Coveo Reviews

My main use case for Coveo is optimizing UX on the site, specifically search results and pointing people to what we think they need and analyzing web traffic.

A quick specific example of how I use Coveo to optimize search results or analyze web traffic is that people often search for things but don't spell them correctly, and they might search for a word using a foreign term for the product, disease, or treatment. We try to reconcile those misspellings to what it's clear that they're looking for and point them to the right page.

We use the thesaurus a lot of the time, and we also do regular web traffic reports, whether it is on-site search or external searches.

Coveo has positively impacted my organization by cutting down the number of no result searches from something like 15% to about 5%, which is huge, saving calls to call center people and probably preventing people from seeking out products from our competitors, so we get people to pages that they want more quickly and more effectively.

I measure the reduction in no result searches through regular reports where we can see what searches ended up in no results, and we also do regular reports on what searches are made. Most searches are successful and they get to some kind of page, hopefully the correct one, but when we see that the number of no results is cut by two-thirds, that's a great reason to use the tool and keep using the tool.

In my opinion, the best features Coveo offers are that it's quick, it's easy to navigate, and if you have something you want to know, Coveo is very helpful in helping you find out how to get the information that you need and sort the information that you need.

Of those features, the main thing I find myself appreciating the most in my day-to-day work is being able to find what I need without spending a huge amount of time. The speed and easy navigation are important; there are other tools that do the same thing, but finding what I need quickly really matters.

I can't think of anything for how Coveo can be improved or anything that frustrates me or that I wish worked better.

I have been using Coveo for four years.

In my experience, Coveo is stable.

Coveo's scalability is very good; we have an enterprise website, and it handles what we throw at it very well.

Coveo's customer support is good.

I have seen a return on investment with fewer employees needed.

I purchased Coveo through the AWS marketplace.

My experience with pricing, setup cost, and licensing was fine; the prices were competitive and I was happy with everything.

I would rate Coveo overall a 10 out of 10.

I choose a 10 out of 10 because it is easy to use, can handle large data sets very quickly and effectively, and it is easy to learn how to do new things.

Regarding Coveo's AI capabilities, I have not used Coveo AI, but I think it's solid.

Coveo's AI accuracy and reliability of output have met my expectations.

My advice to others looking into using Coveo is to talk to Coveo itself; the people there are very helpful.

My overall review rating for Coveo is 10.

My main use case for Coveo is on one of my e-commerce websites where I have used it as a search platform that allows users to search for products, filter results, and view metadata and details. I have integrated Coveo into my web application.

A specific example of how Coveo helps users on my e-commerce site is that it has streamlined website searches significantly. We have a large catalog of products, and each customer is categorized by certain preferences. Coveo has helped us identify those preferences and show specific products to specific users. It is AI-powered, and it is remarkable how it works behind the scenes.

Coveo fits into our workflow as the core engine of our search, which makes our users' experience amazing by providing us with insights on what products are being sold more and what kind of customers in what regions understand or require what kind of products. Those minimalistic details are what Coveo has provided us with and improved our website interaction.

The best features Coveo offers include the AI-powered search that analyzes the region, the user, specific products, or preferences and needs, automatically sharing that information. Additionally, the search it provides is extremely fast and features an auto-search option that gives users basic details, images, and offers in the search field itself.

The AI-powered search actually helps our team by allowing users to search for products based on their preferences, with caching available that makes the search fast for users. This has increased our throughput time and the number of users engaged with our application, leading to more purchases.

The biggest feature I would highlight about Coveo is the AI enhancements and advancements they have made. Beyond that, the website search for products is faster, and we use it for some of our internal employee search applications as well, making it an amazing tool to utilize.

While Coveo is overall very fast, I find the development integration somewhat challenging. As the documentation states in detail, sometimes a few things do not work on mobile or on the desktop, and that could be improved.

I believe the mobile experience is something Coveo could enhance to provide greater support for mobile applications, as I see most features working fine on desktop but minimized for mobile.

I have been using Coveo for the last one and a half years.

Coveo is very stable in terms of providing and executing its intended functions, making it an amazing tool.

Coveo's scalability is exemplary as it is AI-driven and automated. We have never faced issues with scalability or maintainability, leading to a great experience utilizing this tool.

The customer support and technical team support has been amazing, providing assistance from the initial setup in our system and addressing all our questions.

I would rate customer support a perfect score as it is remarkable how they support and answer all queries, helping us through every setup and system understanding. It is a great tool to use.

Previously, we had our own on-premises systems built up, and we replaced them with Coveo, which has reduced our operational costs and increased performance and revenue.

We have seen a huge return on investment, as users visiting our site have increased and product purchases have risen. After the initial integration, the maintenance required is very low, allowing us to save both time and money.

I believe Coveo is a super solution for any e-commerce site or internal searching engines, doing a great job, as our manual efforts in creating data layers have been significantly reduced since implementing Coveo into our application.

Since Coveo was integrated, we have seen around a 25 percent increase in platform usage and a 40 percent increase in searches using the search bar.

I am not aware of the pricing, but the setup cost and the team's support have been very helpful in terms of initial support and addressing any queries we have.

We did not evaluate any other options before choosing Coveo; we replaced our own system setup directly with Coveo.

I would highly recommend Coveo as it is smart, fast, relevant, and especially suitable for big sites and large e-commerce setups. It is an enterprise-level solution that Coveo has provided, so it is definitely commendable from me and the team.

I would say it is an amazing tool—reliable, fast, and AI-driven—definitely modern and should be utilized in any e-commerce site, making it a great experience to use and implement. I would give this product a rating of 9 out of 10.

My main use case for Coveo is the search functionality that it provides on our website, which is heavily integrated with our content management system, enabling indexing of our content coming directly out of Sitecore, helping visitors discover content.

In the commercial real estate domain, we operate applications that allow listing agents or property agents to list properties for sale or lease. Once they create a property listing, they push out the data to the content management system, which immediately triggers Coveo APIs to index all the pushed content, populating the search UI that visitors use to search and discover listings and other content.

Coveo's primary usage is how it integrates with our marketing technology platform through its search functionality.

One of the best features that Coveo offers is the AI-enabled query suggestions feature, which looks at user query patterns and suggests relevant queries when users revisit the site, driving engagement.

AI-enabled query suggestions help users return more frequently and build trust in the search capabilities our site offers, allowing us to drive content towards users based on popular queries. Coveo offers a lot of analytics, including reporting on the most used queries and search relevance, which helped us consolidate various search hubs to improve end-user experience.

Coveo has helped build trust among users, although we still have a lot to improve for the overall search experience. We measure trust or improvement in user experience through engagement metrics, looking at website visit frequency, users' success in finding what they're looking for, click-through rates, and how those convert into leads.

Coveo could be improved by simplifying its configuration and setup, as it has a lot of dependencies on Coveo expertise, making it less intuitive.

I have been using Coveo for the last one and a half years.

Coveo is stable; the indexing is reliable, and we can see the fallouts after indexing runs.

I have not seen Coveo being scaled significantly during the last one and a half years, with only a few configurational tweaks made.

Customer support has been adequate; we do not involve them too much.

This is my first experience with a search platform, and I have not used a different solution previously.

In a general sense, we are not seeing a remarkable return on investment because maintaining Coveo for our search functionality is costly, and we have to balance investments in search versus other areas.

I rate it at six mainly due to the complexity of setting up Coveo independently and its considerable expense as a product.

Those were not things I was personally involved in, but I heard feedback about how expensive it was to use Coveo, particularly regarding API calls.

Coveo is deployed in a private cloud in our organization. I am not really sure specifically which cloud provider that is for our private cloud deployment. Anyone using Coveo should be aware that customizations will involve more work whenever changes or improvements are made. I would rate Coveo a six out of ten based on my experience.

Our main use case for Coveo is to retrieve data efficiently. Before using Coveo, we stored this data in Salesforce itself, which took longer. We migrated our database to Coveo.

A specific example of how we use Coveo to retrieve and store data is related to our website, which functions as a doctor's website. Previously, we stored all data related to our doctors' content in Salesforce directly. When a doctor searches for it on our website or opens our record page, content pages, or content landing pages, content loading time took around 300 milliseconds. While not a major difference, in terms of user experience, it matters. After migrating to Coveo, it took less than 10 milliseconds to load all this data. This is our main use case, and we also have other use cases, such as content recommendation to our doctors based on trending content and personalized content.

Regarding the content recommendation use case, Coveo helps us personalize content for doctors with an ML-related feature. We send every detail of the doctor's viewing of pages to Coveo directly with the help of Google Analytics. First, we send the data from Salesforce to GA once a doctor views any particular content, and GA sends all this data to Coveo to train our ML models. With the help of those ML models, we see what the trending content is currently. Our doctors are specialized in 12 to 13 different specialties. Based on their specialties, we recommend content directly on the dashboard pages and on the content detail pages.

The best features Coveo offers include content recommendation, which is one of the best features. Additionally, the retrieval of data takes less time. It has moved from 300 milliseconds from our Salesforce data retrieval to less than 10 milliseconds in our Coveo retrieval. This is one of the other best features. The best thing is that we do not need to make an API callout for every communication between Salesforce and Coveo. I believe we are using headless communication, which is considerably faster. These are the best features I have observed.

Coveo has positively impacted my organization primarily by reducing the waiting time from 300 milliseconds to 10 milliseconds. Coveo also provides us very high data storage. We store our Salesforce records directly into Coveo as well. It syncs, and it takes around one hour to have a sync between Salesforce and Coveo.

Besides the improved speed and data storage, I have noticed increased productivity. Productivity has increased. The improvement is good, but I do not have the exact metrics of those details.

Coveo almost fulfills all my use cases, so I do not have any other recommendations or suggestions for Coveo.

One other suggestion is that as of now, the sync between our Salesforce and Coveo is one hour. I know we can make it real-time also. I do not know much about it, but I think it costs higher if we make it real-time. I would say that could be one of the improved features. It can give us more satisfaction if it is a real-time sync.

I have been using Coveo for a year.

Support is very good with Coveo.

It was great working with a product such as Coveo and integrating that product with our Salesforce. The experience has been good. I would rate this review a 9.

I used Coveo for content indexing and content search from AEM, and I have used it for multiple languages. The content was available in English language and Chinese language, so our requirement was to index the content and then search the content from Coveo.

Coveo has a push API which indexes the content with a certain structure and JSON payload for the page properties that exist in AEM pages. For example, a page title, page description, page tags, and metadata properties from the page are indexed in Coveo with the help of Coveo push API. There are certain request headers such as UTF-8 encoding. We wrote an HTTP client which makes calls to Coveo push API to index the content. To search the content, we used the Coveo endpoint and the page ID as the path, basically the page path as a resource to pull the content from Coveo.

We used Coveo for multiple languages with English language content and Chinese language content. We faced challenges initially because we had not added UTF encoding in the header, so the Chinese content was not properly indexed and instead showed question marks. We added the header with UTF-8 encoding, and after invoking the same API, we were successfully able to index the Chinese content. We fixed this issue and successfully indexed and fetched the Chinese content from Coveo.

Coveo is an enterprise-grade search and index platform that can be integrated with any CMS platform; in our case, it was Adobe Experience Manager. Since it is API-based, Coveo can also be used in other CMS platforms.

We did not face challenges in the integration process with Adobe Experience Manager specifically. We went to Coveo platform, reviewed the API integration methods, and learned how to pass the key and authentication methods in the API. We configured the OSGi configurations, which is typically how AEM supports any kind of configurations. We used the OSGi configurations in AEM and configured these API endpoints and the authentication methods, OAuth tokens, and other security measures. We invoked that API in our code base; in our code, we have OSGi services and Sling Servlets which are invoking Coveo's APIs.

Coveo is scalable and I have not seen any latency issue. The content immediately reflects the moment you index it, and in a couple of minutes, it is reflecting on the Coveo server. It is easy to use, easy to navigate, and you can easily find your content which is indexed on Coveo platform. Coveo is a specialized platform in the field of indexing and searching and is a known search platform. We seamlessly integrated and benefited, especially because indexing the content was taking too much time when we were indexing to other platforms such as Solr. However, in Coveo, we have not faced any latency issue and we were quickly able to index the content and search the content.

Coveo is easy to integrate and it hardly takes a few lines of code that we need to write. There are not many complex configurations we have to write for integration. A small configuration for the API endpoint, the API payload, and the authentication methods allows us to write a few lines of code for an HTTP client to invoke these APIs. It is easy to integrate and we can easily maintain it. If there are any changes in the future, we can easily make these changes.

Coveo can improve by supporting a few more languages. For example, we have tested with English and Chinese and a bit of French. I think Coveo can come up with indexing and searching with non-native languages apart from these known languages.

When we have to scroll down to search for particular pages, maybe they can provide easier navigation and links to find content, either with the help of page paths or another method. We have seen that if we have multiple pages or many pages published to Coveo to index, then it is time-consuming to scroll down and see the content, especially if it is indexed properly.

I have used Coveo for more than two years.

Coveo is deployed in our organization on a public cloud with API integrations and authentication methods, and we call this publicly hosted Coveo platform on the cloud.

We have used AWS, if I remember correctly, because it was a long time ago, almost more than two to three years back now.

There was a separate team actually accessing Coveo console and they were aware of that; we were using those APIs and the configurations as a vendor.

If others are looking into using Coveo, I would advise that if they have a use case for scalable solutions for enterprise-wide search capability or indexing content, then Coveo is the right choice.

I would rate Coveo a seven out of ten.

My main use case for Coveo involves working on SAP Commerce technology where we built an e-commerce solution for one of our clients who faced issues while performing smart product searches on the site. Coveo provides AI-driven search results and proper recommendations based on customer interaction and context, which is very beneficial for us because it improves our order conversion rate, allowing customers to find the exact products they want and see similar products, greatly increasing upselling.

A specific example of how Coveo helped solve the smart search issue for our client is that it offers personalized recommendations based on customer interaction rather than global recommendations. We send an event to Coveo, and based on that event for a particular user, Coveo sends personalized recommendations, which is crucial as customers searching for products receive recommendations based on what other similar users have purchased. Coveo's flexibility allows us to create recommendations based on strategies like recently viewed or most purchased products, and it integrates easily with SAP Commerce, making it straightforward for us to index all products in Coveo, which uses a machine learning model. We can configure a search result via a query configuration pipeline, which adds to the flexibility and intuitiveness of using Coveo as it continually updates with new features like real-time facet management that requires no technical knowledge to implement.

Coveo greatly helps our customers, as we use it not only for product search but also for content search. Its indexing capabilities are versatile, utilizing various approaches like pull or push methods, and its AI model effectively responds to user interactions. Coveo enables us to configure all aspects of the search page, from facet ordering and priorities to sorting criteria, which can be done easily through the customer merchandising hub, enhancing workflow efficiency and increasing customer satisfaction as users receive relevant search results effortlessly.

The best features Coveo offers include how easily a customer or client can manage their products. We indexed around 70,000 products in Coveo, creating a tailored pipeline for search, listing, and recommendations, which grants us full control over what is displayed in search results. Additionally, all customer interactions are captured through events using either out-of-the-box libraries or custom events, allowing Coveo to provide personalized search results that significantly improve user experience. Even on the order confirmation page, Coveo suggests complementary products based on order history, facilitating upselling and addressing customers' needs seamlessly.

We use Coveo's search pipelines and event tracking features, which stand out due to their unique query pipeline structure that allows customization of search results. Coveo typically provides three pipelines: search query, listing query, and recommendation query pipelines. This structure enables us to implement synonyms and keyword redirection while managing filtering logic to enhance user searches and provide a seamless experience. Coveo also supports pre- and post-conversion processes during indexing, giving us the ability to tailor the search results effectively and allowing clients to boost or bury specific products easily. For event tracking, Coveo offers smooth integration with Google Tag Manager to capture ongoing events effortlessly, and their support for SAP Commerce integration streamlines the overall process.

Coveo is recognized as a trusted partner with SAP, and its effective indexing process coupled with multiple integration sources allows us to enhance the customer experience continuously while offering insightful analytics on user behavior that inform our strategies to improve product relevancy and conversion rates.

One area of improvement for Coveo is the size limitation concerning response results. Specifically, when grouping various variants under a parent product, the number of variations that can be displayed is restricted, which we mitigate through infinite loading techniques. Enhancing the response size capacity would be beneficial, although the newly introduced facet manager improves the configuration process, its limitation of 30 pin facets is somewhat restrictive, although it is a manageable performance consideration.

While the documentation is mostly adequate, I believe it can be improved further. Certain valuable insights provided by the PS team are not always captured in the documentation, which can be a hurdle for new users during the integration process.

I have been using Coveo for the past one year and above.

Coveo is stable with minimal downtime in my experience. While occasional issues arise, their excellent support team resolves them quickly based on priority, ensuring smooth operations.

Coveo's scalability is adept, allowing for growth aligned with the chosen plan and tier. With the ability to upgrade, higher tiers can support increased processing needs, reducing indexing time as product volumes grow.

Customer support is fantastic. I appreciate how they engage with us by asking the right questions and providing timely resolutions and updates, ensuring excellent overall support from both customer service and the PS team.

Previously, we used Solr, a standard SAP Commerce feature, but we switched to Coveo because of its limitations, which SAP itself recommended we overcome by integrating Coveo for a more effective solution.

We see an increase in conversion rates due to Coveo, but the primary benefit is the analytics it provides, enabling our clients to understand customer behavior better. They can now monitor events and identify profitable opportunities by accessing detailed metrics through the dashboard, ensuring they have clear visibility into their performance with ongoing support from Coveo's team.

Coveo is deployed in our organization through their provision of dedicated non-production and production tenants, where we utilize various sources for different environments, including QA, development, and production, ensuring smooth operational processes across the board.

We have seen a return on investment through Coveo, reflected in measurable growth in both product and content searches, which allows customers to easily find relevant information and products, enhancing their experience and providing valuable insights for our clients on customer behavior and conversion metrics.

Regarding pricing, Coveo's dictionary feature allows us to control prices dynamically according to the customer, location, or store, making it adaptable to varying business needs. We also utilize the dictionary for stock availability. Setup costs and licensing entail a fixed number of hours for PS team support during integration, alongside a plan based on API call volumes.

Coveo positively impacts our organization by resolving the personalization issue we faced with our previous solution, Solr, by enabling customers to find the right product at the right time based on their interests.

Coveo is stable with minimal downtime. While occasional issues arise, their excellent support team resolves them quickly based on priority, ensuring smooth operations.

Coveo's scalability is adept, allowing for growth aligned with the chosen plan and tier. With the ability to upgrade, higher tiers can support increased processing needs, reducing indexing time as product volumes grow.

One area of improvement for Coveo is the size limitation concerning response results. Specifically, when grouping various variants under a parent product, the number of variations that can be displayed is restricted, which we mitigate through infinite loading techniques. Enhancing the response size capacity would be beneficial, although the newly introduced facet manager improves the configuration process, its limitation of 30 pin facets is somewhat restrictive, although it is a manageable performance consideration.

While the documentation is mostly adequate, I believe it can be improved further. Certain valuable insights provided by the PS team are not always captured in the documentation, which can be a hurdle for new users during the integration process.

My advice for those considering Coveo is to choose it if they are looking for enhanced, personalized search results that can significantly improve user satisfaction. I rated this review an 8 out of 10.

Coveo's main use case revolves around content indexing and search integration, primarily when working with Sitecore as a CMS implementation.

In our Sitecore CMS implementation, we have very large content volumes, and Coveo helped us significantly with relevance through the use of indexing, query pipelines, and security features. Relevance was the most valuable feature for us. We were able to determine what content was shown to users and which was not, so Coveo provided substantial value from a relevance perspective.

From a business perspective, Coveo's biggest value is having more control over how users discover content, including relevance, ranking, personalization, and analytics, which can help improve the search experience without requiring a developer to change application code for every small adjustment. The exact value obviously depends on how well the organization configures and uses those capabilities.

Before moving to Coveo, we had metrics of user clicks around two million, but after we went live with Coveo, user clicks and all view analytics jumped up over 3.5 million, which was a significant upgrade after implementing Coveo.

In my view, the best feature that Coveo offers is relevance, which is one of the strongest areas. You have more control than you would typically get with a basic search implementation, particularly through query pipelines and ranking configuration. The important thing is that good relevance still requires proper configuration and understanding of your content, so I would not expect the platform to solve every relevance problem automatically.

I find query pipelines particularly useful because they give you a way to influence search behavior based on the context of the query without having to pull that logic into your application. For example, you can apply different ranking on query behavior depending on the situation, which provides a good separation between the application and search configuration.

Recently, I have been exposed to working with Coveo Atomic while working on the headless Sitecore implementation, and I appreciate the Atomic library because it provides reusable search components, so you don't have to build every part of the search interface from scratch. It makes the front-end integration faster while still allowing the developer to customize the experience when needed.

The main area I would improve is the learning curve because there are many concepts and configuration options, and understanding how indexing, query pipelines, security, and all those pieces work together requires experience. Coveo's onboarding experience could be simpler, especially for developers using the platform for the first time, and I believe that improvement on the documentation is also needed because while Coveo has extensive documentation, it is sometimes difficult to find exactly what you need.

As a developer, when trying to find something regarding how to do indexing, I have to read the documentation, but if there were screenshots showing how it can be done, it would make a developer's life simpler. Additionally, including code examples would greatly enhance the experience for developers.

I have been using Coveo mainly in enterprise search implementation, particularly in Sitecore environments, so I have been working with Coveo for the last five to six years.

From an enterprise perspective, scalability is one reason we consider a platform Coveo because we don't have to build and maintain the underlying search infrastructure ourselves, and the platform is designed for large content environments. My direct experience is more focused on implementation and integration, but I haven't seen scalability become a major limitation.

I recommend starting with search requirements rather than starting with product features. Understand your content sources, security model, and relevance.

So far, I haven't touched Coveo's AI-powered solutions, so I may not be able to comment on that because my experience is mostly with integrating Coveo, but not with the AI-powered solution models. I haven't used those AI features much, so I cannot provide insight on the accuracy and reliability of output.

I would rate Coveo an eight out of ten. I choose this rating because the platform is powerful, has strong relevance, and enterprise capabilities, but I deduct a couple of points mainly due to the learning curve and some areas that can take time to understand when implementing Coveo for the first time.

My main use case for Coveo is that I use it as a search engine for my websites.

A quick specific example of how I use Coveo as a search engine for my websites is that we mostly used it for article pages; for example, if we need to sort a latest article, we send the entire article data to Coveo, which filters it for us and gives the exact result based on the rules we provided for Coveo. We have N number of customizations and rules for Coveo, so based on these rules, Coveo sorts out the data.

Coveo provides a search engine experience that is superior when compared to the other search engines available in the market as of now.

The best features Coveo offers include customization; for example, if you are working on regional search and you have an issue with needing to customize the date format for Coveo results for one particular regional site, you can customize it using date formats in Coveo. These kinds of customizations are not offered in other search engines in the market, which is where Coveo provides more customization features.

Coveo has positively impacted my organization by helping us achieve client requirements in a better way.

Coveo can improve based on the rules we have provided in our CMS platform; in my case, we are using a CMS platform integrated with Coveo, so the rules we are providing, called Coveo rules, generate the data.

I chose eight out of ten because we have some limitations, such as when we share heavy data, where a large number of data takes more time for Coveo to process; that is one area where Coveo needs to improve, but apart from that, it is good.

Coveo is already doing well, and I have no additional suggestions at this time, but if there are any in the future, I will definitely let you know.

I have used Coveo for more than two years.

I have already mentioned that the customer support is very good; when we reach out to the customer support team, the way they answer our questions and follow up is really well, so on a scale of ten, I will definitely give ten for this customer experience.

Before Coveo, we usually used an alternative solution.

From the perspective of pricing, setup cost, and licensing, you can use their help to set up Coveo, and I did not face any issues while setting it up.

I have seen a return on investment; it is a one-time implementation once we set it up, and we do not need employees to work on Coveo as we can automate it, so overall, it is good.

Before choosing Coveo, we evaluated other options; we usually used the Sitecore search engine before Coveo, but compared to Sitecore, Coveo is working well and giving better results, which is why we switched.

My advice for others looking into using Coveo is that I would definitely suggest them to go with Coveo; if they are looking for any search engine, I will definitely recommend Coveo.

Coveo is a good search engine when compared to others.

Coveo is performing well, and I have no additional thoughts before we wrap up. I would give Coveo an overall rating of eight out of ten.

My main use case for Coveo is that we are using it for search as well as recommendation perspective. We are trying to index our content pages, products, and then we are utilizing that on our product listing pages for recommendation, search, and then auto-suggest, everything that we have in our e-commerce site.

A specific example of how Coveo is used on one of our product listing pages is that we have products that we are currently indexing in Coveo. After indexing, we are trying to display those product attributes and everything in our search box. We put some search text over there and then it is returning a full list of products that we have. Apart from that for our content pages as well, we are trying to search some newsletter or something and then it is returning those results in our listing pages.

Coveo is currently offering exceptional flexibility, integrating easily with any enterprise stack. Its user-friendly interface ensures a low learning curve for functional teams, while its native AI/ML engine delivers superior search and recommendations. With Coveo, business users can independently manage search rules to drive better customer outcomes with minimum manual effort.

The best features Coveo offers, in my opinion, include seamless integration with any enterprise application and its intuitive UI, which allows even non-technical users to become proficient quickly. Its built-in AI/ML capabilities significantly enhance recommendations and the overall search experience. Furthermore, business users can easily fine-tune search rules with minimal effort to optimize results.

Customers are satisfied with the results that we are seeing on our listing page as well as search pages. What we are seeing is that our sales have significantly increased by twenty to thirty percent just within one year of using Coveo. We measure that sales increase based on data tracked by our complete marketing team, and we have an SEO team as well. They are specialized in looking at this data and they give us these metrics, and then we are able to know about what is going on and how efficient it is.

Coveo is currently providing all the features that we need, but the AI/ML specifications that they have can be optimized in a better way so that it will help us with more features apart from recommendations. I would give Coveo a nine out of ten because the response and everything is fine, but the technical team of Coveo helps us, yet I think it is not one hundred percent. It lacks something in small features, but it can be better.

I have been using Coveo for more than one year now.

Coveo is quite stable.

As far as I know, Coveo's scalability is quite good. Mostly their team does that for us. We have to raise a request, and they do that on the basis of our requirement.

I would like to add that their support team is awesome in responding to the technical queries we faced during integration. I would give Coveo five out of five for customer support.

We were earlier using Solr, which had limited capability. The UI was not good at all and it was a little bit cumbersome to use, so that is why we moved to Coveo.

I have seen a return on investment in terms of time saved. From a money perspective, we have not come across all the metrics as of now. We will spend some more time. We have a contract for two years, and after completing that time, maybe we will be able to figure that out.

My experience with pricing, setup cost, and licensing is that these things are mostly managed by our senior teams. We do not have much idea on what cost we are paying to them. It is not much of my concern as a senior software developer about the cost.

Before choosing Coveo, there were other search engines apart from Coveo that we evaluated, such as Elasticsearch, but ultimately with the AI/ML capabilities and other features that Coveo has, we finalized Coveo as a final solution.

From my experience, I feel Coveo is one of the best products that I have used in the search and recommendation field, and I am satisfied with the product.

My advice to others looking into using Coveo is that they should switch to it. It is quite customer-friendly. Functional employees would also get a benefit from that, and even the AI/ML capability that they have will definitely help them.

Overall, I think Coveo is a nice product, and everyone should try it once and see the benefits that it is providing. I give this review a rating of nine out of ten.

One of my main use cases for Coveo is enterprise knowledge search and contextual content retrieval. For example, in one project, we had information spread across sources like SharePoint, CRM systems, and internal documents, and we used Coveo to index that content and provide a unified search experience. We configured the indexing pipeline to normalize and enrich the content, and then used query pipeline and relevance tuning to make sure the most relevant documents were surfaced. On top of that, we have integrated the search capability into an AI-powered assistant, so when a user asks something like, 'What is the latest account summary for this customer?', the application retrieves the relevant Coveo content and uses it as context for the AI response. In simple terms, Coveo handles the enterprise search and relevance layer, while the AI assistant uses those results to provide a contextual answer.

Overall, the process of integrating Coveo search with my AI-powered assistant was fairly smooth, but there were a few challenges, mainly around making Coveo results reliable enough for the AI layer. The integration itself was straightforward; we used Coveo APIs to execute searches, retrieve relevant documents, and pass the results as context to the AI assistant. The bigger challenge was relevance and context quality. Sometimes the search returned technically relevant documents, but not necessarily the exact information the user was looking for. We addressed that through query pipeline configuration, query extensions, filtering, metadata enrichment, and relevance tuning. Another challenge was controlling the amount of content sent to the LLM, as we did not want to pass a large number of documents blindly, so we applied result filtering and ranking before sending the context to the AI. The API integration was relatively smooth; however, getting the search results to be strictly accurate and AI-ready required more tuning and testing.

I would add that I see Coveo as the retrieval and relevance layer rather than just a search box. In the AI assistant integration, the quality of the final answer depends heavily on the quality of the content retrieved from Coveo. We paid particular attention to metadata, security permissions, relevance tuning, and grounding the AI response in the retrieved content. That also made monitoring important, as we validated both sides independently—whether Coveo was returning the right results and whether the AI was using those results correctly. Overall, the integration worked well, but the key learning was that good AI responses start with good enterprise search and well-structured content.

The biggest positive impact Coveo has made on my organization is reducing the time people spend manually searching across different enterprise systems. In one of our implementations, we brought content from multiple sources into a unified Coveo search experience and then exposed those results through an AI assistant. This reduced the need for users to manually go through SharePoint, CRM records, and internal documentation. From a business perspective, we saw faster information retrieval and reduced manual effort, particularly for teams that frequently work with customer and account information.

In one of the AI search implementations I worked on, we measured roughly forty to sixty hours of manual effort saved per week across the team by automating the retrieval and processing of information that previously required people to search multiple sources manually.

In my experience, a few Coveo features stand out, with the first being relevance tuning—the ability to control how results are ranked through query pipelines, ranking expressions, query extensions, and ML-based relevance, which is very useful for enterprise search. The second is the broad range of connectors and indexing capabilities, as being able to bring content from systems like SharePoint, Salesforce, websites, and other enterprise sources into a unified index is a major advantage. The third is Coveo Machine Learning; features such as automatic relevance tuning and recommendation models can improve the experience without having to manually configure every ranking scenario. Finally, I find the APIs and Headless framework particularly useful because they allow us to integrate Coveo into custom applications rather than being limited to Coveo's out-of-the-box UI. The combination of enterprise content ingestion, strong relevance capabilities, ML, and developer flexibility is what I find the most valuable.

If I had to pick one, I would say relevance tuning has been the most impactful feature for my projects. In enterprise search, simply indexing a large amount of data or content is not enough, as users need the right result at the top, and what is considered relevant can vary significantly by business context. For example, we had a scenario where multiple documents could match a customer or account query, but the user needed the latest and most contextually relevant document rather than just a keyword match. We used query pipelines, ranking expressions, metadata, and ML-based relevance capabilities to improve how those results were ordered. The biggest benefit was that we could continuously tune the search experience based on actual user behavior and search analytics rather than hard-coding every possible scenario. Connectors solve the 'How do we get the content into Coveo?' problem, while relevance tuning helps solve the most important 'How do we get the right content in front of the user?' problem.

Coveo's full value really comes from how these features work together. For example, connectors bring the enterprise content into the index, indexing pipelines enrich and structure the content, relevance and ML determine what should surface, and the APIs allow us to integrate the experience into custom applications or AI assistants. I would also highlight security and permissions; in enterprise environments, it is not enough to return relevant content. We need to make sure users only see content they are authorized to access. The combination of relevance, enterprise security, content ingestion, and developer flexibility is what makes Coveo particularly useful for large-scale enterprise search.

In my experience, Coveo's governance and security are particularly important strengths for enterprise AI use cases, especially because Coveo can operate on sensitive enterprise content. The key aspect for me is permission-aware retrieval, as you do not want the AI assistant to retrieve information that the requesting user is not authorized to access. Coveo's security model and permission handling help maintain those access boundaries.

From a developer's perspective, there are a few areas where Coveo could be improved. The first is configuration and troubleshooting; Coveo is very powerful, but for someone new to the platform, understanding why a particular document is not indexed or why a result is ranked a certain way can take time. More guided diagnostics and clearer error explanations would help. Second, the developer experience could be more streamlined. Having a more unified experience across APIs, Headless, query pipelines, and ML configuration would reduce the learning curve.

While Coveo's documentation is generally good, it can sometimes be difficult to find the exact guidance for a specific implementation scenario. For example, when troubleshooting indexing or relevance issues, it would be helpful to have more end-to-end scenario-based examples rather than having to piece information together from different documentation sections.

The main reason I rate it an eight out of ten is that while Coveo is very capable, there is still some room for improvement regarding the developer experience. The biggest gaps for me are troubleshooting and observability, documentation discoverability, configuration complexity, and AI or RAG workflows.

One additional area I would mention regarding improvements needed is cost visibility and optimization.

I have been using Coveo for around two years, primarily gaining hands-on experience that includes Coveo indexing data sources, indexing pipeline, query pipelines, relevance tuning, Coveo ML, and API integrations. I have also worked on troubleshooting issues around indexing failures, search relevance, query behavior, and integrating Coveo search into enterprise applications.

In my experience, Coveo has been stable for our enterprise workloads, as we have not experienced major stability issues with the core search service. Most of the issues we have encountered have been more related to indexing configuration, data source connectivity, permissions, or query configuration rather than Coveo itself.

From my experience, Coveo has been quite scalable for our enterprise search, as we have used it with multiple data sources and a growing volume of indexed content while supporting concurrent users and API-based search from our backends. One thing I like about the SaaS model is that we do not have to manage the underlying search infrastructure ourselves, allowing us to scale the application and integration layer independently while Coveo handles the search infrastructure.

Coveo scales well for enterprise search, and we have not hit a fundamental platform limitation as indexed content and usage grew. The areas we pay attention to as usage increases are indexing volume, query API traffic, connector throughput, and relevance performance, which can require architectural and configurational adjustments.

Overall, my experience with Coveo's customer support has been positive; for technical indexing, query pipelines, or API behavior, the support team has generally been helpful in narrowing down the root cause. The main area I would improve is speed and depth for more complex issues, as some problems require multiple rounds of investigation, particularly when they involve a combination of indexing, permissions, and relevance configuration. I also think having more self-service diagnostic tools and scenario-specific documentation would reduce the need to raise support tickets in the first place. Overall, support is reliable, but there is room to make troubleshooting faster and more self-service oriented. I would rate Coveo's customer support an eight out of ten.

Before using Coveo, we relied more on native search capabilities across individual systems, such as SharePoint and CRM search, rather than having a unified enterprise search layer. The main challenge was that information was fragmented across multiple systems, requiring users to search each source separately, and the relevance and ranking were different from one system to another. We moved toward Coveo because we wanted a centralized search and relevance layer that could bring those sources together, apply consistent relevance and service controls, and expose the results through APIs for our AI assistant. The main driver was not that the previous tools were inadequate individually; it was the need for a unified enterprise search, better relevance, and easier integration with our AI experience.

The clearest ROI metric from our implementation was the time saved rather than a direct headcount reduction. By combining Coveo search with an AI assistant and workflow automation, we estimated roughly forty to sixty hours of manual effort saved per week across the team, as previously, users had to search multiple sources and manually consolidate all the data.

From my experience, the setup process itself was fairly straightforward, but the pricing and licensing can be more complex because they depend on factors such as usage, data volume, features, and specific enterprise agreements. I was not directly responsible for negotiating Coveo's contract, so I cannot provide a specific dollar figure for my organization.

We looked at a few approaches before choosing Coveo, including native search capabilities from platforms like SharePoint and Salesforce, as well as building a more custom search solution using APIs and search engines such as Elasticsearch or OpenSearch.

Coveo's accuracy is strong when configured properly, but I would not consider the output automatically reliable just because Coveo is involved.

In our organization, Coveo is primarily used as a cloud-based enterprise search and retrieval service rather than something we deploy directly on our own infrastructure.

For Coveo itself, we classify our consumption as a SaaS platform, so we do not directly manage or select the underlying cloud infrastructure where Coveo's service runs.

My main advice for others looking into using Coveo would be to start with a clearly defined business use case and measurable success criteria rather than starting with the technology. I recommend identifying key data sources and security models early, investing time in relevance tuning, prototyping with real user queries and real enterprise data, and planning for AI integration from the beginning if you are going to use Coveo for RAG or AI assistant. You also have to define metrics upfront. I rate Coveo an eight out of ten overall.