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AI-Driven Language Tuning for Knowledge Management

The client, a leading electric vehicle manufacturer with an annual revenue of $650M. The company sought to improve internal knowledge-sharing processes to keep up with its rapid growth and complex operations.

Challenges

Knowledge was siloed across departments, making it difficult for employees to access information quickly.

  • Existing search capabilities yielded generic results, often requiring manual filtering.
  • Variations in terminologies and documentation styles created communication barriers across teams.
  • Employees spent excessive time locating critical information, impacting productivity.

Implementation

  • Delivered a robust Enterprise Knowledge Management solution with advanced AI-driven language tuning, leveraging AWS services such as SageMaker, OpenSearch, S3, Glue, Lambda.
  • Fine-tuned natural language processing (NLP) models tailored to the client’s domain-specific terminology and technical jargon.
  • Implemented semantic search capabilities to deliver context-aware and accurate results.
  • Integrated knowledge-sharing workflows with existing collaboration tools such as Slack and Microsoft Teams.
  • AWS CloudWatch tracked user interactions and system performance to enable continuous improvement.

Results

  • Reduced time spent searching for information by 50%, significantly enhancing employee efficiency.
  • Improved search accuracy, with 90% of queries yielding relevant results on the first attempt.
  • Achieved 20% reduction in support costs by minimizing dependency on internal help desks.
  • Increased employee engagement and satisfaction scores by 25%, reflecting the ease of accessing information.

Tech Stack

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