Excellent for Building Voice Bots, Web Bots, and Agents with Internal or External LLMs
What do you like best about the product?
Creating Voice Bots , Web Bots and Agents using external and Internal LLMS
What do you dislike about the product?
High Cost and Hidden Fee and Platform complexicity
What problems is the product solving and how is that benefiting you?
While writing large script in script node we cant find contrl F in the Window.
Voice bots have transformed customer journeys but post-implementation support still needs work
What is our primary use case?
My main use cases for Kore.ai include a diversity spanning from banking and the BFSI sector, to the travel market including aviation, and applications on the automobile side. Kore.ai has created sub-products for different industry verticals, which provides good use cases in terms of banking.
A specific example of a use case in banking is where a client needs to perform real-time transactions from one account to another. I can call using Kore.ai, and as a consumer, I can transact an amount of dollars from one account to send to any beneficiary that is already added into my account. On the aviation side, we have done use cases with Riyadh Air, which is a new airline in the Middle East focused entirely on guest experience. Customers can call Riyadh Air help assistance to book a ticket, schedule a trip, or select seats at certain airports.
I want to add the use of AI technology and the ASR and TTS services that we use as part of my main use cases. The performance of the bot becomes more dependent on what kind of external services or external LLM sources are being used. We are currently using Microsoft ASR and TTS services in most of the bots that we have deployed with Kore.ai, and Kore.ai has their inherent native Microsoft speech services enabled as well. Therefore, Kore.ai is more efficient when it comes to Microsoft ASR and TTS speech services. They have their own LLM, but based on our experience, we have used Cloud Anthropic most often and have also used OpenAI, which works very well with Kore.ai.
What is most valuable?
My favorite feature that Kore.ai offers is their Agent Desktop. If you are integrating Kore.ai with a contact center solution as an integrated solution, Kore.ai also provides a standalone solution. You can perform both types of deployment, and their Agent Desktop, Agent Assist, and Agent Co-pilot features are very exciting in terms of how they can pull in knowledge. They have their own knowledge libraries that can facilitate your agents when calls are routed from an AI agent to a human agent.
The Agent Desktop and Agent Co-pilot become especially useful for my team when you have a large volume of knowledge to traverse through as a human agent. With Agent Co-pilot and Agent Assist inside the platform, the information from the knowledge base becomes easy for you to access. Based on customer intents during calls or chats, Kore.ai Agent Assist can detect the intents quite efficiently and bring out the best knowledge articles from the knowledge libraries to present to you as an agent. The system does most of the work that an agent has to do in finding knowledge and searching for it in real-time. We have improved the average handle time significantly with the use of Agent Co-pilot.
Another exciting feature is the industry vertical-based bots that have already been tried and tested by Kore.ai. I don't believe any other vendor offers this with specific bots for the healthcare industry, aviation, BFSI, automobile, and insurance. They have predefined use cases already plugged in, so you don't have to start from scratch. Predefined templates inside the libraries can be reused and built upon for your bots.
Kore.ai has positively impacted our organization by helping us roll out the platform in one of our Middle Eastern markets first, where Arabic language was a challenge. We addressed those challenges through our own local native Arabic speaking personnel and then moved to the European market, where there is significant language diversity. The more exposure Kore.ai received with us, the same kind of efficiency we achieved when switching from one language to another. We have built a team of 30 plus agents who are conversation designers, AI engineers, AI implementation engineers, and Kore.ai experts on the platform. Organizationally, we have progressed considerably with Kore.ai.
The positive impacts we have seen include expected reductions in average handle time, which is typically around 40 to 50 percent for any BFSI industry use case. In automobile and aviation, the AHT reduction comes at a cost because the calls are longer. We track parameters such as AHT, customer experience, and CSAT. For example, how the bot engages with the customer, carefully takes the intents from the client, and then responds back to them reflects these metrics. We see KPIs related to average handle time and agent reduction playing a significant role as we are the biggest BPO provider.
What needs improvement?
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.
For how long have I used the solution?
I have been using Kore.ai for more than five years.
What do I think about the stability of the solution?
Kore.ai is definitely stable. The on-premises version is the most stable, followed by the hybrid cloud model, and the public cloud setup is stable as well.
What do I think about the scalability of the solution?
Kore.ai's scalability has limitations, particularly on Azure cloud, which is not cloud dependent. It is mostly cloud agnostic, and while it scales very well with AWS, there are certain microservices on Azure that need elasticity, indicating gaps from the cloud provider, not from Kore.ai technology itself.
How are customer service and support?
Customer support is where Kore.ai has significant room for improvement. Post-implementation support is a particularly discouraging aspect for me.
Which solution did I use previously and why did I switch?
Previously, we have partnered with Cognigy and have our own in-house solutions, including Unify apps. While other vendors have their positives and negatives, we prefer Kore.ai due to our strategic partnership with them, making it our go-to solution in the market. We worked with Cognigy previously, which is now acquired by Nice, and we specifically compared Kore.ai with Cognigy.
What was our ROI?
Definitely, we consider the digital transformation journeys for customers, taking into account that investment costs are typically higher in the first two years for implementing technology, identifying use cases, and mapping them. Once up and running, the benefits of AI come into play. The results we see are agent reductions of 15 to 20 percent in multiple cases, lower telephonic costs due to SIP provisioning, and improved customer experiences with voice bots, chatbots, and reduced call times.
What's my experience with pricing, setup cost, and licensing?
Licensing is worked out on a case-by-case basis with their account management teams based on volumes. Their expert app services, which provide professional support during implementation, are higher in price. We have an in-house team capable of implementing Kore.ai, but post-implementation support, as I reiterated earlier, needs improvement both in terms of cost and delivery.
What other advice do I have?
Teleperformance is an exceptional reseller from Kore's side, and we have a great partnership with them. We are direct vendors and resellers of Kore.ai as a direct vendor. For our public cloud deployments, we use AWS most often. We deploy Kore.ai using multiple configurations, mostly public cloud in AWS Frankfurt, Microsoft Azure in UAE, and we also have one on-premises deployment for one of the leading banks in the Middle East, so it is a blend of all. 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. Another exciting feature is the industry vertical-based bots that have already been tried and tested by Kore.ai. I don't believe any other vendor offers this with specific bots for the healthcare industry, aviation, BFSI, automobile, and insurance. They have predefined use cases already plugged in, so you don't have to start from scratch. Predefined templates inside the libraries can be reused and built upon for your bots. I rate this review overall as a seven.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Thee most developer friendly platform ever for IVA development
What do you like best about the product?
Everything from design to build to deployment to testing,all are integrated in a single platform which reduces the hurdles faced by we developers. Low code platform with place to analyse bot performance are really helpful in real time monitoring.
What do you dislike about the product?
Adding templates and images for enumeration list entity needs some additional work which could be taken care of. More features for IVR will be cherry on cake if available.Try to add an inbuilt non relational database also along with existing database.
What problems is the product solving and how is that benefiting you?
NLP engine is really good even after few shortcomings and some real time customer issues are addressed and solved within very less time because of developer friendly environment.
All-in-One Conversational AI Platform with Flexible Bot Customization
What do you like best about the product?
It offers a complete AI-based conversational AI platform where we can design bots, train NLU, integrate with backend systems, and monitor performance all in one place. This gives developers the flexibility to customize as needed.
What do you dislike about the product?
Nothing as of now—I don’t dislike anything about Kore.AI at this time.
What problems is the product solving and how is that benefiting you?
It solves the challenge of scaling human-like conversations across enterprise systems by automating customer and employee interactions through intelligent virtual assistants. It helps me by reducing the manual effort required to handle repetitive queries, enabling faster integration with backend systems, and allowing me to build and customize conversational AI chatbots efficiently.
Easy-to-Use Interface with Great Documentation and Lots of Possibilities
What do you like best about the product?
Many possibilities, an easy-to-use interface, and good documentation.
What do you dislike about the product?
Occasionally, the system becomes unresponsive.
What problems is the product solving and how is that benefiting you?
Bots creating and voice bots. In koreAI it is easier
Smart No-Code AI That Deflects Tickets and Delivers Clear ROI
What do you like best about the product?
It offers no-code, drag-and-drop features, along with extensive support for deploying the app across various channels. AI is smart enough to understand messy, natural language rather than just acting like a basic FAQ bot. The ROI is clear: it deflects a ton of manual support tickets, making the investment well worth it for any team looking to scale. The training support was amazing.
What do you dislike about the product?
Even though it’s "no-code," the interface is so packed with advanced features that it feels cluttered and overwhelming for a beginner. It takes a lot of time to truly master how to build complex flows without hitting a wall. I’ve also run into some performance lag when the bot is trying to pull data from multiple integrations at once, it can cause a noticeable delay in the chat, which isn't ideal for a smooth user experience.
What problems is the product solving and how is that benefiting you?
The biggest benefit has been the "self-service" aspect, customers get instant answers to things like order tracking, order details without waiting in a queue.
Low Cost and Easy to Love
What do you like best about the product?
It’s a low-code automation tool, so we can build automations with minimal coding knowledge. The tool is also easy to use and straightforward to work with.
What do you dislike about the product?
I’m not sure whether this can be used for UI automation, like Selenium, Playwright, etc.
What problems is the product solving and how is that benefiting you?
Automated ticketing tool.
