OpenText Enterprise Document Q&A
Suggested workflow: Use natural language to search and summarize enterprise documents. Task: Suggested implementation: Deploy natural language search and summarization for enterprise documents using Google Cloud AI tools. The workflow involves ingesting internal documents, configuring an AI agent to process queries, and generating summaries. Human roles include defining document scopes, reviewing generated answers for accuracy, and managing access permissions.; tools: Google Cloud AI. Human role: Source evidence unavailable; actual deployment requires defining human review and operational roles based on specific tasks.; limitations: Prerequisites include secure document storage and clear data governance policies. Integration uncertainty exists regarding compatibility with legacy file formats. Operating costs depend on query volume and model usage. Human approval is required before publishing any AI-generated summaries to ensure compliance and factual correctness..
Tools
Published on Agent Work Atlas:
Overview
Suggested workflow: Use natural language to search and summarize enterprise documents.
Workflow
Suggested implementation: Deploy natural language search and summarization for enterprise documents using Google Cloud AI tools. The workflow involves ingesting internal documents, configuring an AI agent to process queries, and generating summaries. Human roles include defining document scopes, reviewing generated answers for accuracy, and managing access permissions.
Reported result
Actual outcomes are unknown as the source provides no specific metrics for this exact use case. Proposed acceptance check: Measure the reduction in time spent locating information and the percentage of accurate answers in a controlled pilot. Verify that the system correctly cites source documents for each response.
Human role
Source evidence unavailable; actual deployment requires defining human review and operational roles based on specific tasks.
Limitations
Prerequisites include secure document storage and clear data governance policies. Integration uncertainty exists regarding compatibility with legacy file formats. Operating costs depend on query volume and model usage. Human approval is required before publishing any AI-generated summaries to ensure compliance and factual correctness.
Try it
I want to pilot a natural language search system for our internal HR policy documents using Google Cloud Vertex AI. First, upload a sample set of 10 PDF policy documents to a secure Cloud Storage bucket. Next, configure a Vertex AI Search engine to index these documents. Then, run a test query: 'What is the procedure for requesting remote work?' and generate a summary. Verify that the answer is accurate and cites the correct document. If the test passes, expand to 50 documents. Do not publish results until I review the accuracy report. If documents are missing or access is denied, stop and ask for clarification.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.