just shipped the core of Skillship — an AI-powered LMS built for schools.

One platform, many schools, and one rule that shaped every decision: School A must never see School B's data. Tenant-scoped models, filtered querysets at the base class, UUIDs everywhere.

Four AI features live: — Career Pilot: personalised career paths for students — Question generator: chapter PDF in, board-aligned MCQs out — Adaptive quizzes: difficulty follows actual performance — Semantic search: teachers ask in plain English, pgvector finds the material

Django + DRF for the LMS core, a separate FastAPI service for all AI work (Gemini + a pgvector RAG pipeline), Next.js 14 on the front. Django brokers every AI call — the AI layer never touches the browser.

The hard part wasn't the AI. It was making multi-tenancy boring enough that no future dev can leak a school's data by accident.

Open for new work — full-stack builds, AI integrations, RAG pipelines, multi-tenant SaaS. Send me a project.

The tenant boundary is where I'd put the first failure test. I'd try to make a user from one school retrieve another school's record before I trusted any of the AI features.

Client: Pest control company, Lafayette, IN Problem: Ranking page 3–4 for every high-intent keyword that mattered. What we did: Technical cleanup, LocalBusiness/Service schema, AI-answer-ready FAQ content, and a local authority push. Result (3 months): +65% average position (21.1 → 7.3), +161% clicks, +192% impressions. The bigger story: their content was rewritten to directly answer the real questions people ask AI search tools ("who's the best pest control in Lafayette?"). That's the direction local search is heading worth planning for now.