CASE STUDY

Revolutionizing HR Policy Management: A Generative AI Solutions for a Logistics Company

Industry

Transport & Logistics

Work Done

AI ML Consulting, Business Intelligence, Data Analytics,

In the modern corporate setting, effective HR policy management is one of the key elements in ensuring organizational governance and contentment among employees while creating an environment that allows business to run smoothly.

Client Overview

One of the giant companies in logistics and services, from Chicago, USA was dealing with the daunting task of managing an immensely large portfolio of HR policies. These policies, ranging from holiday allowance to appropriate behavior at the workplace acted as a compulsory platform for this company’s operation.

The company has 1 million customers and has a turnover of $5 billion. The company has 5,000 employees.

Challenge

The sheer number and variety of the HR policy documents posed a challenge to the logistics firm. Such a blizzard of policies was becoming more and more difficult to interpret, understand its nuances, and always keep up-to-date with. The organizational challenge was primarily motivated by the need for an effective policy retrieval system which demanded more understanding to reduce HR personnel’s workload and improve efficiency.

Initial Meeting

We established an understanding of the requirements and challenges faced by the client during our first meeting. It allowed us to gather insights directly from the client, shaping the subsequent work towards the solution.

Solution

Aware of the need for this transformative solution, our team embarked on a quest to use Generative AI: an innovative technology that revolutionized automation and decision-making processes across many industries. The HR Policy Document Query Assistant, an innovative tool meant to renovate policy management was the core of this solution.

Architecture and Implementation of LLM Models In order to empower HR Policy Document Query Assistant, we adopted a Linguistic Learning Machine which is one of the latest machines that can be used in linguistics processing. With the addition of a document loader feature, data processing is greatly simplified: The converted text from PDF documents can now be read. The use of this advanced tool allowed one smart planner to greatly simplify dealing with policies by making policy data more available.We investigated gte-base;gte large, bge base en, and as well as aque Large En in the hierarchy of open-source models for document embeddings and question answering. The large model emerged as the optimal choice for embeddings, striking a balance between performance and efficiency. The Pinecone vector database played a crucial role in managing and retrieving embeddings efficiently, offering superior performance and ease of use.
Prompt Construction/Retrieval Recognizing the limitations of traditional prompt methods, we delved into advanced techniques to enhance LLM’s understanding and responsiveness. The orchestration framework, LangChain, facilitated seamless integration by enabling communication with external APIs, retrieval of contextual data from vector databases, and memory maintenance across LLM interactions. The introduction of LLMChain and ReAct within LangChain brought intelligence and adaptability to the system. ChatGPT played a pivotal role in abstracting prompts and maintaining state, contributing to a simplified alternative for prompt construction. The advanced techniques and orchestration capabilities collectively elevated the intelligence and responsiveness of the HR Policy Document Query Assistant.
Improved Employee Experience With quick and accurate responses to the policy-related questions, overall satisfaction amongst the 5,000 employees of the company has increased a lot. Due to this, the HR queries have decreased by 30%, which led to a huge reduction in the work pressure of the HR personnel.
Improved Compliance Providing clear and precise policy information reduced compliance risks and allowed the company’s one million customers to remain compliant with regulations. This led to a 20% reduction in compliance-related incidents.

Outcomes

The implementation of the HR Policy Document Query Assistant marked a paradigm shift in HR policy management for the logistics company. The outcomes were manifold, bringing about positive transformations in various aspects of the organization.

Enhanced Employee Experience
Employees experienced a significant improvement in accessing policy-related information. The Assistant provided quick and accurate responses to queries, enhancing overall satisfaction and reducing frustration.
Time and Cost Savings
HR personnel witnessed valuable time savings, as the Assistant streamlined the process of manual query resolution. This allowed HR teams to redirect their efforts towards more strategic initiatives, contributing to organizational growth.
Improved Compliance
By providing employees with accurate and easily understandable policy information, the Assistant played a pivotal role in ensuring that policies were followed correctly. This, in turn, reduced compliance risks and contributed to a more regulated work environment.
Customization
The flexibility of tailoring the Assistant to the organization’s specific policies and requirements ensured a perfect fit for unique needs. This customization feature added a layer of adaptability, making the Assistant a versatile tool for diverse organizational structures.

Technologies used

Conclusion

In conclusion, the HR Policy Document Query Assistant emerged as a game-changer for HR departments in large corporations. By leveraging the power of Generative AI, the system not only streamlined policy management but also significantly improved employee understanding and automated query resolution. This not only enhanced the employee experience but also liberated HR resources to focus on more strategic endeavors. The customizable and efficient nature of the Assistant marked a significant leap in HR policy management technology, setting new standards for the industry.