Zoftware Hireathon

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About the challenge — You'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 Join — Solve 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 & Sanitization: You'll receive a sample software product catalogue (intentionally imperfect). Clean, normalize, and validate it against our product 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 Engine: Build an engine that takes a customer query (e.g., "I need a CRM for a 20-person sales team that integrates with Slack") and returns the top 3 product recommendations. Your engine must include a probing 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: Why this product was recommended (reasoning tied to the actual query/probing answers), and What would make it a better fit — i.e., what's missing, or what could improve the match.

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