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Hello I'm new here. Currently working on my Ai decoder project. My stacks, python, RestApis, N8n and LLMs
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Built the complete Admivo.in ecosystem from scratch for a foreign education consultancy, covering the student-facing platform and internal operations. The system includes country/destination discovery, application journey, counsellor workflows, resource libraries, live admission updates, document management, sessions, and an admin dashboard for managing the entire ecosystem.
CSV preparation — an independent Python workflow for splitting tagged records and mapping columns to a supplied schema.
The synthetic example preserves text IDs and multiline fields, stops on unknown tags, and reports output counts and file hashes. Multi-tag routing is explicit, so extra output rows can be explained rather than mistaken for duplicates.
Code and worked example: https://github.com/sjdoane/csv-tag-splitter
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
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— 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.