Learning Companion
A learning system that connects source material, questions, synthesis, project decisions and long-term knowledge.
Challenge
What needed to be solved
Learning tools often stop at summarisation. The real problem is carrying an idea from source material into understanding, action, review and future reuse.
Approach
How the product was framed
The companion treats learning as a connected workflow: import material, identify the important questions, compare perspectives, create an action, record what changed and preserve the result in a durable knowledge base.
01 · Learning loop
Connect input to action
Every learning item is linked to a question or project. This prevents summaries from becoming an isolated archive and makes it easier to see why the material matters.
- Source capture from documents, pages and video
- Key concepts, differences and high-value insights
- Action items connected to current projects
- Reflection notes that update the knowledge base
02 · Memory
Build a project history
The system stores not only final notes, but also questions, rejected directions and decisions. That history gives future AI sessions more useful context than a flat collection of summaries.
03 · AI role
Support judgment, not replace it
AI helps compare sources, expose gaps and propose next questions. The user remains responsible for deciding what is important and how the learning changes the project.
Outcome
The framework established a practical MVP centred on project-linked learning records, a database-backed history and a repeatable reflection workflow.
Learning
Knowledge compounds when the system preserves relationships: between a source and a question, a question and a decision, and a decision and the next experiment.