Contiinex Speech to Text can deliver high-quality speech automation and help you optimize your cost by almost 80% while securing your data 100%.
It comes with a built-in industry-specific attribute library for Insurance & Healthcare enabling faster deployment and accurate contextualization.
It is tailored to deliver the use case such as automating the quality audit process or finding anomalies in customer verification process or identifying points of leakages in Claims process or identifying customers at retention risk. The derivatives from the speech analytics platform are in easily customizable in the formats that fit your company’s needs to provide high quality data-driven insights.
The NLP built to interpret and contextualize the transcription, it can be customized for identifying keywords during conversations, to automate your complete quality audit process.
Our STT accuracy is 90% for untrained data & can scale to 97% with platform training & calibration
Get deeper insights of what your customers are saying and get compliant on all regulatory aspects
Contiinex can be plugged into your cloud environment to help comply with all data privacy
Contiinex comes pre-integrated with all leading voice diallers for easy streaming of your voice data and can be deployed as a plug & play into your cloud instance.
It also comes integrated with all leading CRMs and can ensure high accuracy of information.
Convert all or any of your conversations from speech to text with Contiinex. It has capabilities to comprehend a variety of languages & dialects with an output accuracy of 90% & above and further improves it over time with learning.
Contiinex Speech to Text can be deployed in-house on your Customer’s cloud instance behind the firewall and ensures 100% of your data is secure.
Contiinex speech API uses a speech pre-processing and speaker diarization for determining & labelling the transcription output with speaker level identification.
Contiinex speech analytics uses established methods to take unstructured data from the STT engine and make it readable for the NLP engine. The data is then analyzed and simplified to draw business or process trends and patterns.
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