Cognigy.AI Platform Reviews

My main use case for Cognigy.AI Platform is building conversational AI agents and customization, adding additional extensions in knowledge AI, and building XApp. These are the key parts of my work with Cognigy.AI Platform.

A specific example of how I have used Cognigy.AI Platform for one of those conversational agents or customizations is when one of our customers needed knowledge integration of their proprietary knowledge with Cognigy.AI Platform. I built a custom extension which accesses the knowledge AI and their knowledge bases in Cognigy.AI Platform. This is one of the customizations. Additionally, for XApp, I built features to get a lot of consent from the customer through pop-ups. There are many customizations and implementations I have completed as part of XApp.

I mainly focus on customization based on the customer's business requirements and have worked on various interesting implementations with Cognigy.AI Platform.

In my experience, the best features Cognigy.AI Platform offers include AI agents that allow me to quickly build conversational flow. The NLU part with external natural language understanding providers is also a key portion. Then there are the copilot, agent copilot, and voice gateway integrations along with knowledge search. These are the great features of Cognigy.AI Platform. Now it is integrated with the CX1 knowledge base, so I can quickly call external services using web API or DB requests or trigger functions. These are good features of Cognigy.AI Platform.

Out of those features, I usually use the code node, which is most useful for me. The knowledge search and all XApp nodes—Init session, how adaptive cards are shown, and show HTML—are features I use as part of my implementation.

Apart from that, I sometimes use live agents and explore the analytics part, but I do not dive deep into the analytics area.

Cognigy.AI Platform has positively impacted our organization as I was working with various platforms such as Avaya, Cisco, Genesys, Google CCAI, Omelia, and Amelia. Cognigy.AI Platform is easy to learn and has fewer nodes, but it impacts the building of quick flows significantly. I can easily convince customers because it is an LLM-enabled platform. This makes it easy to convince customers to implement their business needs quickly. That is the overall impact.

Regarding how Cognigy.AI Platform can be improved, overall it looks good. However, some deep documentation with different use cases would be beneficial. The current documentation is plain. As an implementation engineer, if I want to understand customer requirements and how they should be implemented, there is no systematic process. Documentation on how to collect and convert customer requirements into workflow, and then how to implement the same in Cognigy.AI Platform, would be really helpful. Those pieces of information will help those who are newly understanding the platform and will speed up the overall process.

Regarding needed improvements, the translator service currently supports only three providers: Microsoft, DeepL, and Lex. However, I need to add some additional services, but I do not see any customization as part of the translation service if I want to add it from my side. Those customizations would really help to improve the platform by bringing your own translator instead of using the out-of-the-box translator service. If customization were provided in those areas, it would be really helpful for us.

I have used Cognigy.AI Platform for the last one year.

Cognigy.AI Platform is stable in my experience.

The scalability of Cognigy.AI Platform is excellent. If call volumes are increasing, it handles the increase well due to the internal architecture of Cognigy.AI Platform. I do not see any concerns regarding scalability.

Customer support has been good. Our support team usually raises tickets to Cognigy, and they coordinate accordingly. I am mostly involved directly with the customer.

I previously used Google CCAI for conversational tasks before adopting Cognigy.AI Platform.

The main reason I switched from Google CCAI to Cognigy.AI Platform is that Cognigy.AI Platform is user-friendly. All the nodes are set up so that even a layman can easily understand them, and business users can create workflows easily. In contrast, Google CCAI requires proper training, which is a time-consuming process. That is the reason I chose Cognigy.AI Platform.

I have seen a return on investment with Cognigy.AI Platform. The time saved is significant, although I do not have any data on the overall cost at this time. Previously, it would take a month to build all conversational flow and IVR flows. Now it takes just days. It is a good platform, and being LLM-enabled, the time saving is a key benefit.

I am part of a technical team, so the pre-sales team takes care of cost and licensing. I am not much aware of these details as that is outside my area.

Before choosing Cognigy.AI Platform, I evaluated other options recommended by our pre-sales team. They did a comparison with other conversational AI platforms such as Omelia, Amelia, and Google CCAI. However, I do not have data on why Cognigy.AI Platform was chosen, as that decision was made by the pre-sales team.

My advice for others looking into Cognigy.AI Platform is that it is easy to integrate, implement, and learn. It is also very flexible, supporting various contact center products such as Genesys, Cisco, or Avaya. Additionally, it enables different voice types and supports multi-channel, omni-channel, and digital channels. These features make Cognigy.AI Platform a great choice for conversational AI agents.

I am really impressed with Cognigy.AI Platform and the way it has been implemented. It is easy to use and user-friendly. I would rate this product a nine out of ten.

My primary use case for Cognigy.AI Platform was building and evaluating enterprise conversational AI solutions. I mainly used Cognigy.AI Platform to design chatbot conversation flows, configure intents and entities, integrate backend APIs, and test end-to-end customer scenarios. One example was creating a virtual assistant that could answer FAQs, authenticate users, and retrieve information from backend systems through API integrations to automate customer interactions.

One challenge I faced with my main use case was designing conversation flows that could handle multiple user paths while keeping the experience natural and easy to maintain. I also spent time testing API integrations and error handling to ensure the bot responded gracefully when backend services were unavailable. Overall, it was a good learning experience and helped me understand how to build scalable enterprise conversational solutions. Since my use case was mainly for evaluation and a proof of concept, I focused on understanding Cognigy.AI Platform's capabilities and comparing them with other conversational AI platforms. The main challenge was getting familiar with the platform's architecture and identifying the best approach for designing conversational flows and integrations.

What stood out to me when building that virtual assistant was the visual conversation designer. It made it easy to create and manage conversational flows without writing a lot of code. I also appreciated how Cognigy.AI Platform is built with enterprise use cases in mind, especially its integration capabilities through APIs and webhooks. Compared to some other platforms I have used, I felt Cognigy.AI Platform provides a good balance between low-code development and flexibility to customize more advanced workflows when needed.

In my opinion, the best features Cognigy.AI Platform offers were the visual flow designer because it made it easy to build and maintain conversational flows. I also appreciated the platform's integration capabilities through APIs and webhooks, which made it easier to connect with enterprise systems. Another strength is that it supports both low-code development and more advanced customization. It works well for different levels of complexity, and overall, I found it well-suited for enterprise conversational AI use cases.

From my evaluation, I appreciated that simple conversational flows could be built quickly using the visual designer, while Cognigy.AI Platform also allowed API integrations and custom logic for more complex scenarios. The flexibility means teams can start with low-code development and then extend the solution as business requirements grow without having to move to a different platform. In the proof of concept I worked on, I used the visual flow builder for the core conversation and integrated external APIs to retrieve dynamic information. The visual interface made it easy to modify the conversation, while the API integration allowed the assistant to provide real-time responses instead of relying only on static content.

Overall, I had a good experience with Cognigy.AI Platform, but I think there are a few areas that could be improved. The onboarding experience for new users could be more intuitive with additional hands-on tutorials and real-world sample projects. I also think the debugging and error tracing experience could provide more detailed guidance when integrations or conversation flows don't behave as expected. Finally, having more pre-built templates and connectors for common enterprise use cases would help teams get started even faster.

Regarding that, I think the documentation could include more end-to-end enterprise examples and best practices for common use cases. It would also be helpful to have more interactive tutorials for new users and improved debugging tools that provide clearer error messages and troubleshooting guidance. Other than that, I didn't encounter any major issues during my evaluation and found the overall user interface clean and easy to navigate.

I have been working in the current IT field for around seven years.

Based on my evaluation, I found Cognigy.AI Platform to be stable. I didn't experience any major crashes or reliability issues while building and testing conversational flows. Since my experience wasn't from a large-scale production deployment, I cannot comment on the long-term operational stability, but for the use cases I evaluated, it performed reliably. I would rate its stability around a 9 out of 10 based on my evaluation experience.

Based on my evaluation, I believe Cognigy.AI Platform is designed to scale well for enterprise use cases. It supports complex conversational flows, integrations with enterprise systems, and the ability to manage multiple bots and channels. I didn't test it under high production loads, so I cannot comment on the performance at scale from firsthand experience. From the architecture and features I explored, it appears to be well-suited for organizations that need to scale their conversational AI solutions.

Based on my experience, I had limited interaction with Cognigy.AI Platform's customer support since my experience was mainly through an evaluation. Based on the resources available and the assistance I received when needed, the experience was positive and responsive. I didn't encounter any major issues that required extensive support, so I cannot fully evaluate their long-term customer service. I would rate around 8 out of 10.

Before evaluating Cognigy.AI Platform, I had experience with platforms such as Dialogflow, Yellow.ai, Core.ai, and Microsoft Bot Framework. I didn't switch away from those platforms; rather, I evaluated Cognigy.AI Platform to understand its capabilities and compare it with other enterprise conversational AI platforms. My goal was to assess how it handled visual conversation design, integrations, and enterprise use cases.

Since my experience was limited to an evaluation and proof of concept, I didn't measure a formal ROI or business metric. However, I noticed that the visual development approach reduced the time needed to build and iterate on conversational flows compared to coding everything manually. It also made it simpler and easier to prototype ideas and gather feedback, which can help reduce development effort during the early stages of a project.

As part of the evaluation, I also looked at platforms such as Dialogflow, Yellow.ai, and Core.ai. I wanted to compare their capabilities around visual conversation design, integration options, scalability, and overall suitability for enterprise conversational AI use cases before assessing Cognigy.AI Platform.

My advice would be to start with a clear use case and spend some time understanding Cognigy.AI Platform's visual flow designer and integration capabilities. Take advantage of the available documentation and build a small proof of concept before moving to a larger implementation. That approach helps you understand how Cognigy.AI Platform fits your organization's requirements and allows you to get the most value from its enterprise conversational AI capabilities. I would recommend Cognigy.AI Platform to organizations looking for an enterprise conversational AI platform, especially if they need strong integration capabilities and a low-code approach while still having the flexibility to implement more advanced use cases.

Overall, I had a positive experience evaluating Cognigy.AI Platform. I think it is a strong enterprise conversational AI platform with an intuitive visual development experience and good integration capabilities. While there are areas where the onboarding experience, documentation, and debugging tools could be improved, I believe it is a solid choice for organizations looking to build scalable conversational AI solutions. I give this review a rating of 8 out of 10. I appreciate the opportunity to share my feedback.

Over the past year, my main use case for Cognigy.AI Platform has been to build both voicebots and chatbots for B2C use cases across several industries. Projects have included a voice assistant for an automotive dealership that automates appointment scheduling, handling identity verification, customer data lookup, and escalation without using staff involvement, as well as projects related to an internal employee-facing chatbot for a large corporate to handle ID and document processes, automating this entire process with self-service flows such as identity verification, document request, and appointment booking, as well as developing a multilingual, agentic hotel chatbot answering guest questions using both German and English.

The project for building an assistant for an automotive dealership stands out as the most interesting and challenging because we had to integrate multiple databases and use API endpoints, which made it complex because of the way the endpoints were also built. Understanding users' requests and intents regarding when they want their input was very challenging as well.

One of the best features Cognigy.AI Platform offers is being agentic because when I previously started working with Cognigy.AI Platform, it mainly was rule-based using NLUs. Currently, having agentic capabilities and using the tools is very useful, as well as having Playbooks because they really automate the way that we test. We used to do manual testing, and with more engagement with Cognigy.AI Platform, we have started really using Playbooks, and they are a very helpful feature.

We have mainly used Playbooks for testing on Cognigy.AI Platform, so we have created our use scenario and what we expect to have from our flows. Instead of going through manual testing where we have to type everything one by one, we run a Playbook, and it checks whether the feature is completely developed as it should be or if there are any edge cases that are happening. This simplifies the way we test and really helps to stay on track with the use case that we are creating or implementing.

Cognigy.AI Platform has really helped us positively in our organization with our customer projects because the projects that I have worked on have been customer-related. We also use it internally for automating different tasks, such as ID card registration. Instead of employees going through the entire process and communicating with different colleagues to just get access for a certain building, having to do it via Cognigy.AI Platform is much easier and more simplified.

One of the biggest challenges I continue facing while using Cognigy.AI Platform is the fact that the platform lacks proper version control, and this makes it difficult when I am working with multiple team members and it is very hard to track the changes that I do or the changes that my peers are doing.

One improvement for Cognigy.AI Platform would be creating a version control, which would really help developers. This way, we can keep track of what we are doing and how we are doing it. Additionally, if we could make an integration with IDEs just like Visual Studio Code, it would make easier implementation for the coding part and just to have an integration between an IDE and the local platform. I think that would be an interesting feature.

I have not had the chance to have a lot of communication regarding customer support because it is handled by another level of support for us, but it has been quite useful. Every moment that we have had the need for customer support from Cognigy.AI Platform, they have been there for us, solving our issues and guiding us.

If I take it from the customer side point of view, I think we have seen a return on investment. Automating customer service really reduces the number of employees needed during peak hours, which is an achievement, especially if you are dealing with multiple calls per day. This would reduce a lot of the work for the employees.

Cognigy.AI Platform has really helped us positively in our organization with our customer projects because the projects that I have worked on have been customer-related. We also use it internally for automating different tasks, such as ID card registration. Instead of employees going through the entire process and communicating with different colleagues to just get access for a certain building, having to do it via Cognigy.AI Platform is much easier and more simplified.

I would suggest getting into Cognigy.AI Platform. The more you start working with it, the more you can see that you can do a lot of great things. You learn by doing and by being curious about what Cognigy.AI Platform can offer.

Regarding Cognigy.AI Platform's AI capabilities, I would say its governance and security are good so far, but this is not one of the scopes I focus on.

When it comes to the accuracy and reliability of output from Cognigy.AI Platform, the information is accurate. We just need to always be careful with the guardrails, mainly when prompting. I would rate this review an eight out of ten.

My main use case for Cognigy.AI Platform involves building AI agents for a couple of industries, mostly focused on the telco industry.

I have completed several projects with Cognigy.AI Platform in the telco industry. I started with creating simple Q&A and FAQ agents. Then I worked on a project where I was helping customers with next best offer creation. We were from the customer service side, and after closing service calls, we wanted to offer upsell opportunities to customers, which is the type of agent that I built in one of the projects. I also attempted one where I wanted to cater to a couple of intents where the top intents were billing dispute and network outage.

The best part of using Cognigy.AI Platform is that the user interface is very friendly and easy to use. Learning the platform itself is quite good. The best part is the academy that Cognigy provides, and since we are partners, I have access to courses where I can learn more. Learning through those courses is very easy, and using Cognigy.AI Platform, the UI that is made available, the flows, the nodes, branching, all these features are very handy. The options for STT and TTS, along with the right panel where I can perform many commands, are also quite solid. The integration capabilities are a strong suit of Cognigy.AI Platform. For example, I have worked actively with Salesforce, and it is relatively less challenging to integrate Cognigy.AI Platform with Salesforce. These are the major aspects where I prefer Cognigy.AI Platform compared to other similar platforms that I have used.

I would also like to highlight that the flow and UI of the portal itself are excellent. The lexicons, AI agent side, LLMs, and knowledge hub—all of which are most pertinent to building the journey—are right in front of me. The broad window where the flows are being built is also very nice. When I right-click on any box, the options that appear seem to include everything I need. The right-click menu presents all necessary features in an intuitive way, making it easy to use.

I feel there are challenges with scripting in Cognigy.AI Platform. While building nodes, I often had to do scripting, and just by prompting or providing instructions, the node was not able to behave the way it should. I had to resort to scripting, but scripting is not easy in Cognigy.AI Platform. Additionally, there are no handy resources available that could tell me which commands will actually get followed. For example, if there was a yes or no node, every time it was going towards yes despite providing multiple instructions, and eventually, I had to do scripting using some GPT assistance to find out what script would work. It worked, but it was quite a hassle.

The scripting challenges extend to documentation as well. I could not find very handy resources available to learn how to do scripting in Cognigy.AI Platform. All the tutorials talk about nodes and joints and how to do prompting, how to provide instructions, and how to create multiple layers of branching, but I do not recall any resource that specifically covers scripting. There was probably a development course available initially, but it is no longer there. I could not find handy resources to refer to when doing scripting, and I had to resort to trial and error. I tried a couple of scripts from Chat GPT, but they were not working, which meant they were not correct. The problem was that I did not know what the correct script actually was.

Regarding features I wish were included, I think it would have been beneficial if a pre-built LLM was available in the sandbox environment or trial box. In my organization, I often create demos or POCs before going to clients, and at that time it becomes a bit of a challenge because I do not have LLMs available just to showcase. Perhaps with limited tokens, some LLM should be available. The voice bot and STT and TTS are good, but the ease into chatting is more than building a voice bot agent. I cannot pinpoint exactly what, but there is a difference in ease of use between these features.

I also think it would be great if Cognigy.AI Platform could provide more use case specific pre-built AI agents. The current pre-built agents look very much like placeholders and are very straightforward. In Salesforce, for example, there are agents that are specifically for summarizing, account discovery, or sampling and ordering. These are very niche-based use cases that are pre-built. If Cognigy.AI Platform could provide more niche use cases for each industry instead of having a blanket one, that would ease deployment when creating something or pitching Cognigy.AI Platform to clients. This would reduce implementation time drastically. Regarding the voice bot, ElevenLabs is a very good platform that is integratable, but if those features were native to Cognigy.AI Platform, that would have been great because ElevenLabs is becoming a favorite when it comes to voice bot capabilities.

I think Cognigy.AI Platform can be improved by adding more features that other platforms are bringing in. It should become more multi-modal, and voice integrations should have more options available. As a conversational AI platform, Cognigy.AI Platform could benefit from video-based conversation capabilities similar to what other platforms offer. The world is moving towards a direction where customers would prefer to show rather than speak or type, and if Cognigy.AI Platform could advance in that direction, it would be very helpful.

The analytics capabilities could also be improved. Although I use conversation insights and analytics are provided, the dashboard cannot do analysis on many aspects, and much deeper analytics have to be pulled from the platform that I integrate into it. The analytical behaviors can be improved significantly. One interesting use case was about sentiment analysis, where I can call out whether it was negative, positive, or neutral sentiment. However, my clients want to go deeper into it, looking for sentiments such as happy, cheerful, pleasant, sad, and grief. If those kinds of sentiments could be brought into the analytics, it would be a big plus for Cognigy.AI Platform.

I have been using Cognigy.AI Platform for more than two years now.

I have not previously used a different solution for my clients. I have been working on Cognigy.AI Platform for the last two years only. Primarily, the use cases have been driven with the LLM that has been brought in. Before that, I worked with native features in Salesforce, such as Einstein chat. The quick deployment and short time to get up and running were a couple of aspects where clients were demanding Cognigy.AI Platform. This was not a case of replacement but rather a case of development for the first time. Most of the projects have been greenfield projects.

I have seen a return on investment with Cognigy.AI Platform for my clients. I have mostly targeted reducing wait time and AST, which are average speed to answer and wait time. These are the two areas where I have seen the most benefits. Although I target many KPIs, the feedback has been positive on the side that customers did not have to wait anymore and AST has gone down significantly. These are the two solid pieces of feedback I have received. There are others as well, but I would like to emphasize on these two primarily.

My advice to others looking into using Cognigy.AI Platform depends on who they are. If it is my colleagues and my team, I would rather have them learn it quickly and get their hands dirty as soon as possible. If it is clients, I would want them to have their ecosystem prepared, with their integrations and other components ready to be used actively with Cognigy.AI Platform. Since I work as a consultant, the adoption is not specifically for Cognigy.AI Platform but rather for the contact center or conversational aspect where clients are facing challenges, and I provide them alternatives, amongst which one could be Cognigy.AI Platform. I would rate my overall experience with Cognigy.AI Platform as a nine out of ten.

My main use case for Cognigy.AI Platform is building a chat assistant platform for our Swiss airline company. Users ask about rebooking or refund options for their flights.

Cognigy.AI Platform helps users with rebooking or flight options by saving us time, so we do not need to use human agents for chatting with customers. The user starts a chat and asks for a rebook. The bot from Cognigy.AI Platform asks for the PNR number, and then everything is done in Cognigy.AI Platform flows, where we define all the flows regarding rebooking or refunds. It really helps us and saves us time.

I think the best features of Cognigy.AI Platform are the flows and how easy it is to set up the flows. Also, the integration of endpoints is straightforward. You get the endpoint which provides an out-of-the-box whole chat assistant.

Setting up flows in Cognigy.AI Platform is easy for me because the user interface is intuitive and easy to navigate. There are plenty of tutorials and also a free academy on Cognigy.AI Platform where you can dive in and see all the benefits and how to use Cognigy.AI Platform. This really speeds up the platform.

Cognigy.AI Platform has positively impacted my organization because it saves us time implementing the chat assistant. We do not need to build the chat assistant from scratch. We are using Cognigy.AI Platform for it, so it is out of the box. It reduced the development time for new features and made it easier for both technical and non-technical team members to collaborate on chat improvements.

My overall experience with Cognigy.AI Platform has been positive. In terms of performance, it could be more reliable or more flexible for version control and collaboration. Clear visibility into changes between flow versions would also help teams working on larger improvements. More comprehensive documentation and additional real-world examples for advanced use cases would make onboarding and development smoother.

Cognigy.AI Platform is a great platform. It is a powerful and flexible conversational AI platform with strong integration capabilities and good developer experience. It has helped us build and maintain chatbot solutions efficiently. The reason I did not give it a ten is that there is still room for improvement in areas such as debugging, version control, editor performance on larger projects, and documentation for more advanced use cases.

I have been using Cognigy.AI Platform for one year.

Cognigy.AI Platform is really stable because every few days or few months we get a new release from Cognigy, so they are keeping it and maintaining it up to date.

From our experience, Cognigy.AI Platform has good scalability. It supports growing conversational volumes, multiple use cases, and complex conversational flows without major limitations.

The customer support for Cognigy.AI Platform is really good. Every week or so, we have a meeting with Cognigy from the customer support where we can ask any relevant questions or if we have any issues or bugs that need to be fixed from the Cognigy.AI Platform side. They are really great.

This is my first time using a conversational AI platform.

This was the only option that I could use because it was set up before I joined the team.

If you cannot build a conversational AI from scratch or you do not have any capacity to build one from scratch, then go for Cognigy.AI Platform. I would rate this platform an eight out of ten.

Our main use case for Cognigy.AI Platform is to provide a self-service solution to our global customers because LTIM is distributed across the globe and we have major customers across the US and Europe region, and we are also expanding in the APAC region, capturing industries such as BFSI, hospitality, manufacturing, and all relevant domains.

Recent use cases that we have built for our customers and in-house users include a fully self-service module using Cognigy.AI Platform that allows users to connect to HR and IT systems to get their queries answered. Based on the queries, Cognigy.AI Platform understands and integrates with backend systems, whether IT systems, HR systems, AD systems, or backend IT systems, which helps whenever there are new users onboarding or they have issues with an existing system. These issues can be verified via Cognigy.AI Platform, and Cognigy.AI Platform raises a backend request or issue that goes to the authorized person based on a persona. Once they approve or reject, Cognigy.AI Platform takes the next step and completes the process.

The best features that Cognigy.AI Platform offers are all based on AI use cases that are implemented as self-service, which are very good. We tried some use cases for medical lines where customers or patients are calling and it provides better schedules, appointments, and renewals of existing policies in the banking sector. In IT, we have onboarding where there are issues or requests for new devices that are very quickly adopted by Cognigy.AI Platform and it helps us.

The experience that we faced via Cognigy.AI Platform is very good, using its LLM modules or all that we have in-house with Azure and OpenAI. These are very deep and quick to easily understand, leading to no issues whenever users are speaking. Cognigy.AI Platform understands clearly what the user is saying and based on that, it provides the right response, thus reducing the average handling time and first call response to the user, improving our customer CSAT.

Cognigy.AI Platform has had a really good impact on our organization because whatever investment we are doing in the solution, it is giving the right output, reducing the cost of the investment, and we are getting better ROI. We tried to use other solutions, but as compared to those, Cognigy.AI Platform is really good.

Day-to-day work is basically focused on the use cases because it is a mix of that, and majorly we work on self-service, which is really good in Cognigy.AI Platform, providing better output, quick response, and it is easy to solve for the end users. I would say that majorly 90 percent of our use cases are in self-service.

Currently Cognigy.AI Platform is really good and I do not see anything we have to change. However, if you could put in-house LLM modules so that it should not go to find out on a third-party module, and whatever the cost factor you have, if that can be a little bit better, it will be more good to propose to our global customers.

If you could enhance somewhere in the training point of view for the administrator or users who are using Cognigy.AI Platform, that would be great.

We are using Cognigy.AI Platform for the last two to three years.

Currently Cognigy.AI Platform is stable, although I am not sure how much impact it will have if we are having an overload and how much load it can take.

I am not sure currently about Cognigy.AI Platform's scalability. As per the details from Cognigy.AI Platform or partners website, it can be, but I am waiting to just test it before I can confirm that it is fully scalable to be put at any limit.

The governance and security point of Cognigy.AI Platform is also good, as we tried and verified with our in-house governance team and AI evaluation teams, finding out that everything is very mature and properly designed.

We did not switch as we are using both solutions. We already have two or three solutions in-house and we all use those while also parallelly starting to use Cognigy.AI Platform, evaluating all three solutions. Most probably in a few months, we will have actual value based on costings, time saving, improvement and everything we will capture and we will share it out.

We have other solutions like Voicing.ai, Yellow.ai, Rezo.ai, and we also have Azure Voice Bots. We worked with all of them, and that is why we are using Cognigy.AI Platform now because we have seen a lot about Cognigy.AI Platform in the market and we are trying to use it. As a partner with NICE, we got it and we are evaluating that as well.

On an ROI basis, we evaluated based on other things. The costing is high, but the ROI as compared to long-term, say in five years or seven years, is achievable and that is good based on the feedback and the time we are spending with a normal human agent compared to a Cognigy.AI Platform virtual agent, which is really good.

Cognigy.AI Platform covers all three outcomes, including cost savings, time saved, and customer satisfaction scores, although I do not have actual numbers right now because we are still evaluating. Most probably within a year we will have all the data, but I would say that the major point is the cost and second is the customer satisfaction that is going high.

As compared to others and the market standard, the pricing, setup cost, and licensing for Cognigy.AI Platform is currently high. I am not sure if it is because of my partners, but the pricing that we have received is a little bit high. If you could work on that and give it as per the market standard, that would be great and you will have more business in that.

I would give advice to others looking into using Cognigy.AI Platform that it is a really good solution and they should try it, as implementing a solution for different use cases is a really quick and easy setup that provides good output.

If you have some good training material and hands-on labs, you could share them with me, as I want to try more on Cognigy.AI Platform. I provided a review rating of 10 for this solution.

I built an agentic bot for customer support with Cognigy.AI Platform, where I had the possibility to come up with my own architecture. I constructed both an agent that was performing intent recognition. To understand the accuracy of the agent, I built a judge that was an LLM system evaluating the agent responses and intent accuracy recognition. This was useful because since the analysis section in Cognigy.AI Platform is limited, by using another system to judge the decision, I was able to track how accurately the system was performing intent recognition.

The academy resources provided by Cognigy.AI Platform are valuable for getting started. After learning everything, there is a test that, once passed, provides a certification, which is useful as a backup document certifying that I am a developer in Cognigy.AI Platform.

In terms of building flows and working with the platform, the setup is easy to understand. There is no backend black box that could allow me to mess something up. The platform allows sharing projects with different people who can work simultaneously on the same project. I can see who is changing what and when, which is a strong feature.

Cognigy.AI Platform's natural language processing capabilities have helped streamline communication within my organization in positive ways.

Cognigy.AI Platform has enhanced my customer interactions because all clients have been positive about it and have seen the benefits. When connecting cloud systems with different sections that are organized, everybody can see what goes where and when. There is transparency that the client values.

Cognigy.AI Platform is limited from a developing perspective, especially regarding the analytics aspect that should improve. When debugging, there is no direct page for this. I have to go to the logs and see what errors appear each time I run the flow. There is significant room for improvement where the platform needs to think from a technical perspective and support different personas, both non-technical and technical.

For technical people, it would be beneficial if the platform could connect to Visual Studio Code so that technical people can easily debug without having to go node by node and adjust things whenever errors are thrown.

The graphs in the analysis section of Cognigy.AI Platform should display metrics such as average duration of calls or number of conversations, which would be helpful if visualized internally.

I have been working with Cognigy.AI Platform for six months.

Cognigy.AI Platform is performing accurately, but to do so, I need to restrict the LLMs properly. There is a lot of back and forth in terms of system prompt adjustments. If I rely solely on the LLM, things would go downside. I have to be very careful about how I am defining and setting up the prompt engineering.

I find Cognigy.AI Platform scalable.

The technical support team takes some time to provide a response.

The initial setup of Cognigy.AI Platform is straightforward. I just have to define the agent prompt, set up the model that I want to use, and then move forward to building the actual workflow. This is similar to what ElevenLabs does, so on that regard, they are the same.

Cognigy.AI Platform's pricing is expensive for the service that is offered.

In comparing Cognigy.AI Platform and ElevenLabs, the analysis section of ElevenLabs is different because it offers a different number of conversations, average duration, and total cost, all set up internally.

Pros for Cognigy.AI Platform include the easy setup and the possibility to build everything from scratch instead of predefining it as is done in ElevenLabs.

Pros for ElevenLabs include the agentic bot that I can speak to directly, which is not just doing everything within a flow. This is a feature that Cognigy.AI Platform is lacking. The language configuration within ElevenLabs gives a lot of freedom to target different markets.

The main benefits that Cognigy.AI Platform brings to the table are easy setup and the academy. Clients now rely more on Cognigy.AI Platform because ElevenLabs is relatively new to the market. If I can provide Cognigy.AI Platform to them, they will be very happy because they already know the platform well.

Given my experience with Cognigy.AI Platform and ElevenLabs, my advice for users looking into Cognigy.AI Platform who would like to start working with it is to check all the videos and get the certification first. It would be useful to complete this within a week because it is absolutely achievable. Then, do not be afraid to start being creative and setting up things so you can understand how Cognigy.AI Platform thinks, how it works, and what kind of freedom is available when using it.

I would rate this product a 7 out of 10.

My main use case for Cognigy.AI Platform is that I work for an airline and I create conversational assistants. A concrete example of how I use Cognigy.AI Platform to create these conversational assistants within the airline is that we have different agents connected to each other. I would like to add that they are ultimately connected to APIs and dump the data into the airline's systems.

The best features that Cognigy.AI Platform offers according to my experience are the ease of connecting with different external services and the ease of managing the different languages that the assistant can have.

Cognigy.AI Platform facilitates the management of multiple languages and integration with external services in cases where there is no artificial intelligence. Creating flows is very simple because you can change the language and you do not have to replicate the bot in different languages.

Cognigy.AI Platform positively impacts my organization by making it easier not to have to program everything in a programming language and to have an interface that allows you to create things without initially having knowledge of Cognigy.AI Platform.

I think Cognigy.AI Platform could improve by addressing the part related to agents and connection with different artificial intelligence providers. I would like to add that in the NLP part it needs to work better.

I think the part about agent management could be improved, as it is still starting out and you can see it needs to continue evolving. The NLP part also requires improvement.

I think Cognigy.AI Platform should improve the documentation as sometimes it is a bit scarce and you do not find solutions for what you are looking for.

I have been using this tool for almost three years.

I consider Cognigy.AI Platform to be stable.

The scalability of Cognigy.AI Platform needs to improve because there are times when you generate packages and they do not work the same way in different environments.

Cognigy.AI Platform's customer support is usually good since you open a public ticket and they respond quickly. You can even schedule meetings with support and see it in real time. I would rate customer support on a scale from one to ten as a seven.

I did not previously use another solution.

I have observed a return on investment as there are some cost savings since the virtual assistant resolves issues before they reach the contact center. A specific result related to this impact is that there has been a reduction in development time and we have also managed to reduce costs for the airline, which was the client.

Before choosing Cognigy.AI Platform, I know they did a product comparison, but that was not made transparent to us.

I would rate Cognigy.AI Platform an eight on a scale from one to ten. I give it a rating of eight because of the ease of use, the web interface, and the documentation aspect. My advice to other professionals considering using Cognigy.AI Platform is to take the training courses available on the website. My overall review rating for Cognigy.AI Platform is eight.

My main use case for Cognigy.AI Platform is building conversational AI agents. Regarding my main use case on Cognigy.AI Platform, we're also now looking into, or maybe mainly I'm looking into the domain also for integrating MCP within the conversational AI agents.

Cognigy.AI Platform has positively impacted my organization because, on the use cases itself, the AI-driven decisions are working quite well. It is better received than the NLU solution that was in place before, so I'd say the implementation itself was a definite win.

The best features that Cognigy.AI Platform offers include the flexibility of the different nodes provided in order to develop different domains, so you are independent of APIs, or you do not have to rely on specific APIs that are only available in Cognigy.AI Platform, but you can also work with other APIs and then get it integrated. I think that is the biggest advantage.

The flexibility of Cognigy.AI Platform has helped me in my projects because it's easier to integrate it with the in-house or on-premise APIs that clients have, which makes it easier. Of course, you have to consider the security layer there, but it's almost direct access you have to the resources, considering that you also take the security into account.

Since implementing Cognigy.AI Platform, the containment rate has improved. Most of the conversations are maintained within Cognigy.AI Platform itself, and unnecessary conversations such as intents that shouldn't be processed are taken quite well and then also accordingly responded to from the AI agent.

I believe Cognigy.AI Platform could be improved in terms of versioning of flows, for example, similar to GitHub, not only on the custom extensions that you have, but also on the flows itself. If there's some versioning possible, that would be helpful, and this is one of the bigger issues.

Regarding Cognigy.AI Platform's AI capabilities, I think security-wise there are some concerns because you cannot really protect the REST endpoints, and there are some open points on the security perspective and on the governance perspective. There are options for OAuth authentication, but for the REST endpoints, for example, this was a major topic.

I have been using Cognigy.AI Platform for around a year.

Cognigy.AI Platform is stable.

Regarding the scalability of Cognigy.AI Platform, there's a limitation on the rate limit. However, with the right vendor support, there are ways to avoid these kinds of issues.

The customer support for Cognigy.AI Platform is quite good.

I previously used a different solution before Cognigy.AI Platform, specifically Dialogflow from conversational agents from GCP. The reason we switched was that the vendor support was better with Cognigy.AI Platform than with conversational agents from GCP.

Before choosing Cognigy.AI Platform, I evaluated other options such as Dialogflow or conversational agents from GCP. This was one of the first choices because we initially had some experience there with that product.

My advice for others looking into using Cognigy.AI Platform is to complete the training that is offered from Cognigy.AI Platform itself to get an idea of how flow building and everything works, and to get a high-level overview. On a second level, use metaprompting to build your flows because a lot of the components can already be co-developed using metaprompting.

The development itself on Cognigy.AI Platform is quite self-explanatory. Via metaprompting, you get all the resources, and it's also easily configurable. The UI is quite friendly, but far from perfect. In terms of Cognigy.AI Platform's AI capabilities regarding its accuracy and reliability of output, it's quite good. It depends on the language model you use, but in general, all functionalities work quite well. At the end, it depends on the end-to-end testing and the quality of the data you have. On a scale of one to ten, I would rate Cognigy.AI Platform an eight.

My main use case for Cognigy.AI Platform is as a software engineer where I do the implementation and configuration of different CCaaS tools, with Cognigy being one of the tools I mainly use for designing flows, conversational AI designs, and IVR designs.

Cognigy.AI Platform offers best features that include designing flows, setting up conversational AI or IVR, and integrating with external tools, which stand out to me as very easy for any configurator or implementation person.

I find the ease of integrating Cognigy.AI Platform with external tools to be very smooth. The integrations with Cognigy.AI Platform or a third-party tool are straightforward, allowing connection and use of all the pre-built, predefined integration tools, making it very easy compared to other CCaaS tools.

Cognigy.AI Platform has positively impacted my organization by helping with the automation of tasks and providing pre-built libraries that assist configurators with no-code design, which outclasses other tools.

I have not come across any areas for improvement in Cognigy.AI Platform because it stands out as a tool that is easy to build or configure with low-code functionality compared to other tools.

I wish for better documentation for Cognigy.AI Platform. The articles and help articles would definitely assist any configurator or customer in understanding the features and functionality to make complete use of Cognigy.AI Platform's capabilities.

I have been using Cognigy.AI Platform for more than six months. I have gone through extensive training and completed my certifications.

Cognigy.AI Platform is stable in my experience.

Cognigy.AI Platform's scalability is adequate and can handle increased workloads or users easily.

I think customer support for Cognigy.AI Platform can be improved, but currently it is good.

I previously used Genesis before switching to Cognigy.AI Platform because of its low-code and no-code capabilities and its integration functionalities.

I am not directly involved in the purchase process of Cognigy.AI Platform through the AWS Marketplace. My company handles that, and I am the one who does the configuration and implementation.

A specific example of a project where I used Cognigy.AI Platform for conversation or IVR design is with an airline customer, where we designed flows based on zero-shot models. Whenever a customer reaches out to their website to understand ticket-related or luggage-related information and wants to make a payment for extra luggage, we configured the workflows in such a way that Cognigy.AI Platform is being used.

Since using Cognigy.AI Platform, I have seen specific outcomes like time saved, improved customer satisfaction, and reduced manual work, all of which are achievable because the tool is easy to understand and can be quickly learned to start building easily.

Since using Cognigy.AI Platform, I have observed a return on investment that includes performance-wise improvements, time saved, and cost-wise savings which are helping customers.

I am not much aware of the pricing and licensing features for Cognigy.AI Platform, but I would love to learn about them.

The evaluation for solutions was already done and we finalized our choice before I became involved in the decision.

Cognigy.AI Platform is a very easy tool to configure and understand, with no-code and low-code functionalities being standout features. I rate Cognigy.AI Platform a nine out of ten.

Regarding Cognigy.AI Platform's AI capabilities, I think it has good governance and compliance, though I have encountered other tools as well, and I feel it is good but there will always be room for improvement.

I have practically used Cognigy.AI Platform and seen how it can achieve better accuracy and reliability of output compared to other tools.

My advice for others looking into using Cognigy.AI Platform is to definitely choose it as it is a very good tool.

I have no additional thoughts about Cognigy.AI Platform. Everything is good, and I am happy to work more on Cognigy.AI Platform and make customers happy.