About the Client
The client manages health-related educational content delivered through Learning Management Systems (LMS). Learning materials are distributed across multiple formats, including PDF documents, recorded video sessions, and SCORM packages.
The Challenge: The information existed. Finding it was the problem
Users often knew what information they needed but were unable to locate it efficiently within the LMS.
Accessing precise information was time‑consuming and inefficient, particularly for complex medical and educational topics distributed across multiple learning assets.
Key challenges:
Dependence on manual navigation and keyword-based queries.
Lack of contextual understanding across content formats.
No reliable source attribution for retrieved information.
The Solution: AI-Powered Q&A over LMS content
Polcode delivered a Proof of Concept to validate whether an AI-Powered, natural language question-answering approach could replace traditional browsing in a healthcare LMS.
The PoC focused on technical feasibility and answer quality, including ingestion of PDFs, SCORM packages, and video materials, as well as semantic retrieval, contextual accuracy, and reliable source attribution.
Key solution components:
Natural language querying over LMS content.
Retrieval-augmented generation (RAG) approach.
Semantic search across PDFs, presentations, images, videos, and SCORM packages.
LLM-based answer generation grounded in indexed content.
Explicit source attribution for all responses.
The Results: Clear answers, grounded in source materials
The Proof of Concept confirmed that AI-Powered search can reduce the need for manual LMS navigation when accessing complex, health-related educational content.
Confirmed capabilities:
High-quality text extraction and indexing from PDF documents.
Successful indexing and semantic search across SCORM packages.
Effective retrieval across multiple content formats within a single search experience.
Clear and accurate answers aligned with source materials.
Correct handling of unanswerable questions without fabricated responses.
Technical insights from the Proof of Concept
SCORM packages can be effectively indexed and queried as part of a unified LMS search experience.
Video transcription quality varies depending on the source material and recording conditions.
What was actually built
The PoC helped identify technical considerations for the next stage while confirming that the solution is feasible.
Deliverables
Suggested next steps toward a production-ready solution
Based on the PoC findings, next steps may include:
Defining integration approaches with existing LMS platforms.
Designing a dedicated ingestion management layer for content updates and versioning.
Implementing query logging and analytics to improve answer quality and user experience.
Establishing governance rules for content updates and model grounding.
When moving toward production, ingestion workflows should account for the internal structure and consistency of SCORM packages to ensure stable indexing and retrieval quality at scale.
From PoC to production-ready AI search
Discover
Explore how AI-powered natural language search works in a health-related LMS.
Act
Talk to our experts about applying this approach to your LMS or knowledge platform.
Let’s Get Started
Tell us more about your project, needs and timeline. Our sales and developer teams will be more than happy to answer your questions.