IndiLearn builds AI education tools that live inside your school — not someone else's cloud. Student data never leaves the building. Two products, one architecture, zero compromises on privacy.
Mac mini running local models. No per-token billing. No data sovereignty risk.
Proprietary neural engine captures isolated grapheme responses with Australian accent support.
Tracks whether students enact next-steps. The missing link in every other feedback tool.
Built around real classroom pain points. Reduces workload rather than adding to it.
Every IndiLearn product targets a specific logistics gap between what research says works and what teachers can actually deliver at scale.
A structured literacy tool for Prep–Year 2, built on the Queensland scope and sequence. Our proprietary speech recognition neural engine captures children's isolated phoneme production in real time — something no other app does — with full support for Australian accents.
6-stage teaching flow — Read letters → Write letters → Read words → Write words → Read sentences → Write sentences. One thing at a time.
Proprietary phoneme recognition — Novel neural engine trained to capture isolated grapheme-phoneme correspondences with Australian accent variants. Never done before.
PencilKit tracing — Multi-algorithm scoring (pixel coverage, Fréchet distance, Hausdorff) gives zone-specific feedback: "top stroke good, lower curve needs work."
On-site LLM content generation — Decodable words and sentences generated using the child's active graphemes and real-session struggle data. Zero cloud calls.
AU/US accent toggle — Configurable for school context. Wired through speech recognition and TTS simultaneously.
Quality descriptive feedback has an effect size of 0.92 — one of the largest in educational research. Three things stop it happening at scale: it's physically impossible to deliver individually, students don't always apply it, and teacher capacity to analyse work varies. IndiLearn closes all three gaps.
Rubric-grounded feedback — Teacher uploads a unit PDF. System extracts structured success criteria. Feedback is always anchored to a specific criterion.
Enactment tracking — Did the student use the next-step? The system explicitly checks. "You're working on the same thing. Here's another way to think about it."
Teacher dashboard — Class roll, lesson themes (three most common next-steps across the class), and a one-paragraph recommendation for tomorrow.
Student fingerprint — Across a 10-week unit, each student builds a longitudinal record of recurring strengths, recurring next-steps, and trajectory per criterion.
On-site inference — All OCR, feedback generation, and theme synthesis runs on the school's Mac mini. Not a single student work sample leaves the building.
Every IndiLearn tool runs inference on a Mac mini that lives in your server room. No per-token API costs. No student data in someone else's training set. No privacy consent forms for every child.
This isn't a cost-saving compromise — it's the procurement story that cloud-based AI education tools structurally cannot tell. A 600-student school amortises the hardware in months, then pays nothing per query, ever.
IndiLearn tools are grounded in the most replicated findings in educational research. Not buzzwords — actual effect sizes.
One of the largest effect sizes of any pedagogical approach measured across 1,800+ meta-analyses. The gap between this potential and classroom reality is not knowledge — it's logistics. IndiLearn closes the logistics gap.
Nearly three quarters of Australian teachers report moderate-to-high burnout. Workload is the single greatest driver. IndiLearn reduces marking and feedback burden — not adding another platform teachers must manage.
Queensland's 2023 Reading Commitment backed systematic synthetic phonics with a $35M investment, Year 1 Phonics Check, and Australian Curriculum v9.0 rollout. IndiLearn aligns directly to this commitment.
Quality descriptive feedback has one of the largest effect sizes of any pedagogical approach. Three things stop it happening: physically impossible at scale, students don't always apply it, and teacher analysis capacity varies. IndiLearn closes all three gaps.
The federal government's workforce projection, combined with 70% of teachers reporting unmanageable workloads, means the profession needs tools that multiply teacher capacity — not require more of it.
The Australian Framework for Generative AI in Schools identifies student privacy as the key concern for Education Ministers. IndiLearn is the only platform that addresses this structurally — not through policy, through architecture.
The research behind the products. The context behind the decisions.
Australian children's data is uniquely valuable to bad actors. Here's why on-site inference isn't a feature — it's the only responsible architecture for school AI tools.
Nearly three quarters of Australian teachers are burning out. The cause is specific, measurable workload tasks. Technology can reduce them — but only if it's designed to.
The AI-replaces-teachers conversation misunderstands what schools do. Teachers cannot be replaced. But their logistics gap can be closed.
Every IndiLearn design decision maps to a specific, replicated finding in educational research. Here's the evidence behind the products.
Commercial programs that require scripted delivery de-skill teachers and ignore local context. IndiLearn is built to work with schools, not tell them what to do.
Capturing isolated grapheme-phoneme production from young readers is technically unsolved. Here's what we built and why it matters for any classroom.
A 600-student school amortises local AI hardware in months, then pays nothing per query. Here's the architecture decision that changes the economics of school AI.
IndiLearn is in active development. Register your school's interest to join a pilot or receive updates as we approach launch.
No spam. No commitment. Your data stays with us — never shared, never sold.