bashwerkz

Most document Q&A tools start and end with a chat box. Upload a PDF, ask a question, get an answer. That works for one person — it doesn't work for a team managing thousands of documents across different roles and access levels.

We built a platform that handles the whole workflow.

The Challenge

We didn't start with a platform. We started with three separate needs that converged:

Document Q&A at scale. Educational teams needed to turn PDFs and images into conversational answers, with access boundaries — students and admins couldn't share the same view.
AI-assisted learning. Training teams wanted more than search. They needed a structured ingestion pipeline for course materials with an AI layer that could explain, summarize, and quiz on domain-specific content.
Content delivery with embedded intelligence. A full-stack web presence that combined marketing content — blog, podcast, portfolio — with an embedded AI document chat, deployed cleanly in production.

The common thread was clear: ingest unstructured documents, store them intelligently, retrieve answers conversationally, and serve different users differently.

Our Approach

Semantic ingestion pipeline. Documents enter through a unified ingestion layer that handles PDFs and images. ChromaDB stores embeddings for semantic retrieval — queries find conceptually relevant content, not just keyword matches. A multimodal LLM layer means uploaded diagrams and scanned documents can be reasoned about, not just text-extracted.

Role-based access model. The platform layers permission boundaries from the start. Admin users manage the knowledge base, configure retrieval parameters, and oversee user access. End users interact through role-scoped Q&A interfaces — a student sees training materials, an admin sees everything. An admin dashboard surfaces usage metrics, document health, and user management.

Learning engine layer. Beyond basic Q&A, the platform supports structured learning workflows: bulk PDF ingestion for training modules, AI-generated summaries and explanations, and ChromaDB-backed retrieval scoped to specific knowledge domains. The system shifts from "answer this question" to "help me learn this topic."

Full-stack delivery. The entire application ships as a Dockerized stack — Svelte frontend, Node.js backend, ChromaDB vector store — with Nginx routing in production. The same deployment powers the content marketing layer: blog, portfolio, and contact sections live alongside the document chat, all from a single codebase.

Results

The platform handles three use cases from a unified architecture: document Q&A for education teams, AI-assisted training for learning workflows, and a content-rich web platform with embedded intelligence. ChromaDB semantic retrieval ensures answers are contextually relevant rather than keyword-dependent. Role-based access keeps knowledge scoped to the right users. The Dockerized pipeline means the entire stack replicates from dev to production with a single command.

Tech Stack

ChromaDB — semantic vector storage and retrieval
Svelte + Node.js — full-stack application layer
Multimodal LLM — text and image reasoning for document Q&A
Docker + Nginx — containerized deployment and production routing
RAG pipeline — ingestion, embedding, retrieval, generation