Zoftware Hireathon

About the challenge🎯 OverviewYou'll be handed a messy, real-world-style software product catalogue. Your job: whip it into shape according to our data guidelines, ingest it, and then build a recommendation engine smart enough to ask the right questions, zero in on what a customer actually needs, and justify its top 3 picks like a human analyst would.This isn't a toy problem; it's a compressed version of a real challenge our product team deals with regularly. We want to see how you think, not just what you can copy-paste.Why JoinSolve a real problem, not a puzzle.This mirrors an actual challenge our product team works on — your solution isn't thrown away at 5pm.Get hands-on with LLM-powered engineering.Build with a live local LLM key — embeddings, reasoning, conversational probing, your call on approach.Fast-track your interview.Standout performers get priority fast-tracked interviews with the hiring team — this is as much an audition as it is a hackathon.Walk away with a portfolio piece.A working recommendation engine with reasoning output is a strong project to show off, wherever you land next.Mentors on the floor.Our engineers will be around throughout the day if you get stuck or want a sanity check — not just judges who show up at the end.Good food, good people.Pizza, wifi, and a room full of people who like solving hard problems.🧩 The Challenge Part 1 - Data Ingestion & SanitizationYou'll receive a sample software product catalogue (intentionally imperfect).Clean, normalize, and validate it against ourproduct data guideline(schema, required fields, formatting rules — provided at kickoff).Ingest the sanitized dataset into a structure your recommendation engine can query.Part 2 - The Recommendation EngineBuild an engine that takes acustomer query(e.g., "I need a CRM for a 20-person sales team that integrates with Slack") and returns thetop 3 product recommendations.Your engine must include aprobing layer— it should ask clarifying questions when the query is vague or under-specified, rather than guessing blindly.For each of the top 3 results, output:Whythis product was recommended (reasoning tied to the actual query/probing answers)What would make it a better fit— i.e., what's missing, or what could improve the match