A docs assistant a small business can run on its own laptop, built with open-weight AI
*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle] hacktoberfest-week2-2026-10-12 What I Built PumpGuard assistant : a question-and-answer assistant that answers from a company's own documents (manuals, FAQs, error-code tables), cites the page it used, and sa
Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 馃捀
*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle] hacktoberfest-week2-2026-10-12
PumpGuard assistant: a question-and-answer assistant that answers from a company's own documents (manuals, FAQs, error-code tables), cites the page it used, and says "I could not find that in the knowledge base." when the answer isn't there.
Who would pay for it? Small businesses that sell a technical product and answer the same support questions every day: "What does error E42 mean?", "How do I reset the sensor?". They have the answers in PDFs and wikis, but:
The side hustle is a setup service: I install the assistant on the customer's own machine or server, load their documents, and hand over a small dashboard that shows which questions it could and couldn't answer. A one-time setup fee plus a small monthly fee to add new documents.
For this build, the product and its knowledge base are fictional (the "PumpGuard" pump monitor, 10 pages cut into 26 chunks), so I could build and test everything without touching any real company's data.
It also grew out of something bigger: I'm a developer becoming an AI engineer in 6 months, in public, and this assistant is the project my free learn-along course builds from Day 1 to the end.
https://www.youtube.com/watch?v=AGSYcwMQ-1Y
The video above introduces the course and the project behind it. The course itself is live: dev-to-ai-engineer.vercel.app
flowchart LR user([Browser]) --> web[React chat page] web --> gateway[ASP.NET Core gateway] gateway --> ai[FastAPI AI service] ai --> db[(PostgreSQL + pgvector)] ai -.-> model{{open-weight model on your own machine}} How I Built It Open-source AI sits at both ends of every answer:
Around it: a.NET gateway (input checks, request ids, clean errors), a React chat page with thumbs up and down, and a dashboard.
What I have not run yet (I'd rather say it than hide it): the Docker images and the CI workflow were checked but never built or run, and the local model path was run once, on my own Mac. By default the tests use a stand-in writer, so they run with no key and no cost.
Because for my customers, the documents are the business.