Industry · Consumer
AI Solutions for EdTech and Media
Adaptive learning, bilingual voice agents and content workflows at consumer scale.
Overview
Consumer products break AI economics in a way enterprise deployments rarely do. A feature that costs a few cents per interaction is fine for a hundred internal users and ruinous across a million learners, so cost per session is a design constraint from the first sketch.
We build learning and media features that hold quality while staying affordable at scale: smaller models for the routine path, larger ones reserved for the cases that need them, aggressive caching of anything repeated, and latency budgets that keep an interaction feeling live rather than delayed.
We have shipped bilingual reading and voice experiences for learners, including on-device speech where connectivity and privacy rule out sending audio to a server.
Where AI pays off in EdTech & Media
Adaptive learning paths
Difficulty and sequencing that respond to how a learner is actually performing, not to a fixed curriculum position.
Bilingual voice and reading agents
Conversational practice and pronunciation feedback, on-device where privacy or connectivity requires it.
Content generation and localisation
Assessment items, summaries and translations produced at catalogue scale with editorial review in the loop.
Moderation and safety
Age-appropriate filtering and escalation paths for user-generated content, tuned to your policy.
What we design around
EdTech & Media constraints
- Cost per session at consumer volume
- Latency low enough to feel conversational
- Child safety and age-appropriate content policy
- Offline and on-device operation where connectivity is poor
Services we apply here
How we build for EdTech & Media
Proof in production
Related case studies
Frequently asked
EdTech & Media: common questions
How do you keep per-user AI costs sustainable?
By routing most traffic to smaller models, caching repeated generations, capping context size, and reserving the expensive model for cases that measurably need it. Cost per session is tracked as a product metric from day one.
Can voice features work offline?
Yes. We have deployed embedded speech models that run on-device, which also removes the privacy question of sending a child's audio to a server.
How is generated learning content quality-checked?
Generated items are scored automatically against curriculum rules and sampled for human editorial review before publication, with the review rate falling as measured quality holds.
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