When Engineers Cross Boundaries: How “Boundary‑Breaking” Boosted Problem Solving at Dewu

In the Dewu technology department, several engineers recently ignored traditional role boundaries, opting to engage directly with users and operations instead of following formal requirement documents. This “boundary‑crossing” approach, though unconventional, consistently produced more thorough solutions.

Case 1 – Beichen, data development engineer : Assigned to the Zhili AI platform, Beichen found the initial product goal—building a generic Q&A tool—insufficient. He first interviewed dozens of seed users, discovering that most requests centered on cross‑table joins and fuzzy intent detection, requiring AI‑driven data assets. The team pivoted from a generic agent to a structured intent‑recognition system with a knowledge base. After launch, a user remarked that they no longer needed custom reports or manual data stitching, confirming the new direction.

Beichen emphasized the team’s culture of “run a small sample, let results speak.” Early prototypes were released despite imperfections, allowing rapid feedback and learning from failures, which he considered more valuable than flawless projects.

He also initiated the AI‑TAG project, a rule‑based labeling tool that automatically tags user feedback in seconds, replacing labor‑intensive manual review. This further demonstrated the benefit of tackling real‑world problems directly.

Case 2 – Dongfang Shuo, backend engineer : While working on a merchant AI assistant, Dongfang realized the product was not addressing merchants’ core pain points. He bypassed the stagnant requirement doc, consulted product and operations teammates, and identified recurring high‑frequency issues such as deposit payments, product reviews, and onboarding hurdles.

He proposed an “intelligent partner” rather than a simple chatbot. Lacking LLM expertise, he completed an intensive AI training, learning about large‑model fundamentals, prompt engineering, and the concept of “Harness”—a rule‑based framework that guides model output, analogous to the Dujiangyan water‑diversion system.

Applying this, Dongfang and his team built a knowledge‑base pipeline, cleaning and classifying hundreds of frequent merchant questions, then designing response logic. After deployment, the “merchant growth path” feature automatically answered 60% of common queries, reduced support pressure by 30%, and cut new‑merchant onboarding time by 50%.

When adding an intelligent recommendation module, the team introduced a validation mechanism to ensure accuracy, ran a week‑long prototype, and encountered typical pitfalls such as cold‑start inaccuracies and filter bubbles. Their gray‑release system allowed quick rollback and issue resolution.

Both engineers’ stories illustrate that stepping beyond formal boundaries—talking to users, iterating quickly, and embracing failure—expands the effective problem‑solving space and yields concrete performance gains.