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Empowers HR teams to deliver fast, accurate, and personalized employee support through conversational AI, automated knowledge management, and seamless HR system integration.
Internal Knowledge Optimizer
Transforms video transcripts into an interactive knowledge base for fast, contextual answers via natural language.
#ReduceTimeToInsight #EnterpriseKnowledgeBase #Education
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Business Challenge
Organizations have vast amounts of unstructured content — including webinars, meetings, training videos, podcasts, and various document formats — that often remain underutilized. This content represents a valuable corporate knowledge base, but it is typically confined to rigid, top-down access methods and rarely leveraged to its full potential. Extracting meaningful insights from it is time-consuming and inefficient, especially when scattered across disconnected platforms.
There’s a growing need to transform this dispersed information into a structured, dynamic knowledge base that teams can easily access, search, and interact with to support learning, collaboration and the discovery of relevant expertise and insights.
Solution Overview
Our Internal Knowledge Optimizer uses generative AI to convert large volumes of unstructured multimedia content into an intelligent, queryable knowledge base.
Through a combination of fine-tuned language models and semantic search, the application enables users to interact with internal knowledge via natural language — extracting key concepts, summaries, timelines, references and more from complex content.
Natural language queries are interpreted and routed to the best retrieval path — structured, semantic or a combination of both — depending on the user’s intent. By blending these two approaches, the system delivers more comprehensive and accurate results. The result is an AI agent capable of responding to a wide range of requests by leveraging the full context of the organization’s audio, video, and textual assets, enabling direct access to knowledge and promoting continuous learning and reuse of internal content across the organization.
Technical Implementation
The application relies on a multi-step pipeline that:
Processes raw content
converts videos, audio files and documents into text using transcription and advanced vision models, enriching outputs with metadata (timestamps, speaker identification, source type).Extracts structured knowledge
applies information extraction techniques to identify key entities, topics and relationships, transforming the unstructured content into a coherent knowledge base.Fine-tunes a retrieval model
a dedicated language model is fine-tuned on the structured knowledge base to optimize understanding of natural language queries and ensure accurate extraction of relevant insights.Integrates semantic search
for queries that require conceptual understanding, the system uses vector embeddings (e.g., OpenAI's text-embedding models) to retrieve semantically similar content, even in the absence of direct keyword matches.Combines results intelligently
the system dynamically selects the most effective strategy — blending structured lookups and semantic matches — and ranks responses based on relevance, recency and user context.
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