Internal Database Chatbot for Enterprise

The brief

The client needed a solution to allow employees to query internal databases using natural language.

The goal was to improve data accessibility, speed and reduce the need for technical expertise in querying databases.

The challenge

The challenge was to create a system that could understand and respond to natural language queries about the company's internal data, while ensuring data accuracy and security.

• Translating natural language queries into structured database queries accurately
• Ensuring the chatbot could handle different phrasings and synonyms for all possible queries
• Maintaining data security and access control, so sensitive information is only accessible to authorized users
• Providing clear and understandable responses to users, even when the underlying data is complex or technical

How we approached it

We approached the chatbot development by first understanding the structure of the internal databases and the types of queries employees would need to make. We then designed a natural language processing (NLP) model that could accurately interpret user input and map it to the appropriate database queries.

We implemented a robust error handling mechanism to ensure that the chatbot could gracefully handle unrecognized queries or ambiguous input, providing helpful feedback to guide users towards successful interactions.

We also incorporated security measures to ensure that the chatbot only provided access to data that the user was authorized to view, and we implemented logging and monitoring to track usage and identify potential issues.

What we built

A web-based chatbot interface that allows employees to input natural language queries and receive structured responses based on the internal databases. The system includes an NLP engine that translates user input into readable queries, retrieves the relevant data, and formats it for easy understanding.

We also built an administrative dashboard for managing user access, monitoring chatbot performance, and updating the NLP model as needed to improve accuracy and responsiveness.

Results

The figures below were measured against the client's previous manual process of querying databases.

In use

The chatbot is now actively used by employees across the organization, significantly reducing the time and effort required to access internal data. It has improved decision-making by providing quick and accurate responses to queries, and has reduced the reliance on technical staff for database access.