Technology

How we build AI that is safe to put in front of a child

Six layers, each with a narrow job. The interesting engineering is in the constraints — keeping a generative system grounded, measurable and interruptible.

01

Knowledge layer

A curriculum graph of concepts, prerequisites, objectives and assessment items, aligned to board and grade. Retrieval is scoped to this graph, which is what keeps generated explanations on syllabus.

Concept graphStandards alignmentGrounded retrievalContent versioning
02

Reasoning layer

Language models with tool access, running against the knowledge layer. Tools let the tutor drive simulations, pull assessment items, and check the learner record before deciding what to say next.

Tool callingRAG pipelinesSession planningGuardrails
03

Learner model

Bayesian knowledge tracing maintains a probability of mastery per concept, updated from answers, speech attempts, hesitation and applet interactions. Prerequisite gaps surface before they compound.

Mastery estimatesPrerequisite detectionInteraction telemetry
04

Realtime layer

Streaming voice in both directions with sub-second turnaround, so a lesson feels like a conversation rather than a request-response cycle. Session state survives reconnects and device switches.

Streaming speechDuplex audioEvent syncResumable sessions
05

Speech layer

Pronunciation assessment scoring accuracy, fluency and completeness down to the phoneme, plus generated reference audio so a learner can hear the target immediately after their attempt.

Phoneme scoringProsody metricsSpeech synthesis
06

Platform layer

Multi-tenant institution and enrolment management, content publishing, role-based access, bulk import and scheduled export. The unglamorous infrastructure that makes the rest deployable in a real school.

Multi-tenantRBACContent pipelinesReporting exports

Engineering practice

Built to be operated, audited and handed over

Schools cannot run on a prototype. Everything we ship is designed for the day it is someone else's responsibility to keep running.

  • Infrastructure defined as code and deployed through reviewed pipelines
  • Encryption in transit and at rest across every learner-facing service
  • Tenant isolation enforced at the data layer, not just the API surface
  • Role-scoped access for administrators, instructors and learners
  • Structured evaluation of model output before it reaches a classroom
  • Observability on latency, error rates and generation quality in production

Reviewing our technical approach?

Happy to go deeper on architecture, evaluation methodology or data handling for grant, partnership or procurement review.