About the challenge

🎯 Overview

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)
- What would make it a better fit — i.e., what's missing, or what could improve the match

Judge Accessibility

Estimated Audience

About the challenge

🎯 Overview

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)
- What would make it a better fit — i.e., what's missing, or what could improve the match

Judge Accessibility

Estimated Audience

Zoftware Hireathon

About the challenge

Judge Accessibility

Organizers

Estimated Audience

Quality Score

Zoftware Hireathon

About the challenge

Judge Accessibility

Organizers

Estimated Audience

Quality Score