Case Studies

Challenge: 

The client needed to improve its market analysis and risk assessment processes. 

Solution: 

We developed AI/ML models to enhance market statistics production, enabling the client to better monitor and analyze market trends and risks. 

Outcome:

The client experienced improved efficiency in market analysis and risk identification. 

Challenge:

The client required high-quality data to ensure regulatory compliance and effective enforcement. 

Solution:

Our team provided data quality assurance and data processing services to enhance the client's data accuracy and reliability. 

Outcome: 

The client benefited from better data quality, enabling it to maintain regulatory compliance more effectively. 

Challenge: 

The client sought to modernize its ERP and CRM solutions to meet the evolving needs of its clients. 

Solution: 

We developed customized software solutions, including AI-based retrieval and generation for efficient document management. 

Outcome: 

The client experienced streamlined project management and improved productivity. 

Challenge: 

Clients faced challenges in efficiently searching and analyzing large volumes of data. 

Solution: 

We implemented retrieval-augmented generation (RAG) solutions using large language models (LLM) for customized prompt-based searches. 

Outcome: 

Clients gained faster and more accurate access to relevant data, enabling better decision-making. 

Challenge:

The client needed predictive insights to enhance its procurement operations. 

Solution: 

We created a predictive model to estimate contract award time and a tool for identifying risky vendors. 

Outcome: 

The client optimized its procurement processes, reducing risks and increasing operational efficiency. 

Challenge: 

Clients' existing RAG (Retrieval-Augmented Generation) models provided answers but often lacked precision and failed to account for the relationship between entities.

Solution: 

We introduced a Graph RAG solution by incorporating a Neo4j graph database. This allowed us to map relationships between entities in the knowledge graph, improving the understanding of entity connections and enabling more accurate answer generation.

Outcome: 

The client experienced significantly improved accuracy in generated answers, with the solution leveraging relationships between entities to provide contextually relevant and precise responses.

Challenge: 

The client’s chatbot frequently delivered inaccurate responses, leading to a poor user experience and user dissatisfaction.

Solution: 

We enhanced the chatbot’s architecture by integrating RAG model to address queries related to specific company content. Additionally, we implemented a Semantic Router to classify each query accurately, generating personalized responses based on user input.

Outcome: 

The client observed a marked improvement in user experience, with the chatbot providing accurate and friendly conversation responses, resulting in enhanced user satisfaction and engagement. 

Challenge: 

The client’s chatbot struggled to manage multiple functionalities effectively, often failing to complete tasks due to the vastness of each functionality.

Solution: 

We enhanced the chatbot's architecture by introducing a classification layer that accurately categorizes each query. Following classification, we implemented LangChain's multiple agents, assigning at least one agent to each class. Each agent was equipped with a variety of tools tailored to meet the specific requirements of the query, allowing for detailed and accurate final responses.

Outcome: 

The client experienced significant improvements in the chatbot's ability to handle multi-layered, complex queries accurately and completely, leading to enhanced user satisfaction and overall performance.