,000 concurrent learners during peak exam periods with zero degradation.
Adaptive assessment engine using item response theory to personalize question difficulty in real time across 10,000+ items.
Learner analytics pipeline processing xAPI events from 15 content sources into a unified progress model per student.
FERPA-aware data platform with parental consent workflows, right-to-erasure automation, and district-level audit reporting.
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