Voice AI
What is Entity extraction?
Entity extraction is pulling specific pieces of information, such as names, dates, amounts, locations and product names, out of unstructured speech or text into structured fields.
Also called: named entity recognition, NER, information extraction
On a property enquiry call the entities might be '2BHK', 'budget 80 lakh', 'Gachibowli' and 'Saturday 11 baje'. Indian calls add particular challenges: amounts expressed in lakh and crore, dates and times spoken in a mix of languages, and locality names that speech recognition may mangle. Good extraction also normalises what it finds, so '80 lakh' becomes a number a spreadsheet can sort.
Structured fields are what make a call actionable, because they can be filtered, routed and scored. Outpero lets a business define custom fields for each AI employee, such as budget, preferred locality or visit date, fills them from each conversation, and delivers them to the dashboard, CSV export and webhook so they land in the CRM without retyping.
What is Entity extraction?
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Entity extraction is pulling specific pieces of information, such as names, dates, amounts, locations and product names, out of unstructured speech or text into structured fields. On a property enquiry call the entities might be '2BHK', 'budget 80 lakh', 'Gachibowli' and 'Saturday 11 baje'. Indian calls add particular challenges: amounts expressed in lakh and crore, dates and times spoken in a mix of languages, and locality names that speech recognition may mangle. Good extraction also normalises what it finds, so '80 lakh' becomes a number a spreadsheet can sort.
Why does Entity extraction matter?
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Structured fields are what make a call actionable, because they can be filtered, routed and scored. Outpero lets a business define custom fields for each AI employee, such as budget, preferred locality or visit date, fills them from each conversation, and delivers them to the dashboard, CSV export and webhook so they land in the CRM without retyping.
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