London EdTech Week 2026 | 9–10 June | Olympia London
AI is already on most EdTech roadmaps. As an AI engineering partner for EdTech companies, Aristek takes on the hardest part — making AI useful inside real learning workflows: assessment, content, learner data, reporting, integrations, and operations.
years in EdTech
years in AI
of clients stay 5+ years
For classroom platforms, district-facing products, school systems, assessment tools, and curriculum solutions.
Safe AI / assessment integrity / teacher workflows / student data / accessibility / classroom-ready implementation
For digital learning platforms, student success tools, university-facing products, learning analytics, and LMS-connected systems.
Retention / progression / learning outcomes / reporting / curriculum logic / LMS operations / fragmented data
For LMS, LXP, skills platforms, workforce learning products, certification, and professional training.
Personalized learning / ROI / skills intelligence / HRIS/LMS workflows / manager reporting / operational automation
For content/curriculum providers, publishers, learning content companies, assessment content teams, and training content businesses.
Content transformation / curriculum mapping / accessibility / assessment generation / metadata / adaptive formats
For student recruitment platforms, enrollment management systems, admissions tools, agent portals, and CRM-connected solutions.
Application conversion / admissions workflows / document processing / CRM integration / funnel visibility / fragmented applicant data
For learning products that combine elements of multiple segments or solve challenges in their own way.
Scaling architecture / disconnected systems / data consistency / operational visibility / integrating AI into an existing product
These are not boxed products. They are anonymized reference versions of solutions we have built or implemented for EdTech clients to solve similar problems. Use them to choose the conversation you want to have with us at London EdTech Week.
In a 30-minute conversation, we won’t walk you through a generic deck. We will focus on one product bottleneck and discuss:
What similar problems usually require technically.
Where AI can create real product value — and where it can’t.
What data, integration, or governance risks may block the idea.
Whether the right next step is validation, prototype, build, or architecture review.
For commercial conversations, partnerships, delivery models, and C-level alignment.
For AI, data, prototype logic, technical feasibility, and product-to-production questions.
For first conversation, event follow-up, matching your product bottleneck with the right team member, and next-step coordination.
