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Companies generate and store large volumes of text and voice in emails, calls, and documents. AI makes it possible to transcribe, classify, and analyze that content automatically, revealing valuable insights about problems and opportunities.

AI Solution Type: AI Agent that does not include a chatbot (it is possible to integrate a conversational interface or AI chatbot, if required)

Traditional Process: When companies seek insights in emails, calls, or documents, they depend on manual audits or incomplete sampling. This method is slow and easily misses relevant details or systematic patterns.

Application of NLP and Speech-to-Text:

1. Call transcription: Using Speech-to-Text, audios are converted into text for subsequent massive analysis.
2. Sentiment analysis: NLP tools identify emotions or intentions in messages.
3. Automatic classification: Key themes (complaints, requests, frequent questions) are tagged based on content.
4. Keyword detection: Critical or sensitive terms are located, facilitating prioritization.
5. Trend visualization: Aggregate reports show peaks of certain themes or changes in the tone of interactions.

Benefits:

• Total coverage: Large volumes of interactions are analyzed without relying only on samples.
• Early problem identification: By detecting atypical words, emotions, or trends, proactive actions can be taken.
• Resource savings: Automated analysis reduces the manual labor of reading and listening.
• Continuous improvement: Findings allow for systematic team training and process improvement.

Conclusion: Analyzing text and voice with AI democratizes access to valuable information. By gathering large volumes of conversations and documents, companies discover trends and areas for improvement that would otherwise go unnoticed.