Discover how LLMs revolutionized content creation and management in EdTech, delivering streamlined and automated workflows for better outcomes.
Transitioned from low-code to tailored AI, boosting scalability and user experience
Redesigned with LangChain for improved scalability, accuracy, and customization
We are collaborating with an education-centric company that has developed an AI-driven application to streamline the publishing process. Powered by AI, this company promotes self-publishing and is featured on the Inc. 5000 list.
The company faced limitation of their low-code platform (AirOps) that was used for building an AI flow within the app. They required an in-house expertise to unlock scalability and greater flexibility.
To address the limitations of the existing system and meet the company’s goals for scalability and customization, we reimagined the general AI workflow and architecture. Our efforts were focused on three key areas:
We assessed the existing system on AirOps, identifying inefficiencies in prompt workflows followed by customer experience bottlenecks such as limitations in scalability, customization, and data integration.
Our next step was to redefine the prompt engineering process that lies in the core of the system. There was a multi-step prompts creation process for self-publishing that we improved with AI model optimization and prompts refining for accurate data extraction and generation.
Ensring the stable work of the system, we designed a scalable solution using LangСhain. The new solution now includes modules for topic research, customer insights, book outlining that boost end-users experience for publishing. This new architecture ensures better customization, usability, and performance.
The transition to custom AI solution brought significant improvements in several key areas, addressing the limitations of the previous platform and delivering tangible benefits to the company. These included enhanced cost efficiency, scalability, and usability.
Transitioning to LangChain-based solutions reduced operational costs.
The previous platform lacked scalability and customization, limiting project growth. By moving to Langchain, the company can now scale their application effectively.
The prior low-code platform was unsuitable for users unfamiliar with LLM solutions. The new system offers better flexibility and usability for both technical and non-technical users.
We have completed the first iteration of the system and are continuing to refine and improve its performance based on ongoing feedback and needs.
Technology, Information and Internet
USA
LangСhain, LangSmith, LLM, Python, Argilla
Ongoing
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