Case Studies

AI Chatbot For Smart Database Search

3 months
Less time spent on searching data
Chatbot UI
AI Chatbot For Smart Database Search

Project Summary

An AI-powered smart search in a company's database. Inquiry-based information search, multiple data access levels, chatbot UI.


AI development
Cloud engineering


1 Project manager
2 AI developers
1 QA engineer

Target Audience

Large companies with 100+ employees


Our client is a large company with hundreds of employees, multiple departments, and dozens of ongoing projects at any given time. Each department logs the progress on projects, approaches and work experience into a company-wide database — a collection of information on each completed and ongoing project. Employees use the database to get updates on projects from other departments and exchange work experience.

The knowledge base contains thousands of documents, making searching for relevant pieces of information difficult. Coupled with confidential data and multiple levels of data access rights, the database is an inefficient tool for data exchange.

The company approached us to develop a smart search system for the database which would help employees find information faster as well as restrict access to confidential or sensitive information based on the employee access level.


The chatbot-like search module lends itself perfectly to the use of natural language processing models. However, given the confidential nature of the information stored in the database, using models like ChatGPT is not an option due to data leaking concerns.

We have implemented a different approach to natural language processing.

Chatbot For Smart Database Search

ChatGPT is the most powerful language model in the world, but it doesn’t mean there are no other solutions to processing natural language. Taking the project requirements in mind, we have implemented multiple smaller language models which perform just as good in this particular application.

The smart search system is powered by a collection of multiple smaller language models. When a user asks a question, the system uses a retrieval language model which looks for parts of text which might contain the answer. After the texts have been located, a reranking model ranks them in order of relevance. The system can also combine text from multiple documents to provide a complete answer.

The system also restricts access to sensitive information based on employee access level or department.


The smart database search decreased the time employees spend looking for information by 35%, not only making the search process more efficient, but helping employees get a deeper understanding of the company's processes and projects.

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