Four layers, working together.
Generative AI
Self-hosted language models that read, distil, translate and explain — the voice behind every summary, takeaway and insight.
Agentic AI
Systems that act in multi-step sequences on their own: discover, screen, generate, evaluate their own output, and correct what falls short.
Data Engineering
Continuous pipelines that discover, clean, classify and de-duplicate content — the layer that makes intelligence trustworthy.
Product Engineering
Mobile and web experiences engineered end to end, from architecture to the interface in someone's hand.
Machine intelligence, practically applied.
What runs inside Inlrn today.
From raw data to a finished feed.
Data & Discovery
It starts with the data — what exists, what's missing, what it can honestly support.
Generative Modelling
Language models turn raw content into something worth a minute of someone's time.
Agentic Evaluation
Every output is scored against its source across five dimensions, and failures are corrected — automatically, without waiting on a person.
Safety & Trust
Sources are screened before they're read. Unsafe or unverifiable content never reaches the model.
Personalisation
Behaviour reshapes the feed continuously — the same engine that powers recommendation at scale.
Iteration
Nothing ships once. Real usage is where an AI system actually gets built.