Software Engineer at a consultancy with 10,001+ employees
Real User
Top 5
May 4, 2026
Kore.ai could be improved by adding more features, such as call flow automation in addition to chatbots, which would also be helpful. I rated Kore.ai an 8 out of 10 because additional features could be added to it. The platform is excellent, and if more features can be added in the future, it will be truly great.
Principal Solution Architect In Ai Space at a manufacturing company with 10,001+ employees
Real User
Top 20
May 3, 2026
To improve Kore.ai, I suggest focusing on more agentic automation, such as offering MCP kind of features with an orchestration layer for use cases. This would allow us to implement business logic in the orchestration layer. With the ability to build our own MCP servers and plug-and-play with the MCP client, we could have more scalable options for deploying multiple agents on one platform, enabling them to work simultaneously across different use cases.
Kore.ai can be improved by enhancing their documentation, which is currently a bit disorganized. They should include detailed videos or workshops. There are not many videos or community resources available, so adding more would be beneficial. Integrations with real-time models with Kore.ai would be great. Advanced models like Claude or Anthropic models would be valuable additions. Regarding the rating of 8 instead of 10, the missing comprehensive documentation, tutorial videos, workshops, and community services are factors that reduced the score. Additionally, the unavailability of real-time advanced models from Anthropic or Grok also contributed to deducting one point.
Senior Solutions Consultant at a tech services company with 501-1,000 employees
Real User
Top 5
Apr 14, 2026
There are some technological gaps with Kore.ai when it comes to language detection because this problem is common among all conversational AI vendors. They are using external sources for automatic speech recognition and generating text-to-speech services. The speech recognition mechanism remains primary for these vendors, including Kore.ai. We have also observed some limitations in scalability, particularly on Azure, where we have had to scale it on different cloud platforms around the globe. Implementing Kore.ai on Azure microservices might be a challenge compared to what we have seen in AWS, where cloud services are easier to maintain. From the perspective of post-implementation support, Kore.ai can improve significantly because I have seen it lagging in their industry vertical. Other vendors are quite effective at providing post-sales support, and that is an area where Kore.ai can gain market traction through improvements.
Kore.ai provides advanced tools like Agent Desktop and Agent Co-pilot, designed to handle vast data volumes and improve efficiency. The platform integrates seamlessly with APIs and channels, supports multi-language capabilities, and offers real-time testing.Kore.ai offers robust solutions for sectors like healthcare and aviation, enabling efficient ML model configuration and generative AI chatbot development. While there's room for improvement in language detection and scalability,...
Kore.ai could be improved by adding more features, such as call flow automation in addition to chatbots, which would also be helpful. I rated Kore.ai an 8 out of 10 because additional features could be added to it. The platform is excellent, and if more features can be added in the future, it will be truly great.
To improve Kore.ai, I suggest focusing on more agentic automation, such as offering MCP kind of features with an orchestration layer for use cases. This would allow us to implement business logic in the orchestration layer. With the ability to build our own MCP servers and plug-and-play with the MCP client, we could have more scalable options for deploying multiple agents on one platform, enabling them to work simultaneously across different use cases.
Kore.ai can be improved by enhancing their documentation, which is currently a bit disorganized. They should include detailed videos or workshops. There are not many videos or community resources available, so adding more would be beneficial. Integrations with real-time models with Kore.ai would be great. Advanced models like Claude or Anthropic models would be valuable additions. Regarding the rating of 8 instead of 10, the missing comprehensive documentation, tutorial videos, workshops, and community services are factors that reduced the score. Additionally, the unavailability of real-time advanced models from Anthropic or Grok also contributed to deducting one point.
There are some technological gaps with Kore.ai when it comes to language detection because this problem is common among all conversational AI vendors. They are using external sources for automatic speech recognition and generating text-to-speech services. The speech recognition mechanism remains primary for these vendors, including Kore.ai. We have also observed some limitations in scalability, particularly on Azure, where we have had to scale it on different cloud platforms around the globe. Implementing Kore.ai on Azure microservices might be a challenge compared to what we have seen in AWS, where cloud services are easier to maintain. From the perspective of post-implementation support, Kore.ai can improve significantly because I have seen it lagging in their industry vertical. Other vendors are quite effective at providing post-sales support, and that is an area where Kore.ai can gain market traction through improvements.