My main use case for Kore.ai was creating an outbound calling generative AI-powered chatbot, which is useful for insurance and healthcare companies.
AI For Process
Kore.aiExternal reviews
External reviews are not included in the AWS star rating for the product.
Automation has reduced large call centers and provides real-time outbound support for patients
What is our primary use case?
What is most valuable?
Kore.ai is a low-code platform, and you do not necessarily have to be an expert in artificial intelligence or generative AI to use this platform. However, if you have that experience, it will be a valuable add-on. The main advantage is that it has almost every kind of plugin available to use, whether for live agents or if you want to use it as a call center. You have real-time capabilities and the most useful plugins available, with almost every kind of model available with configurations. We can configure the ML models and train them. The platform is very user-friendly with explanatory features and a great UI.
One significant feature is the live testing platform with Kore.ai. When you are creating a workflow, you have an interactive, real-time testing platform that reflects every change you make. This makes it easy to debug errors or add new features. Additionally, Kore.ai has many different tools and platforms that cater to different needs, whether for a call center, normal workflow, or machine learning workflow, which is really useful.
Because of Kore.ai, it was much easier than creating something manually. A low-code platform always helps, and when that platform has this much capability, it provides far more value than manual development. It was particularly helpful for our team that we could create a POC and get that project into production. Kore.ai helped our team build a generative AI outbound calling chatbot, and it truly helped our customers.
If you have a hospital with a thousand employees in a call center, you might consider outsourcing. However, with Kore.ai's outbound calling capabilities, you can eliminate the outsourcing part. You do not need a thousand call center employees. You can accomplish this through automation with just one or two employees if people need to switch to agents. This represented a significant cost reduction.
What needs improvement?
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.
For how long have I used the solution?
I have been using Kore.ai for nearly one and a half years.
What do I think about the scalability of the solution?
Kore.ai's scalability is pretty much scalable both vertically and horizontally.
How are customer service and support?
The customer support is top-notch, and they respond promptly. I have been in direct contact with the team, and they typically reply with respect.
Which solution did I use previously and why did I switch?
We used different solutions, including manual calling.
What about the implementation team?
For my project or workflow, I collaborated directly with the Kore.ai team. It was an intra-team project, and it was really useful that their UI was very well-designed so that you do not necessarily have to go to the documentation to find anything. You can look at the name of the icons and understand what they do. For my workflow, I had to connect this with a live call center system. There were plugins that helped me dial and call real customers. The plugins available were really good and useful.
What was our ROI?
As I mentioned, if you have a call center for 1,000 people, you can reduce that to two.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing is comparatively lower than other companies and tools available.
Which other solutions did I evaluate?
We evaluated other options like Emma and Elsa, as well as other platforms, Python-based automations, and n8n-based automations.
What other advice do I have?
Really refine your use case, gather your requirements, and talk with the team to confirm if that is something possible before proceeding. I rated this solution an 8 out of 10.
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.