Clinical requirements analysis
Digitized and "smartized" requirements data
The clients' software design requirements with critical domain knowledge are captured following guided process. AI technology is used to digitize and organize these information to build knowledge base structure. Compared to a traditional database, the smart structure hosts information hierarchy in the context of specific application, which resolves a key challenge in knowledge discovery. This empowers the client's team to be less dependent on domain experts, shorten the learning curve and free up developers to focus on actual programming of software features.
Comprehensive relationship built for software features & design factors
Using AI algorithm, the smart knowledge base builds comprehensive relationship between software features and design factors in order for developers to gain clear and complete view of design impacts. This significantly reduces design risks and defects as it complements human's capabilities in the process. Clients can easily view, modify, update and manage the data afterwards to keep them accurate, up to date and compliant.
critical design factors identified, prioritized and managed for analysis
The smart knowledge basel can identify critical design factors and prioritize them for analysis. This is important for removing root cause for defects as well as guiding testing strategies.
domain knowledge retained, enhanced and managed in-house
Once the knowledge base is built, managed and run for a given software product, further analysis can be conducted to generate comprehensive design and testing stories. The knowledge base will keep evolving and become smarter with more learning gained through knowledge feed.
Digitized and "smartized" requirements data
The clients' software design requirements with critical domain knowledge are captured following guided process. AI technology is used to digitize and organize these information to build knowledge base structure. Compared to a traditional database, the smart structure hosts information hierarchy in the context of specific application, which resolves a key challenge in knowledge discovery. This empowers the client's team to be less dependent on domain experts, shorten the learning curve and free up developers to focus on actual programming of software features.
Comprehensive relationship built for software features & design factors
Using AI algorithm, the smart knowledge base builds comprehensive relationship between software features and design factors in order for developers to gain clear and complete view of design impacts. This significantly reduces design risks and defects as it complements human's capabilities in the process. Clients can easily view, modify, update and manage the data afterwards to keep them accurate, up to date and compliant.
critical design factors identified, prioritized and managed for analysis
The smart knowledge basel can identify critical design factors and prioritize them for analysis. This is important for removing root cause for defects as well as guiding testing strategies.
domain knowledge retained, enhanced and managed in-house
Once the knowledge base is built, managed and run for a given software product, further analysis can be conducted to generate comprehensive design and testing stories. The knowledge base will keep evolving and become smarter with more learning gained through knowledge feed.