Customer Stories

Boosting Customer Satisfaction With Quick and Accurate Responses

Foundation model
End products
End products
Database querying
1 AI Agent

Answer queries using natural language

Easy access for non-technical users

Combined key sales information from various sources

Improved customer satisfaction

Quicker response times with accurate information



  • Sales representatives struggled to access relevant sales data scattered across multiple platforms;
  • Finding relevant sales information was slow and required multiple steps;
  • Sales representatives lacked the technical skills needed to effectively query the database.


  • Implemented Database AI to let sales representatives extract data from a single database;
  • Integrated a vector database to enhance the speed and accuracy of query responses;
  • Provided an interface in which sales representatives could make natural language queries which were then translated into an appropriate Structured Query Language (SQL) query by the Agent. This allowed the database to be accessible to non-technical users.


  • Developed Database AI to answer natural-language queries in natural language;
  • Combined information from various sources;
  • Sped up data retrieval to help the sales team make faster decisions;
  • Increased user satisfaction with quick and accurate responses.


This sales company offers services for go-to-market strategy.

Their sales data was scattered across different platforms and difficult to access. Sales representatives struggled to query this dispersed data due to a lack of technical skills, leading to delays in answering customer queries.

They partnered with us to develop an AI Agent that can interact with a database through natural language queries. It consolidated information from multiple data sources, letting non-technical users access data through a simple interface.

Today, the client benefits from a significant boost in workflow efficiency, as they can access the needed information quickly and with fewer steps. They also improved customer satisfaction by providing quick and accurate responses and reducing wait times.

Fragmented Data Slows Down Decision-Making

Quickly accessing up-to-date information like customer contact details and account history is important for making informed decisions and answering customer queries in the sales industry.

However, the client’s sales representatives spent a lot of time searching for information across multiple platforms. This slowed down their workflow and required technical knowledge and skills they didn’t have. Customers were receiving slow responses to their queries as a result of the scattered information.

This meant the client faced two main problems:

  • Difficulties in data handling
  • Lack of suitable automation solutions

Difficulties in data handling:

The client’s main challenge was the inefficiency and difficulty in accessing sales data scattered across multiple platforms. Sales representatives found it time-consuming to sift through various systems to find relevant information. Additionally, a lack of technical knowledge made sales representatives unable to query the database and find relevant information.

This process was inefficient and highly prone to human errors since it relied on the user’s ability to navigate complex databases.

Lack of suitable automation solutions:

Another issue was that the client had no effective way to automate their data access workflows. Existing tools didn’t cater to the specific needs of non-technical users who wanted a simpler interface for interacting with complex sales data. Traditional automation solutions lacked the capability to understand natural language queries, which made them unsuitable for users without technical backgrounds.

They needed an AI solution that was simple to use and combined information from various sources into one database. This would let the client’s sales representatives easily find key sales information whenever they needed it.

Creating a Unified Database to Answer Customer Queries With One AI Agent

To address these issues, we developed an AI Agent capable of understanding and processing natural language queries.

Here are the steps we took to implement the solution:

  • Database optimization: The initial phase involved a thorough analysis and restructuring of the client’s existing database. This involved revising table and column names to be more descriptive and intuitive, making it easier for the AI Agent to handle data and process queries.
  • AI Agent development and training: We then developed the specialized AI Agent, Database AI, using advanced natural language processing (NLP) technologies that help it understand and execute natural language queries, which could then be executed on the database. We integrated the agent within the optimized database environment to allow for direct interaction with the data. Moreover, we equipped it with a set of instructions. These instructions covered different aspects of the database to ensure comprehensive data management capabilities.
  • Implementation: We used Pinecone's vector database capabilities to improve the speed and accuracy of data retrieval. Additionally, a Retrieval Augmented Generation (RAG) system was integrated to enable the AI Agent to pull contextual information from different sources. This helped provide more accurate responses to customer queries.
  • Natural language conversion: Our system lets non-technical users provide queries using natural language. Database AI determines the appropriate SQL code for each query, which then gets executed on the database to retrieve information. The desired information is retrieved and returned through a natural language response. 
  • Prompt engineering: We then refined the interaction between the AI Agent and the database, ensuring customers receive accurate responses to their queries. Using a set of questions provided by the client, we tested the system to fine-tune the AI Agent’s responses and ensure that the right information was returned.

This process provided the client with a solution that lets their sales representatives and customers find key information using simple natural language inputs.

Enhanced Efficiency and Data Processing Improve Sales Operations

Our AI Agent improved the way the client interacts with their sales data, combining all the necessary information from various sources.

It lets the client’s sales representatives provide natural language queries and immediately receive a response. As a result, they can access critical information without needing any technical knowledge.

Additionally, the client saw a massive time reduction when retrieving and analyzing sales data, allowing them to answer customer queries accurately within minutes.

They went from scattered information across various sources that required technical skills to locate, to a user-friendly interface that only requires natural language inputs in just 8 weeks.

Encouraged by the success, the client currently has the AI Agent live on their platform as part of their product offering to answer customer queries. We are currently discussing the implementation of the second version of the AI Agent, which would be able to answer more complex questions and generate charts as part of its responses.

Foundation Model
Product Types
Database querying
Use Case
Information retrieval

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