Muhammad Mubashar@muba12
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2+ years of experience
About me
Muhammad Mubashar Usman is a Computer Science undergraduate with a passion for building technology that makes an impact. With hands-on experience across software development, data science, and AI/ML, he brings a well-rounded technical foundation to every project he takes on. Proficient in Python, C++, and SQL, Muhammad has applied his skills across multiple domains — from developing functional applications to exploring the possibilities of artificial intelligence and machine learning. His intermediate-level experience reflects a developer who has moved beyond theory and into real-world problem solving. Currently pursuing his bachelor's degree, Muhammad is driven by curiosity and a commitment to continuous learning. He is eager to collaborate with like-minded innovators, contribute to cutting-edge AI projects, and grow as a builder in the global tech community.
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BrainOS — Enterprise Knowledge Execution Platform
Every company runs on knowledge scattered across policies, SOPs, and the heads of whoever's been there long enough to remember the exceptions. AI agents don't have that instinct. Most "enterprise AI" tools just search documents and hand back a paragraph — which isn't enough for an agent to safely act on its own. BrainOS reads uploaded company documents and builds a structured company brain: entities, rules, and reusable Skill Cards — an auditable, executable definition of a business process, with its inputs, conditions, and action. Ask BrainOS a real question, and it retrieves the matching skill, checks each condition against the situation, and returns an answer with a full step-by-step reasoning trace, not a black-box response. The pipeline runs document upload, text extraction, LLM-based entity and skill extraction, vector storage in Qdrant, and agent reasoning, all through a provider-agnostic API layer. During development we used the Google AI Studio (Gemini) API; for judging, the same code path points at Fireworks AI, which serves inference on AMD Instinct GPUs, via a one-line environment swap. The working prototype: upload a policy document, watch entities and skills extract in real time on the dashboard, inspect the generated Skill Card, then ask a live question and watch the reasoning trace resolve. Not a search tool wrapped in a chat window — the structured knowledge layer AI agents need to act safely inside a real company.
13 Jul 2026
