This is a submission for the MLH x DEV Writing Challenge Asking an AI about your own documents has an awkward trust problem. It gives a confident answer, and you have no idea whether it came from your files or from the model's imagination.
OpenLoom tackles that. You upload your PDFs and notes, ask a question, and OpenLoom answers using only those documents. Every claim points back to the exact passage it came from, like a thread you can pull. If the answer isn't in your files, it says so instead of guessing.
The name comes from weaving: many separate threads of source material become one answer, and you can always trace it back.
The flow is simple:
OpenLoom is a lightweight platform for building and running AI-powered applications that leverage open models. It helps teams connect model workflows, orchestration logic, and deployment pipelines without locking themselves into a single proprietary provider.
OpenLoom gives developers a practical way to:
In simple terms, OpenLoom helps turn model capabilities into usable application features with a clear and flexible architecture.
The open model is used in the model layer of OpenLoom, where it powers inference, reasoning, and task execution in a transparent and portable way. This is typically the place where:
OpenLoom is built entirely from open pieces, and all of it runs locally:
sentence-transformers turns document chunks into vectors.
The key design choice is the prompt. The model is told to answer only from the retrieved passages, cite them by number, and say "not found in your documents" when the evidence isn't there. That one rule is what makes the citations trustworthy.
Retrieval quality matters more than model size. Chunk size and overlap changed answer quality more than anything else, because a chunk that cuts a thought in half gives the model nothing useful to cite. Keeping the file name and page number attached to every chunk made citations nearly free to build.
I built OpenLoom for Hacktoberfest 2026, MLH's open-source AI hackathon, where the goal was to build something new with open-source AI at its core. Building against that constraint pushed me toward local models, and that turned out to be the right call for a tool that handles people's private documents.