UNI Cloud: Summary of Resource Integration and Scenario based Implementation Practice

As a product manager, I led the design of the core functional modules of the UNI Cloud AI middleware. By integrating internal APIs and computing resources, I quickly addressed the AI capability gaps in text and image recognition, ultimately achieving the benchmark launch of products comparable to those of Alibaba Cloud and Tencent Cloud. Below is a genuine summary based on the project practice:

Project Background and Strategic Positioning

UNI Cloud faced challenges of fragmented AI capabilities and insufficient scenario coverage in the AI field, urgently needing to build a cloud service product comparable to Alibaba Cloud's Visual AI and Tencent Cloud's AI Open Platform. As the project manager and product manager, my core objectives were:

• Capability enhancement: Launch core functions such as OCR and image classification within the set period.
• Resource reuse: Quickly realize product capability invocation and implementation by calling internal APIs and computing resources.
• Scenario specialization: Focus on vertical fields such as finance and government affairs, providing scenario-based solutions like invoice recognition and document OCR.

Core Contributions of the Product Manager

1. Technical Resource Assessment and Cooperation Model Design

• API capability research: Evaluate the performance of internal AI API interfaces (e.g., OCR response time, recognition accuracy rate), and establish a "demand pool - scheduling table" mechanism within the team to ensure that new APIs prioritize meeting business scenarios.

2. Requirement Alignment and Process Optimization

• Scenario-based requirement decomposition: Determine invoice recognition and document OCR as the first batch of functions to go live. Define "field mapping specifications" to unify the data format of different business systems (e.g., retain two decimal places for amounts).
• Cross-department collaboration mechanism: Establish the role of "technology - business translator" to convert customer needs into API invocation parameters (e.g., "automatically distinguish the front and back of ID cards" is transformed into image orientation detection parameters).

Functional Modules and Business Value

Module	Internal Technical Resources	Key Actions of the Product Manager	Business Value
Invoice Recognition	Invoice Recognition OCR API	Define field mapping rules, exception handling logic	Processing efficiency increased by 5 times
Document OCR	Self-developed ID Card Recognition API	Design multi-document mixed recognition strategy, re-entry process	Support for 20+ document types, accuracy rate over 90%
Image Classification	Internal Model	Determine model fine-tuning parameters, deployment node configuration	Product classification accuracy rate of 96%

Project Outcomes and Data Validation

• Technical launch: Timely completion of the launch of 2 AI capabilities and their publication on the UNI Cloud platform.

Challenges and Solutions

• Inefficient cross-department collaboration: Long-term inability to quickly make decisions on requirements from the client side, resulting in significant waiting and modification costs in the project, which indirectly affected the project's fund collection.

Industry Insights and Future Directions

• Product Manager's Mindset as a Resource Integrator: Transition from a "function designer" to an "ecosystem architect," creating value by connecting internal APIs with external scenarios.

Conclusion

This experience has profoundly taught me that in the AI cloud service market dominated by giants, the core value of a product manager lies in "connection": connecting internal technical resources with external business scenarios and linking customer needs with commercialization paths. Through the full-chain design of "resource assessment - requirement alignment - customer success," we not only achieved technological breakthroughs but also established competitive barriers in vertical fields such as finance and government affairs.

(Note: The data involved in this article has been desensitized or obfuscated, and some technical details have not been disclosed due to confidentiality agreements)