RAG & Knowledge Retrieval for Education & EdTech

Most RAG projects fail at retrieval, not generation, the model was fine, the right passage was never fetched. We benchmark retrieval separately, because that is where the accuracy actually lives.

In education & edtech, student data protection and academic integrity constrain what may be automated at all. That single fact reshapes how rag & knowledge retrieval has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is attendance and records automation, usually integrated against student information systems. Integration comes before intelligence. A model that cannot reach your systems of record is a demo with good manners.

Built by engineers who ship production systems, not by a practice that subcontracts the build. Six weeks to something running in production, not six quarters to a strategy document.