Kore.ai Reviews

My main use case for Kore.ai was creating an outbound calling generative AI-powered chatbot, which is useful for insurance and healthcare companies.

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.

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.

I have been using Kore.ai for nearly one and a half years.

Kore.ai's scalability is pretty much scalable both vertically and horizontally.

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.

We used different solutions, including manual calling.

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.

As I mentioned, if you have a call center for 1,000 people, you can reduce that to two.

My experience with pricing, setup cost, and licensing is comparatively lower than other companies and tools available.

We evaluated other options like Emma and Elsa, as well as other platforms, Python-based automations, and n8n-based automations.

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.

I develop chatbots using Kore.ai.

I developed a travel assistant using Kore.ai that books tickets, cancels tickets, or modifies journeys.

I also integrate API data in Kore.ai so that it can fetch data from the API and display it directly to users.

The best features Kore.ai offers in my experience are that the user interface is very easy to use, the performance is excellent, and everything runs very smoothly.

When I say the interface of Kore.ai is easy to use, I mean the tools and everything are very useful and intuitive. When I mention performance, I mean the platform does not lag at all, and its response time is also good.

Kore.ai has impacted my organization positively because most companies are switching their IVR systems to chatbot systems, making Kore.ai a truly good platform for chatbot development.

The best feature of Kore.ai is that it is a cost-saving tool. For example, when we use IVR, we have to assign agents at the backend, but using Kore.ai, we can automate all of those functions.

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.

I have been using Kore.ai for one and a half years.

Kore.ai is stable.

Kore.ai's scalability is good.

Kore.ai's customer support is good.

I can rate the customer support of Kore.ai a 9 out of 10.

Previously, most companies were using the IVR platform, but nowadays, they are switching to chatbot systems, which is why I switched.

Before choosing Kore.ai, I did not use any chatbot development system.

As of now, Kore.ai is working very well for the vendors and has also saved their time and money.

The experience with pricing, setup cost, and licensing of Kore.ai is that the price is low, and the licensing is also not costly.

I advise others to use Kore.ai because it is a beginner-friendly platform. I rated this product an 8 out of 10.

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.

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.

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.

I have been using Kore.ai for more than five years.

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.

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.

Customer support is where Kore.ai has significant room for improvement. Post-implementation support is a particularly discouraging aspect for me.

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.

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.

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.

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.

My main use case for Kore.ai is creating a robot which handles healthcare data, allowing new patients to register with a doctor or hospital and book appointments.

This bot helps patients by asking basic details such as name and illness, then accordingly queries them on when they want to book an appointment and confirms the booking if available. Additionally, I have worked on a banking use case where users can create various account types and retrieve account information.

In the healthcare bot, when a patient wants to book an appointment, they are first asked for their phone number. If the number exists in records, the details are fetched and displayed. The user then agrees to the information and is prompted with other questions regarding symptoms, duration of the issue, and appointment preferences. After providing this information, they can book the appointment and receive email confirmation. They also have the option to fetch or change appointment details afterward.

Kore.ai can be utilized in two ways: through natural language understanding or using DialogGPT.

The use of NLU is basic and does not involve LLM intelligence, while utilizing DialogGPT brings in the LLM intelligence, allowing for intent rephrasing such that when users ask questions, those are rephrased for better answers.

One of the best features Kore.ai offers is its well-conducted training program, which includes beginner and advanced sessions totaling around eight hours. After training, practical assignments based on session teachings are provided and must be completed for certification, which requires a thorough understanding of Kore.ai. I have earned both beginner and advanced certifications. Furthermore, I appreciate the DialogGPT feature, which maintains the context of user queries while providing rephrased answers, as well as features such as charts, graphs, and APIs that allow communication with external data sources, enhancing usability and ease of development compared to other tools I have used.

Kore.ai has positively impacted my organization by helping us build intelligent chatbots and incorporating voice agents, enabling various clients to adopt these solutions, which have been revolutionary for their businesses.

I would suggest incorporating pre-recorded videos in the tutorials and enhancing the documentation area to make it more user-friendly.

I see room for enhancement mainly in improving existing features and increasing marketing efforts to highlight Kore.ai's capabilities, as well as suggesting additional use cases that can be beneficial for businesses.

The user experience has been excellent, with well-structured courses and supportive mentorship provided by Kore.ai, but I would like to see more pre-recorded sessions and increased certification opportunities in various areas.

I have been using Kore.ai for the past one year.

I find Kore.ai to be quite stable, and while my organization evaluated other options, they ultimately found those tools less user-friendly compared to Kore.ai, which had easier functionality and training.

Kore.ai's scalability is quite good, as it offers plugins and allows for additional features based on usage, leading to variable charges that align with needs.

Customer support has been very responsive, providing assistance promptly during development and learning phases, making their help invaluable when challenges arise.

My organization did not use any other solution before adopting Kore.ai, making it our first choice despite having options for other tools.

I have seen a positive return on investment in terms of time saved and fewer employees required, as the bots enable 24/7 support without continually having to deploy human agents.

We have experienced significant results such as saving time and money by reducing the number of necessary human agents, as the intelligent bots created with Kore.ai operate 24/7 and allow users to bypass the limits of human availability.

I advise others to definitely give Kore.ai a chance, as it is user-friendly compared to other conversational AI tools that may appear complex and difficult to implement.

I think Kore.ai is a great tool with impressive learnings, especially in the agentic AI domain, and unlike traditional chatbots, it leverages intelligence to analyze questions and provide relevant answers, including integration capabilities with external tools while ensuring data security with features such as PII reduction. Users should definitely consider trying this product. I would rate this product an eight out of ten.

My main use case for Kore.ai is to design chatbots for customers in the aviation domain.

I have developed one chatbot where we take reviews from customers, specifically customer satisfaction reviews from the airlines domain. Customers use our airlines and we measure the satisfaction rate from our services. We have developed the chatbot to review customer feedback.

I have used Kore.ai in multiple ways. I have developed a chatbot designed for HR, allowing HR to provide all information related to company policies and company information. Employees can ask about leave and company policies. New users can also get information about the company. We have developed an HR chatbot.

Kore.ai offers multiple support and services, including API integration, webhook integration, and multiple channels. These features allow you to integrate and use them very easily, and the documentation provided is excellent so you can reference it.

The most valuable features in my day-to-day work are API integration and channel integration. I have mostly used the web channels.

Kore.ai has impacted my organization positively by providing substantial support related to design and implementation. You can build your own logic and implement it using languages, such as Node. It provides a positive approach where you can design and implement your requirements.

Since using Kore.ai, I have seen multiple improvements. I have used multiple platforms, including LivePerson and LUIS, and Kore.ai provides considerable support for implementation. You can design your own layout very easily, integrate that, and easily integrate with multiple channels and webhooks. It is easy to deploy, and it can track any errors that occur, making it a good way to start with and easy to learn.

I have already seen Kore.ai implement the LLM, and I think there could be improvements with a few cloning properties. For example, if we can integrate with multiple channels, I suggest the ability to use cloning properties in scenarios involving a customer satisfaction chatbot where, based on the requirement and multilingual abilities, we can use human-behavior characteristics where a human can pause and talk.

I choose 7.5 out of 10 for Kore.ai because a few things already need to be improved, one of which I have mentioned. Kore.ai is continually improving their product day by day, but a few things are still missing, particularly related to performance speed.

I have been working in my current field for almost four or more years.

I do not find anything challenging with Kore.ai, as I am mostly using the straightforward approach to implement my own requirements and address problems. Therefore, I do not require any add-on features.

In terms of integration with existing systems or third-party tools, I have faced challenges a few times, but mostly I have used straightforward methods to integrate channels easily. My role is basically to integrate multiple channels very easily, as Kore.ai provides most channels in their own marketplace and categories.

I have a direct relationship with Kore.ai team and we have a direct connection with them, not through any marketplace.

Since using Kore.ai, I have seen multiple improvements. I have used multiple platforms, including LivePerson and LUIS, and Kore.ai provides considerable support for implementation. You can design your own layout very easily, integrate that, and easily integrate with multiple channels and webhooks. It is easy to deploy, and it can track any errors that occur, making it a good way to start with and easy to learn.

New users can easily get started with Kore.ai by using it from the marketplace, as there are multiple marketplace applications that we have already designed and implemented.

I am a partner with Kore.ai, as my company has a business relationship with this vendor beyond just being a customer.

Kore.ai is deployed in my organization as a private cloud, and we can use it for our own employees.

I have designed, implemented, and developed 25 or more chatbots on it, and one of my chatbots is also placed on the marketplace in Kore.ai. I am also receiving compensation from Kore.ai, so I view everything positively.

Since using Kore.ai, I have seen multiple improvements. I have used multiple platforms, including LivePerson and LUIS, and Kore.ai provides considerable support for implementation.

I would advise that Kore.ai is very useful for developing and designing chatbots for any related field, as things automate very easily using the channel integration and API calling.

I find the documentation and resources provided by Kore.ai to be very useful. I have used them extensively, and they are very helpful, with proper examples and good clarification.

I receive updates or new features from Kore.ai in my email since we have connected with Kore.ai directly. Each time, I receive an email, and I go to my platform to update it there.

The community support or user forums for Kore.ai are very useful. Sometimes, when I face an issue, I go to the forum to ask a question, and they answer very easily and respond in a short timeframe.

I rate this review a 7.5 out of 10.

Our main use case for Kore.ai is developing four chatbots which are used to serve customers relating to various tax products. We had different tax products in our organization, and our purpose was to provide customers with FAQ answers, obtain their bank details, retrieve their other product details, assist if they would like to order something, connect them to an agent, and answer any kind of query that they have regarding their products or anything tax-related.

One of the main chatbots I built for a co-system tax product allows users to come in and filter based on which product they are using. They can get information about their locators. If they have filed a tax and received some kind of rejection, they can get their information on that. We also had different kinds of forms which the user could fill and submit their information to the back-end team to act on it. Apart from that, they could also connect to a live agent to get any queries sorted which were not handled through the chat agent. We also had a case creation feature that creates a case at the back-end for someone in our team to look into.

Kore.ai has positively impacted my organization by being a big money saver because before having our chatbot, the use case was that every time some of our customers needed help, they used to call a number. During tax season, the wait time on that number would go up to one or two hours, which cost our company a lot of money. Integrating these chatbots helped reduce that wait time significantly. Mostly, people can chat instead of calling, or they don't even need a person to solve their basic queries, which the chatbot can handle. It reduced the calls made to our agents by 50 to 60 percent. That was evident as our team, which started with three or four people for one bot, expanded to around five to six bots, and a lot of new people joined. We received a lot of extra funding to create agents for all the other products and teams we support.

Some of the best features in Kore.ai include that it is a low-code, no-code tool. I have seen a lot of low-code tools that don't give anyone any room to work with if they have a custom use case or something they don't support. Kore.ai is different in that way; even though it is a low-code platform, we can still write our code if we want and override whatever they are trying to do, in case it is ever needed. Apart from that, their Web SDK and BotKit support is very good, as they have all the functionalities, packages, and everything up to date and available for us to use. Also, they have a lot of in-built integrations for live agent connections. So if you want to connect with Salesforce, Genesys, Live Help Now, Telephony, or any of these kinds of back-end services, including ServiceNow, they just have an integration built-in, so you do not need to write it from scratch. But apart from that, they also give the ability to write it from scratch through a custom BotKit if you don't want to go with their features or if something is missing.

Regarding how Kore.ai can be improved, I think agentic AI is booming. Instead of us designing the whole conversation, which feels kind of boring and repetitive, they could move into a different kind of approach where we could write a prompt, and it should create a conversation based on that, in the way that Microsoft Copilot Studio does. So they should move a little more towards the agentic AI approach and start working on and integrating different prompts so we do not need to design conversations. Nowadays, people are used to talking with ChatGPT, which facilitates a human way of conversation, just back and forth. People are not used to seeing a lot of menu buttons, icons, or lists to select things from. Kore.ai does a little bit of that using the agentic nodes and other features they have added, but they should move further in that direction.

The documentation for Kore.ai often poses a challenge because many times when we had issues, we couldn't find anything in the documentation. We either needed to figure it out ourselves through trial and error, or we raised a support ticket. Some of the engineers came and told us that we could do something to solve our problem. It took them two minutes to tell us, but when we asked if there was documentation for this, they mentioned there wasn't. They just knew it because it is some kind of internal knowledge. So if they improve their documentation, it will be beneficial for everyone, as we will not have to raise tickets for any issues.

I have been using Kore.ai for the last four and a half years.

Kore.ai is pretty scalable because when we started, we just had one bot. However, as we expanded to multiple bots with different kinds of BotKits and Web SDKs, we didn't face any issues with scalability. We also have many lower-end bots, so scalability has never been a problem.

Customer support is okay, I guess. We get what we want from them whenever there is an issue or something. But sometimes when we had issues, it was delayed a lot. We had to reach out to their support team, and while they tried to suggest solutions, they didn't always work. Eventually, after a few calls, they involved some engineers who could actually fix our issues. Sometimes it was bad, but I can say around 60 to 70 percent of the time, it was good. We got the support we needed.

I didn't have a previous solution because we have been on Kore.ai from the start.

I have seen a return on investment with Kore.ai, as it definitely has saved time. We previously only had a helpline where one agent could only attend to one customer at a time. But now, because it is chat, one agent can handle up to three or four chat sessions at the same time for different employees. That is certainly an improvement. Additionally, our chatbot solves a lot of users' problems on its own, meaning it doesn't even necessitate a live agent or person sometimes. This represents a direct time saving for agents to focus on more complex issues.

Before choosing Kore.ai, I know they evaluated other options, but that happened before I joined the team, so I was not part of those conversations. I know they considered Google's solution, which I forgot the name of, and also Yellow.ai and Google Dialogflow, but ultimately they went with Kore.ai.

One of the unique or unexpected things regarding our main use case for Kore.ai is that I have seen a lot of our teams just use the basic default template that they have. But because our company had a design system, we had to rewrite the whole Web SDK code and design it according to ourselves.

Apart from that, we also had to make a lot of changes in the back end to support a live agent because our live agent handles up to 20,000 to 30,000 users on a daily basis. We had to increase capacity and ensure everything was worthwhile so it did not fail while users are currently there, especially during the tax season.

My basic advice for others considering Kore.ai is that they are adding a lot of new features. They have integrated agentic nodes, switched to the XO 11 platform, and introduced GenAI features. It is beneficial to look into these developments and keep up with whatever is happening. Don't get stuck in old ways of making bots. They are introducing a lot of new features; some are good, some are not, but eventually, everyone should try those features and move towards the new agentic platform. I would rate this solution a 7 out of 10.

My main use case for Kore.ai at the beginning was automations for machine learning, but right now they are implementing agentic solutions, and they are doing it really well. Other companies have some small issues, but in broad terms, the most common use case is to automate the entire customer attention process of contact centers in several companies.

For banks, we use Kore.ai to automate the entire customer attention process, such as giving access to statements, providing balance information, activating credit cards, blocking cards, locating branches, and developing transactions. When the bot has no capability to solve a question, it can escalate and move to a human agent with the contact center solution. This normally gives banks the capacity to reduce between 30 to 45% of the traffic that would normally go to human agents. With this capability, they can lower the volume of human agents and focus those people on selling or other areas to be more productive for the bank.

Kore.ai has many other use cases, but I believe that is the most important.

Kore.ai has positively impacted my organization because we are implementors, and I would say that approximately 80% of our income comes from developed projects with Kore.ai. We are Platinum partners of theirs, and we have many banks, financial institutions, and big retail companies, as well as innovative FinTech companies that are using this solution right now and having really good results.

What we normally see is that we guarantee almost between 30 to 45% of budget reduction in contact centers. Because of the containment rate that the agents generate, companies are not going to need so many people giving attention to their end customers, and they can move these people to other areas such as sales or other areas that they need internally in their company.

The best features Kore.ai offers include a really low-code solution. If you are not an expert in building agents, it is going to be easy for you to understand how it works and start deploying solutions. It is not a highly specialized platform and is more focused on resolving or giving value in a short time to develop something that could start to give you results. I think that is one of the most important things about Kore.ai. The other one is that it is an agnostic platform, so you can integrate all types of different LLMs in one single app, allowing you to work with your budget. This is something that right now with the agentic solution is really important to understand and handle.

The low-code aspect helped my team because you do not have to be an expert technician or an expert in the field to understand how to build an agentic solution. If you know your business, you can start to build something really nice with Kore.ai.

Regarding agnostic integration, Kore.ai lets you use different LLMs together in one app, which is important because imagine that you need to perform several activities such as receiving documents, analyzing those documents, and then preparing an email or handling a lot of data to understand a statement. You are going to need different types of LLM models. For example, if you are going to communicate straightforwardly to your customer, you are not going to need a really big and complex LLM. Maybe something mini or flash will help you move forward. But when you are going to analyze a complete statement and you want to give the right answer to your customer, you are going to need bigger models. The good thing about Kore.ai is that it allows you to have integration with commercial models, open-source models, or you can host it internally in their platform. Having that capacity, you are going to be able to push forward and develop a solution without any hassle.

Right now, many other companies face this issue because they are looking to be on top of the technology every single day, and Kore.ai is managing short times of delivery of new versions of their products. This is nice, but the QA process sometimes is not quite the best. You can have releases with several bugs in the middle that affect the behavior of your cloud customers. If you are in an on-premises environment or a private cloud environment, it is different because your version is not going to be affected every single sprint. But if you are in the public cloud, that could happen, and that is something that maybe you need to take into measure if you are going to develop something really delicate for your company. Normally, I highly recommend to our customers to start with something really simple and helpful, and then we are going to be escalating in the meantime. This gives the customer time to be prepared and the platform to be more solid in the features that we are going to be using in the future.

In support, the team is not the greatest, but it works. I would say that having multilingual support would be helpful because you have customers that normally are a little bit desperate to have an answer back. Because support is focusing on just one language and one time zone specifically, gathering attention 24/7 immediately is something that maybe takes a little bit more time than usual.

I have been using Kore.ai since they started in 2014.

It is really easy to upgrade your company and put it into the new era of agentic attention in a short time. Normally, the time it takes us to deliver a solution is between two and a half and five months, which is normally what we have taken to move forward and develop a full solution for an end customer.

We are the partner of Kore.ai.

Kore.ai has positively impacted my organization because we are implementors, and I would say that approximately 80% of our income comes from developed projects with Kore.ai. We are Platinum partners of theirs, and we have many banks, financial institutions, and big retail companies, as well as innovative FinTech companies that are using this solution right now and having really good results.

You can purchase Kore.ai through the AWS Marketplace, but normally the best way is to talk to a partner of Kore.ai and they can help you with the whole process.

The advice I would give to others looking into using Kore.ai is to look forward to having a really nice implementor, for example, someone like us. Because the solution is really easy, but to understand all the different scopes and all the different features that you can use takes time. My recommendation is to be in touch with partners like us in order to maximize your efficiency in a short time. Normally, the implementation is not going to cost you so much money, so it is something that if you look at it, it is going to be more of a benefit than trying to do it directly by yourself at the beginning. After you have a first release and you understand how the whole process works, it is going to be easier for the company to take hands on that and maintain and develop new products by themselves. I give this product a rating of 9 out of 10.

The main use case for Kore.ai was building a chatbot for one of our insurance clients who required a chatbot to allow agents to directly ask about documents uploaded for verification. The chatbot checks if documents exist at a particular link that contains the personal documents of clients and then sends the status back to users or agents. If the document is present, agents may request to read it and check if certain conditions are fulfilled in the document. This is essentially a document validation agent created using Kore.ai as the front end, coupled with Teams channels for the agents.

Kore.ai helped our team specifically in building the document validation chatbot by serving as the front end of the entire development. Using Kore.ai, we connected with the Teams channel and leveraged features such as API connections and API calls to easily retrieve the status of documents.

The best features Kore.ai offers include a very user-friendly interface, making it easy for agents who are not comfortable with technology to navigate it. This is the main aspect I would highlight.

The user interface of Kore.ai is so user-friendly because it is a no-code platform that provides drag-and-drop tools for building chatbots or agents without needing to write code. Kore.ai also offers excellent connectors with different services, and in our case, we built a microservice that was called directly from Kore.ai through API calls. Additionally, it offers popular application integrations such as SAP, ServiceNow, and Salesforce. These features have significantly improved the customer experience and made it scalable enough to manage millions of interactions, which suits large enterprises.

Kore.ai has positively impacted our organization by reducing error rates from around fifteen to twenty percent to just two to three percent after implementation. This significant reduction in errors means that fraud detection is not negatively impacted. It has also helped reduce the time agents spend on document validation, thereby increasing efficiency and accuracy. These are the main two metrics that have contributed to our success.

In terms of time saved, the accuracy has dramatically increased, with error rates decreasing from fifteen to twenty percent down to two to three percent. We have saved almost two hours of daily work per agent. With more than twenty agents working on this document validation task, we are saving a total of around forty hours per day.

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.

I have been using Kore.ai for around eight or more months in my previous company, where I worked on a proof of concept for that particular engagement.

Kore.ai is stable.

Kore.ai's scalability is good, and it is indeed scalable.

The customer support for Kore.ai is really good.

Before Kore.ai, we used InteliX, which was one of the vendor products we explored but switched to Kore.ai due to its superior connectors and more affordable licensing compared to InteliX. Additionally, the user experience in Kore.ai is much more user-friendly, unlike the complex user interface of InteliX.

My experience with pricing, setup cost, and licensing has been good. Although I was not directly involved in the pricing discussions, the setup costs and licensing were straightforward, and we received excellent support from the Kore.ai product team. Their training sessions were effective, and we also achieved certifications through mini-projects alongside the training, making the transition and onboarding process quite smooth.

We have seen a return on investment, particularly in full-time equivalent count saved.

We did not evaluate any other options besides InteliX, but we also tried Copilot. Unfortunately, Copilot did not deliver good accuracy during the proof of concept, and its licensing costs were higher than those of Kore.ai, which is why we ultimately chose Kore.ai.

Kore.ai has impressive features, such as effective connectors and a very good user interface. It is a no-code platform where drag-and-drop functionality is sufficient to build logic without needing to write code. It is easy to deploy agents and much more scalable than other tools I have explored so far.

My advice for others considering Kore.ai is that it is really easy to use for beginners. People will quickly learn how to use it and deploy their own use cases. Kore.ai is a good product, and I would rate it an eight out of ten.

My main use case for Kore.ai is developing and deploying enterprise-grade AI agents and chatbots, which includes designing conversational flow, setting up intent testing, and evaluating LLMs that can integrate to automate customer interaction and streamline internal support workflows.

One specific example involved developing a demo virtual assistant designed to optimize internal support workflows and customer interaction testing. A key part of our workflow was evaluating how well different LLMs integrated with the platform and also rigorous intent testing implemented in that.

I think the best features about Kore.ai are how easy it is for a developer to use. For example, the drag-and-drop dialog builder is exceptional. Also, NLU and intent testing are also good.

When comparing it to other software, I think it is easy for a developer to build the agents, which helps significantly reduce time-to-market while keeping the architecture clean.

In terms of integration and flexibility, Kore.ai provides a significant advantage that makes it highly adaptable for complex enterprise environments. For example, API and back-end integration, authentication handling, and data mapping, etc. Also, multi-channel deployment flexibility is a feature as it is an omni-channel agent.

Kore.ai helps in operational efficiency and faster time-to-market, which is development velocity. Although it has had its cons, such as the platform being buggy and support not being that great.

One thing I have learned is the unpredictability of LLMs and how buggy Kore.ai is as a software. We had a lot of issues with Kore.ai, and now we are trying to shift to other software.

Kore.ai has many cons, especially in that the platform needs to focus on stability as it is buggy. It is hard to find mistakes, as it is difficult for debugging, and it also slows down during heavy training.

Better guides would help, as the manuals explain what buttons do, but they lack good examples. It would help to have simple step-by-step guides showing how to write custom code or fix errors. Additionally, it would be better to have good support since the platform has serious technical glitches, and the customer support takes too long to fix them. The first reply usually offers basic tips instead of quickly sending the problem to senior engineers. Furthermore, I think the platform is expensive and is geared towards massive corporations. I think it needs to have cheaper basic pricing options so smaller teams can test out quick ideas without spending too much money.

I have been using Kore.ai for around one year.

Currently, I do not think Kore.ai is stable because previously it was.

Kore.ai has one of the best scalabilities in terms of handling massive growth in both user traffic and conversational complexity.

I think the customer support needs to improve, as it is inadequate right now. They do not resolve issues quickly and they do not forward it to senior engineers. Rather, basic support is provided.

I think there is a reduction in fallback rates. By fine-tuning hybrid NLU, the bot's ability to correctly understand user intent has increased significantly. I think it led to an approximate thirty to forty percent reduction in unhandled fallbacks.

I think people should focus on hybrid NLUs and not just use any large LLMs for everything. You can use a standard visual builder for important transactions to keep them accurate. Save the LLM features for unexpected questions or conversational fallbacks, and also prepare for the learning curve, as the platform is easy to learn for basic setups but hard for advanced coding.

My overall review rating for Kore.ai is six out of ten.

I used Kore.ai to make dashboards, widget creation, and I tagged some columns. I wanted to create dashboards to get all the details for policy, specifically to understand who are the legacy policy users and who are the Guidewire policy users.

The widget in our system was for the policy product. In that product, users were not able to see on a page how many users logged in today, whether a legacy person or Guidewire user logged in, or what changes or modifications they made. I created dashboard widgets so that legacy users and Guidewire users could track the changes they made in their policies.

I used Kore.ai for this purpose. Before that, I migrated from Kore.ai 10 to 11. I trained the model by adding utterances as training data and achieved almost 97% accuracy. After that, I started working on widget creation.

The best feature I have worked on is widget creation. I went through the entire chatbot workflow to create it, so I have a good understanding of that part. I appreciate the process of adding tags and fetching data by creating a dashboard.

For the admin side, they will get an audit that shows who logged in—legacy or Guidewire users—and what modifications they made. This helps them by seeing that dashboard rather than viewing each policy individually.

It impacted my organization by helping us move into being AI-native. Our organization is proving themselves that they are AI-native, and it helped a lot to show other clients that we have worked in AI.

I have worked only for six months on Kore.ai. While working on it, I haven't seen any area that needs improvement because I need to explore more in voice chat. I don't think there may be any improvement needed for that part as of my knowledge.

I want to work on voice AI chat, but I have not yet been involved in that. For that project, I was working on widget creation, training the model, and migrating from Kore.ai 10 to 11. Then I moved to a different project, so I couldn't explore that. This is a regret for me. In the future, if I get an opportunity with Kore.ai, I will definitely work on voice AI chat.

I have used Kore.ai for six months.

Kore.ai is stable.

Kore.ai is a scalable product. I think they can add more features, but right now I'm unable to recall something specific for improvement. Definitely, it's a scalable product.

The customer support was good. We connected two or three times and received full support from the customer support team.

I haven't switched to any other solution. I got Kore.ai first and worked on that only. It is a good product.

It is definitely a money saver and time saver.

I haven't evaluated other tools. Kore.ai was the first one, so our organization went with it.

For the process of adding tags and fetching data for dashboards on the admin side, admins will get an audit that shows who logged in—legacy or Guidewire users—and what modifications they made. This will help them by seeing that dashboard rather than viewing each policy individually.

I give a nine for the customer support. I will recommend Kore.ai to others. If anyone is looking for tools similar to Kore.ai, I will definitely recommend it. I will recommend it to someone if they are having issues or problems with other tools. I definitely want to explore more of this product. I gave this review a rating of seven.