About
When we found our office operations team overwhelmed by repetitive information requests, we envisioned an AI-powered solution to streamline internal communication. Enter Mobegí, a smart chatbot designed to understand and answer employee questions by tapping into the company's vast knowledge base. The goal? To free up the operations team for more strategic tasks while providing employees with instant, accurate information. This initial phase served as a proof of concept, demonstrating Mobegí's potential to revolutionise how companies manage and distribute internal knowledge.
Challenge
Our team faced several significant hurdles during the project. The primary challenge was ensuring Mobegí could understand and accurately answer a wide range of questions using the company's existing documentation. Additionally, maintaining data security and privacy while processing sensitive information proved complex. Lastly, creating a user-friendly interface that employees would actually prefer over asking human colleagues required careful consideration.
Process
The journey began with a thorough analysis of the company's current knowledge management system and employee query patterns. Our dedicated team consisted of Generative AI specialists, data engineers, and UX designers, supported by a project manager. With a twelve-week timeline, we set out to create a functional prototype capable of answering questions based on the company's internal documentation.
From the outset, our specialists focused on implementing a Retrieval-Augmented Generation (RAG) system. This approach allowed Mobegí to pull relevant information from the company's knowledge base and generate contextually appropriate responses. The team continuously refined the retrieval and generation processes, balancing accuracy with response speed.
One innovative strategy developed by our team involved a multi-pass anonymization process. This ensured that sensitive information was protected during processing while maintaining the context necessary for accurate responses. The system was designed to anonymize input queries, process them, and then de-anonymize the responses, all in real-time.
Meanwhile, our data engineers tackled the challenge of efficiently indexing and searching the company's documentation. They implemented a sophisticated chunking strategy and explored various embedding techniques to optimise retrieval accuracy. This involved careful consideration of document structure, content relevance, and query patterns.
The UX design team played a crucial role in making Mobegí approachable and easy to use. They integrated the chatbot into the company's existing Slack workspace, allowing employees to interact with Mobegí through a familiar interface. This design choice significantly lowered the adoption barrier and encouraged regular use.
Solution
In just twelve weeks, our team brought Mobegí to life, achieving several key milestones along the way.
We successfully implemented a RAG system capable of understanding and answering a wide range of employee questions with high accuracy. This system effectively bridged the gap between the company's extensive documentation and the specific information needs of employees.
To ensure data security, we developed a robust anonymization pipeline that protected sensitive information without compromising Mobegí's ability to provide relevant answers. This approach allowed the system to handle confidential queries while maintaining strict privacy standards.
Our team integrated Mobegí seamlessly into the company's Slack environment, creating a user-friendly interface that employees could access without leaving their primary communication platform. This integration strategy led to rapid adoption and positive user feedback.
We implemented a feedback mechanism that allowed users to rate Mobegí's responses and provide comments. This feature not only helped improve the system over time but also gave the operations team valuable insights into common questions and potential gaps in the company's documentation.
What's Next?
With the success of the initial prototype, we're excited to continue developing Mobegí. Our roadmap includes several ambitious enhancements:
We're exploring advanced retrieval techniques, including hypothetical question generation, to further improve Mobegí's ability to understand and answer complex queries.
We plan to implement a multi-source document ingestion system, allowing Mobegí to incorporate information from various formats and languages, expanding its knowledge base.
To enhance Mobegí's efficiency, we're investigating verified answer caching strategies that can reduce response times for frequently asked questions while maintaining accuracy.
We're developing a sophisticated routing system that can seamlessly escalate complex queries to human experts when necessary, ensuring that employees always receive the best possible support.
Stay tuned for updates as we continue to evolve Mobegí into an indispensable knowledge management tool for modern enterprises!
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