How we made an AI assistant for analytical dashboards in 3 months

Firstly, the AI assistant for analytical dashboards has to allow users to formulate regular questions in a regular manner, which means to understand human-like input.

Secondly, it should be able to maintain at least 90% accuracy in correctly interpreting queries and providing text and graphic responses that make use of all the dashboards’ data and its correlations.

Finally, the system should handle a large number of users simultaneously.

Step 1. Data audit

First, we checked the client’s data for quantity, quality and regularity to comprehend that the project is feasible and the client can expect the desired outcome.

We also talked to various business representatives to gather the most common requests and pain points.

Step 2. Proof-of-Concept

The quality and quantity of clients’ data were satisfactory. Therefore, we took several dashboards that contained company annual reports, examined the data, and trained LLM to provide answers based on them.

The final LLM choice was GPT 4o, because it showed the best quality and speed in giving answers to human-like questions among other candidates.

Next, we measured the accuracy of the LLM-provided query responses by comparing them to the desired results.

The PoC was presented and the client was highly satisfied. Therefore, it was decided to scale the success.

We continued training an AI assistant on all available dashboards, and planning the solution integration with the client’s corporate system.

Step 3. Development

Data collection pipeline We built a data collection pipeline that detects changes in the database data. This is to ensure that when a new dashboard appears, the metadata—which explains what is stored in the table—is parsed and the information is added to the LLM. Solution pipeline The solution pipeline involved applying the NLSQL method. It is used to process any complexity of human questions and transform them into SQL requests and back. The biggest challenge was the incompleteness of some of the dashboards metadata. Yet, we managed to explain to the LLM their contents through a large number of experiments (adding metadata, or writing cleverly constructed prompts).

Backend and Frontend Meanwhile, frontend developers were busy building a user-friendly interface, and backend developers conducted APIs creation to integrate the AI assistant smoothly into the client’s corporate system. Performance monitoring Finally, we verified the accuracy, usability, and functionality through performance monitoring and testing with real users. User training We also prepared comprehensive instructions, trained the C-level and management representatives to use the AI assistant efficiently and pass on knowledge to their team members.

Project duration: The quality and quantity of clients’ data were good enough to enable quick development. Therefore, the project lasted approximately 3 months.

As it was planned, the solution fulfilled all the client’s requirements and even exceeded the expectations. It enabled better and faster decision-making, decreased managers workload, and allowed for resources, processes and strategy optimization.