Global Technology Leader saves 60+% through hyper-automation with LLM based feedback architecture
Objectives
- To transform customer experience using LLM-based hyper-automation solutions
- The current conversational AI solution deployed is trained on limited intents and phrases and is unable to map 50% of the conversations
- A lot of manual effort goes into extracting missed utterances from the bot and mapping them with the right intent to re-train the bot
Customer Environment
- Distributed / unstructured data environment
- Data accuracy and performance is of high importance
- Integrated customer internal enterprise applications
- 1000+ Complex conversational multilingual bots
Global Technology Leader saves 60+% through hyper-automation with LLM based feedback architecture
Objectives
- To transform customer experience using LLM-based hyper-automation solutions
- The current conversational AI solution deployed is trained on limited intents and phrases and is unable to map 50% of the conversations
- A lot of manual effort goes into extracting missed utterances from the bot and mapping them with the right intent to re-train the bot
Customer Environment
- Distributed / unstructured data environment
- Data accuracy and performance is of high importance
- Integrated customer internal enterprise applications
- 1000+ Complex conversational multilingual bots
Outcome
~90% Automation achieved for conversations
Improve End customer experience enabling contextual conversation with 94% confidence score
Unstructured data processing to build the classification model
Integration with Enterprise Application to automation feedback loop
Agile Methodology for acceleration of development
Proprietary Technology for quick adoption of customer environment to deliver in secured environment
