AI that teaches, adapts, and scales personalised learning.
We build AI systems for EdTech platforms, universities, and corporate learning teams - from adaptive curriculum engines to AI tutors that answer learner questions in context.
What we build
Adaptive Learning Systems
AI that adjusts content difficulty, pacing, and format in real-time based on each learner's performance, engagement signals, and learning style - proven to improve completion rates by 40–60%.
AI Tutoring & Q&A
Subject-specific AI tutors powered by RAG over course materials - answering learner questions in context, explaining concepts at the right level, and flagging knowledge gaps to educators.
Assessment & Grading Automation
LLM-based grading for open-ended assessments, essay evaluation with structured rubrics, and plagiarism and AI-content detection pipelines.
Content Generation
Automated generation of quiz questions, flashcards, summaries, and worked examples from existing course materials - reducing content production cost by 70%.
Learning Analytics
Dashboards and predictive models that identify at-risk learners before they disengage - enabling timely intervention from instructors or automated outreach.
Skills & Career Mapping
AI that maps learner skills to job market demand, recommends learning paths, and generates personalised upskilling plans for corporate L&D programs.
Compliance we handle by default
Education AI sits in a highly regulated space. We build compliance into the architecture - not as a checkbox.
FERPA (US)
Student record privacy - data access controls, audit logging, and consent management for educational institutions.
COPPA
Children's online privacy - age-gated data collection, parental consent workflows, and data minimisation for platforms serving under-13 users.
GDPR / UK GDPR
Full compliance for European learner data including right to erasure, data portability, and purpose limitation for educational records.
Accessibility (WCAG 2.1 AA)
AI-generated content and interfaces designed to meet WCAG 2.1 AA standards - including screen reader compatibility and cognitive accessibility.
What adaptive AI delivers in EdTech - with the data to back it.
40–60%
improvement in course completion rates with adaptive pacing
70%
reduction in content production cost using AI generation pipelines
3.2×
increase in at-risk learner intervention rate when ML flags disengagement
< 800ms
AI tutor response latency for in-context question answering
One-size-fits-all learning that leaves most students behind.
Static curricula that don't adjust when students struggle or race ahead
Instructors discovering disengagement only when students drop out
Assessment graded days after submission - too late to change behaviour
Course content updated annually by a small team - expensive and slow
No visibility into which content actually drives skill acquisition
AI that personalises every learning path in real time.
Adaptive engine adjusts difficulty, pacing, and format per learner session
Risk model flags disengagement 2–3 sessions before dropout - enabling timely outreach
LLM-based grading with structured rubric delivers feedback within seconds of submission
AI generation pipelines produce quiz questions, flashcards, and worked examples from existing materials
Learning analytics dashboards surface exactly which content correlates with assessment outcomes
“We cut content production cost by 70% using AI generation, and course completion rates went up 54% in the first quarter after adaptive pacing launched.”
54%
Course completion rate increase
70%
Content production cost reduction
28,000
Learners on the platform
89%
At-risk learners flagged accurately
EdTech AI questions we answer in the first meeting.
How does the adaptive learning engine decide what content to show next?
The adaptive engine maintains a real-time model of each learner's knowledge state using a variant of knowledge tracing - tracking which concepts have been demonstrated and which have not. It selects next content based on a combination of mastery probability, learning objective dependencies, and engagement signals (time-on-task, scroll depth, replay events). Difficulty is adjusted continuously, and content format (video, text, interactive exercise) is varied based on individual learner response patterns.
Can AI tutors handle subject-specific questions accurately?
Yes - when built correctly. We use RAG (Retrieval-Augmented Generation) over your course materials so the AI tutor answers from verified content rather than general model knowledge. This prevents hallucination on domain-specific topics and ensures answers align with your curriculum. We also build confidence thresholds - below a certain confidence level, the system escalates to a human instructor rather than guessing.
How do you handle COPPA compliance for platforms serving children under 13?
COPPA requires verifiable parental consent before collecting personal information from children under 13. We implement age-gating at account creation, parental consent workflows with email verification, data minimisation (collecting only what is necessary for the educational function), and automatic data deletion on parent request. All data collection purposes are documented and limited to the stated educational use.
Can AI grading replace human graders on high-stakes assessments?
For most formative assessments - practice quizzes, short-answer questions, essays with structured rubrics - AI grading is accurate enough for production use. For high-stakes summative assessments (final exams, certification assessments), we recommend AI-assisted grading where AI grades first and flags low-confidence cases for human review. All AI grading systems we build include a full audit trail, rubric traceability, and override capability for instructors.