InfoMind AI Enterprise: Giving Unstructured Information an Intelligent Mind

Hacktoberfest Open-Source AI Challenge: Ghost in the Machine Submission 👻🌼

This is my submission for the Hacktoberfest Open-Source AI Challenge: Week 4, "Ghost in the Machine".

An open-source AI-powered information management and retrieval system built around local LLMs, hybrid search, and grounded RAG.

Organisations generate massive amounts of information every day through reports, PDFs, spreadsheets, research papers, and internal documents. However, storing information is easy; finding the right information at the right time is difficult.

I built InfoMind AI Enterprise, an AI/ML-powered information management and retrieval system designed to transform unstructured documents into searchable, organised, and actionable knowledge. I started it on 28 October 2026, inside the challenge window.

I interpreted the theme "Ghost in the Machine" as the invisible intelligence operating behind an ordinary information management system. Instead of simply storing documents, the system understands their content, classifies them, retrieves relevant information, and answers questions using the underlying knowledge. And when the documents say nothing, the assistant says nothing: it replies that there is not enough evidence instead of inventing an answer.

The primary objective is to demonstrate how open-source AI can turn a traditional document repository into an intelligent knowledge management system. It is an educational prototype and uses synthetic data only: the demo documents are made-up office files (policies, a backup runbook, a purchase order).

How to test it (about two minutes). The app needs a login, and sign-up is open, so there is no shared password: click Create account, then upload a few files from data/sample/documents (for example backup_runbook.txt, data_retention_policy.txt, acceptable_use_policy.txt ). Wait a few seconds for them to show completed, open AI Assistant and ask:

The API runs on a free hosting plan, so the first request after a quiet period can take about a minute while it wakes.

Explore the complete implementation: https://github.com/Abhay-s-8/infomind-ai-enterprise (MIT licence).

The repository contains the application source code, backend implementation, AI/ML components, tests, and project documentation, including a diagram-heavy explanation of every data flow in docs/PROJECT_EXPLAINED.md.

I designed InfoMind AI Enterprise using a modular architecture combining traditional machine learning, information retrieval, vector databases, and open-weight language models.

The rest: a database-backed job queue, role-based access (admin, analyst, reviewer) enforced on the server, an audit log, live streaming of answers, and an evaluation command. There are 111 backend tests plus a frontend suite, and they run without downloading any model.

A major design decision was to use Ollama with open-weight Llama models rather than making the entire system dependent on proprietary AI APIs. This supports locally hosted inference and makes it easy to select and experiment with models: the model is one setting.

That mattered in practice. With the small default model the assistant wrongly refused a question it could answer whenever a second, less relevant passage was also retrieved: 0 out of 5 tries. Switching to Llama 3.1 (8B) answered the same question 10 out of 10 times, and the app's own evaluation scored 4/4 answerable questions answered and 3/3 unanswerable ones refused.

Open innovation was central to the design of InfoMind AI Enterprise. Using open-weight models and open-source infrastructure made it possible to experiment with the complete AI pipeline, from document processing and embedding generation to retrieval and response generation. It provides several advantages:

One honest note: the hosted demo uses a model called beam, which Ollama describes as an open model built by Reflection. Its page does not state a licence, so the open-weight claim in this post rests on the local Llama models, not on beam.

For me, this project demonstrates that building intelligent systems does not necessarily require relying entirely on closed AI ecosystems.

I built this with an AI coding assistant (Claude Code), and this post was drafted with AI help and reviewed by me. Below is a condensed excerpt of the session covering the Hacktoberfest work: reading the challenge rules, adding support for Ollama's hosted cloud, measuring the model, and testing the live demo as a judge would. Credentials and private paths have been removed.

Building InfoMind for Hacktoberfest Week 4 (condensed excerpt) You Condensed excerpt of the Hacktoberfest-related parts of a longer Claude Code session. Credentials, keys and private paths have been removed; the figures and outcomes are the real ones from the session.

Is this project (InfoMind AI Enterprise, a document archive with a cited RAG assistant) good for Hacktoberfest?

Before answering I read the official guide instead of relying on memory.

topic: hacktoberfest Output Hacktoberfest 2026 no longer counts pull requests. Rewards are virtual stickers (3 = a mailed sticker pack). The weekly DEV Open-Source AI Challenges earn a sticker each. ▸ get_challenges (live challenge list) Input (live challenge list) Output Week 4, "Ghost in the Machine", Oct 26-31, submissions due Oct 31 11:59 PM PDT.

Build a NEW project with open-source AI at its core and publish a DEV post with the #hf26challenge tag. If the app needs a login, give judges test instructions. Correction to something I said earlier: I had described the old pull-request rules.

For 2026 the route is the Week 4 challenge. InfoMind qualifies on timing (first commit 28 October 2026, inside the window) and on the open-source-AI requirement. The theme fit is weaker, so the post should be honest about that, and the demo needs to show the AI assistant working.

The live site has no AI assistant because Render cannot run Ollama. Is there another option, for example Ollama's own hosted cloud?

Ollama's docs say cloud models are reachable at https://ollama.com with a bearer-token API key, which my client could send after a small change. Pricing is not simply free: the free plan has a small monthly allowance for starter models, and other models need pay-as-you-go credits. I could not confirm whether a card is needed, so I told you to check your plan before relying on it.

Added an OLLAMA_API_KEY setting that the Ollama client sends as a bearer token, clear error messages for 401/402/403/429 that never echo the key, a refusal to send the key over plain http to a remote host in production, and a `check-llm` command that lists models and makes one tiny JSON request without printing the key. Output 111 backend tests pass, lint clean. ▸ python -m app.cli check-llm --list OLLAMA_BASE_URL=https://ollama.com, key supplied through the environment, mod... Input OLLAMA_BASE_URL=https://ollama.com, key supplied through the environment, model varied per run Output 23 models listed. beam: first text after 5.3 s, valid JSON. gpt-oss:20b and gemma4:31b: HTTP 402 (credits required on this account).

Only beam worked on this account. My first error message said "Is the model pulled?" for a 402, which was misleading, so I changed it to say credits are required.

Does it actually answer well? Measure it.

Throwaway SQLite database, lexical hashing embedder (as on the live site), 14 synthetic documents, model = beam via Ollama cloud Output Answerable questions answered 4/4, expected evidence cited 4/4, unanswerable refused 3/3, citation validity 100%, mean latency 1.04 s. This is seven questions on a small synthetic corpus, one run, and nobody has reviewed the answers for faithfulness. It shows the pipeline works with this model, not that the answers are reliable in general. I recorded it in docs/EVALUATION.md with those caveats.

OLLAMA_BASE_URL, OLLAMA_LLM_MODEL=beam, OLLAMA_API_KEY (value not shown) Output Deploy live. I also checked that the key does not appear in any commit or tracked file. ▸ live API check sign in, check model status, ask two questions, read the stream event timings Input sign in, check model status, ask two questions, read the stream event timings Output Model status: reachable, beam available. "How long are nightly database backups retained?" -> "35 days" citing backup_runbook.txt (first call 8.0 s). "What is the capital of Australia?" -> refused, no citations. Stream events arrived at 0.39 s (meta), 1.19 s (citations), then text, done at 1.34 s, so streaming works through Render's proxy. The key was pasted into the chat, so I told you to rotate it after the demo.

Can a judge actually try it? Test it the way a judge would.

create a new account, upload 5 sample documents, ask an answerable and an unanswerable question Output Sign-up worked, all 5 documents reached completed, the assistant answered "35 days" with its source passage in 1.5 s and refused the out-of-scope question. No console or network errors. Sign-up is open, so a judge can create their own account and upload the sample files from the repository. I put those steps in the post because the challenge asks for test instructions when a login is needed.

full submission post Output DEV refused the write with no reason given. A minimal draft worked, so I added the text back in sections to find the cause. Bisecting showed DEV's write filter rejected two things: a quoted example of a prompt-injection phrase, and a sentence about keys and the gateway. Describing the injection filter in general words instead, and dropping the key sentence, let the full post through. I published only after you read it and said yes, and checked afterwards that it appears in the public hf26challenge tag listing.

The interesting part of building InfoMind AI Enterprise was seeing how information that initially appeared to be just a collection of disconnected documents could become an interconnected knowledge source.

A user can ask a question in natural language instead of manually opening multiple documents and searching through hundreds of pages. The system retrieves relevant information, connects it to the question, and generates an answer with supporting citations. And an archive you cannot trust is worse than none, so when there is no evidence, the assistant says so.

That is the idea behind InfoMind: giving information a way to become useful knowledge.

Primary challenge: Hacktoberfest Open-Source AI Challenge, Week 4: Ghost in the Machine. I am entering the overall prize only. I am not claiming any partner category, because the project does not use a partner's technology in a way that meets their requirements.

Built with curiosity, open-source technologies, and a belief that AI should make knowledge more accessible.

Project: InfoMind AI Enterprise · Developer: Abhay Singh · GitHub: Abhay-s-8