Gather Business Insights with Generative AI
Empowers business users to gain insights into historical, current and future states 24/7 without writing any code or SQL query.
Overview
Data never lies. Modern businesses use data to determine whether certain marketing campaigns, products or services are profitable, and what are the greatest expenses might be. These key metrics often help businesses make informed decisions about revenue generation, or to limit their losses. Most enterprises store their data in SQL databases in one form or another. Up until now, the standard approach for interacting with data stores is through SQL languages. Despite a proven effective approach, writing SQL queries require both experts in database and programming languages. This effort could take days or even weeks before business users could use and see the desired results. Imagine a scenario where you could simply ask a chatbot the following question: How is the business performing over the last 2 weeks? and get a response in natural language interactively, rather than a SQL query select sum(amount) from revenue where dates between date1 and date2 to get the same result. With recent advancements in LLM, it is now possible to leverage Generative AI models to create interactive conversation with your data. This demo show case the capability through a chatbot integrated with a Generative AI model, and a SQL database (e.g. Aurora RDS Serverless) so that you could ask questions relevant to the business domain, and get back responses in natural language.
Chat Agent
GenAI models are trained on large corpus of texts, therefore, it’s naturally great at understanding human languages and to carry conversations effortlessly. However, when it comes to SQL queries, these models are not great at the specific task out of the box. To overcome the limitation, we introduce an agent and a pipeline (langchain) to the chatbot which is tasked with interacting with GenAI model. The agent continuously extracts key information from the GenAI model until a relevant SQL query is created. The chat agent then sends the SQL query to the database connected to the chatbot. Upon receiving the result from the database, the agent builds a natural language response based on the SQL result. The chatbot has memory buffer that could remember previously asked questions within the same user session. Integrating a memory buffer could help the agent retrieves information without making extra trips to the database, so that the chatbot could respond user questions as efficiently as possible.
Architecture
The following workflow describes the architecture in detail:
1. A user initiates a conversation with the chatbot by asking a question in the context of business domain.
2. The chat agent formats the question into a prompt to be consumed by the GenAI model.
3. GenAI model responds with a list of potential tables needed to satisfy the SQL query.
4. The chat agent extracts database schema information for the tables suggested by the GenAI model.
5. The chat agent sends the question, along with the database schema to the GenAI model to build a SQL query. 6. GenAI model returns a SQL query.
7. The chat agent submit the SQL query to the Aurora serverless database.
8. The chat agent collect the SQL results, then create a prompt that combines the question and the SQL result to the GenAI model.
9. GenAI model returns a relevant answer.
10. The chatbot formats the response from the chat agent and provide the answer to the user.
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Testimonials
Engineering Manager
Alice Howard
AIGreenSolutions is a trailblazer in the realm of emerging solutions and AI use cases. As a forward-thinking professional constantly seeking innovative approaches to enhance efficiency and productivity.
Interior Designer
Nathan Marshall
From the very onset, AIGreenSolutions distinguished themselves with their commitment to staying on the cutting edge of technology. Our team of experts exhibited a profound understanding of emerging solutions and artificial intelligence, seamlessly integrating these advancements into practical use cases that addressed your unique business challenges.
Architect
Ema Romero
Equally commendable is AIGreensolution's emphasis on transparency and collaboration. Throughout our engagement, we maintained open lines of communication, keeping you informed at every stage of the process. This collaborative spirit instilled trust and confidence in our capabilities, making the entire journey smooth and enjoyable.
Manager
Ann Smith
What sets AIGreenSolution apart is its unwavering commitment to ongoing innovation. In an ever-evolving technological landscape, we remain at the forefront, continuously exploring new possibilities and refining solutions to stay ahead of the curve. This proactive approach instills confidence and assures clients that we are not just investing in today's solutions but in the promise of tomorrow's advancements.
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