Business Problem Statement

Large organizations maintain extensive collections of internal documents, HR policies, financial guidelines, departmental procedures, compliance rules, etc. Employees often struggle to find accurate and up-to-date information within these documents due to their volume, complexity, and scattered storage.

This leads to:

• High dependency on HR or support teams for simple policy clarifications
• Delays in decision-making because employees cannot quickly locate relevant rules
• Low policy awareness, increasing the risk of non-compliance
• Inefficiency and productivity loss as employees spend time searching or asking multiple people for answers

There is no centralized, intelligent system that allows employees to upload, search, and query organizational policies in natural language. The business needs a scalable AI solution that can ingest large documents, index them, and provide accurate, context-based answers to employee questions, reducing dependency on manual processes and improving accessibility of organizational knowledge.

Key Features (KlarDataLabs Solution Overview)

The solution allows users to register, log in, and upload policy documents, specifically PDF and text files through a simple ingest interface where the system reads and processes the documents in batches and converts them into embeddings for storage in a vector database.

Once documents are ingested, users can ask questions through an AI-driven chat interface that provides answers based on the uploaded content, including features such as summarizing long policy sections, explaining specific clauses, and citing the exact source and page number from the document.

The interface also supports clearing chat history, and there is a planned feature for department-based access control, which is not yet implemented.

Overall, the solution streamlines understanding of complex or lengthy policy documents by enabling employees to query them directly and receive accurate, context-based responses linked to the relevant sections.

Technology Behind the Solution

The solution is built on an AI-based Retrieval-Augmented Generation (RAG) pipeline, which processes and understands large policy documents to deliver accurate, context-aware answers.

In the ingestion phase, uploaded PDF or text documents are read in batches, split into meaningful chunks, and converted into embeddings using sentence transformer models. These embeddings are then stored in a secure vector database, which supports upsert operations to ensure new content is added and existing content is updated efficiently.

When a user asks a question, the system parses and filters the input, converts the query into embeddings, and performs a similarity search within the vector database to retrieve the most relevant document sections before generating a final answer with precise source and page references.

Access to the system is controlled through role-based authentication, as users must register and log in before interacting with the application. The transcript also mentions a planned feature for department-based access control, reinforcing the system’s structured approach to user permissions.

The underlying design supports enterprise-grade data privacy, as all data processing document ingestion, vector storage, and question handling happens in controlled workflows that avoid exposing sensitive content.

While deployment details were not discussed in the transcript, your bullet point indicates that the solution can be made available through on-premises or cloud-based deployments, making it adaptable to organizations with different security, compliance, and infrastructure requirements.

Technical Components