Advancing Information Retrieval With The Power Of AI

The Challenge

Information lived across documents, repositories, and systems that were not designed to work together. Much of it was unstructured, difficult to search, and dependent on individual familiarity rather than system intelligence.

This resulted in:

• Time-consuming manual searches and cross-referencing
• Inconsistent answers across teams
• Repeated clarification cycles for complex or follow-up questions

The enterprise needed a retrieval layer that could connect sources, understand context, and deliver answers to teams with confidence.

The Approach

Our work focused on completely modernizing the retrieval system.

The objective was to establish:

• A structured, governed knowledge foundation
• A retrieval mechanism that returns relevant context, not just documents
• A conversational layer capable of maintaining continuity across interactions
• Controls to validate responses and improve quality over time

AI was positioned as an augmentation layer that accelerates knowledge work while preserving oversight and reliability.

What We Did

The new information retrieval stack functioned through a coordinated set of capabilities designed to work as a single system:

• Unified knowledge sources
  Integrated existing knowledge bases and repositories into a consistent retrieval foundation, enabling cross-source discovery.
• Digitized and structured documents
  Converted unstructured and hard-to-query content into formats suitable for indexing, retrieval, and reuse.
• Implemented intelligent conversation management
  Enabled multi-turn interactions so users could ask follow-up questions without restating context.
• Deployed retrieval-augmented generation (RAG)
  Grounded responses in relevant source material to improve accuracy, traceability, and trust for complex queries.
• Applied data intelligence to retrieval quality
  Used analytical signals to strengthen relevance and surface higher-value answers based on content performance and usage patterns.
• Established response quality validation
  Introduced validation mechanisms to detect weak or inconsistent outputs and reduce low-confidence responses.

Each capability was introduced incrementally, allowing the system to evolve without disrupting existing workflows.

Use Case

A user can request a summary of a specific aspect of a contractual relationship with a customer. The chatbot retrieves the information from relevant documents, analyzes it, and fulfills the request. When the user asks for additional details about the same customer, like their installed equipment or financial history, they won’t need to provide further clarifications; the chatbot already understands the context and the aspects being referred to.

AI in Operation

User-AI conversation states are preserved across interactions, enabling users to explore related questions without having to restart queries. When confidence is low or source coverage is insufficient, responses are constrained or flagged rather than generated speculatively.

Quality signals and feedback are captured continuously, allowing retrieval relevance and response reliability to improve over time while maintaining transparency and control.

Impact

The information retrieval system now processes and provides access to over 10,000 legal documents and approximately 3,000 data sheets, enabling faster and more reliable access to critical enterprise knowledge.

Operationally, the system delivered improvements across two key dimensions:

• Efficiency, by eliminating hours of manual research and accelerating decision-making through AI-assisted retrieval
• Autonomy, by reducing dependency on specialized teams for information access and enabling seamless navigation across interconnected data sources

When evaluated against existing AI agents, the custom solution demonstrated higher output accuracy while offering greater flexibility and improved cost efficiency for enterprise-scale use.