Kyvos Semantic Layer Reviews

We were running into limitations with TM1 as data volume and reporting needs increased. Supporting more detailed analysis often meant reworking models or simplifying requirements. With Kyvos, we have been able to work with different types of hierarchies, including nested and parent-child relationships. Also, Kyvos data model can handle changes over time without constantly restructuring.

We were using TM1 earlier and one thing I noticed after moving to Kyvos is how much easier it is to work with larger datasets. With Kyvos semantic layer, reports stay responsive even as data size and complexity increase.

Transitioning from an existing TM1 setup required some planning, especially around how models were structured earlier. Once that was sorted, it has been fairly smooth.

We wanted to accelerate query response time on Power BI, and Kyvos helped us achieve this without any disruption to our data analytics stack. It has a named connector for Power BI, which is really convenient.

Kyvos fit right into our existing analytics ecosystem - no architecture overhaul or expensive data transfers were needed. We can now analyze massive volumes of data much faster without worrying about integration hassles.

It’s been a great experience using Kyvos so far. No negatives yet.

Different interpretations and handling of data made it difficult to ensure consistency. With Kyvos, that variability is reduced.

We’re responsible for ensuring that data is used correctly across teams. Kyvos semantic layer standardizes how things are consumed which reduces the risk of inconsistencies showing up in reports.

It felt a bit different at first compared to how we were working earlier, but it became easier once we got used to it.

Our analytics setup used to struggle with the increasing data volumes. We kept hitting scale limits that led to slow reporting and also prevented deeper analysis. Kyvos centralized and optimized our data which allowed us to run long‑term trend analysis without technical limits.

One of the biggest wins for us has been the ability to work with several years of customer data without performance slowdowns. Earlier, running period-over-period comparisons would either take forever or require us to simplify the analysis. With Kyvos, the same queries return results quickly, even when we’re slicing data in different ways.

It’s been a very smooth experience so far with Kyvos

Earlier, a lot of time went into preparing datasets before analysis could even begin. With Kyvos semantic layer, that initial friction is significantly reduced. Our workflow is more efficient now.

We work with high-volume data that involves customer behavior, product interactions, time-based trends and we need to explore patterns to make important decisions. Kyvos allows us to move across multiple dimensions, drill into combinations and test different hypotheses.

At first, it feels like you’re working within a defined structure rather than shaping the data freely for every analysis. Over time, that actually speeds things up, because you are not redoing the same preparation steps again and again for similar use cases.

With Kyvos, we’ve been able to map purchase data with each customer’s demographic profile more effectively. We can quickly analyze buying patterns over different time periods and understand customers’ needs better.

Kyvos helps connect the dots across the purchase journey and behavior of each customer. We can easily analyze customer data coming in from multiple channels and get up to date insights that help our teams deliver more relevant offers.

So far our experience with Kyvos has been relatively smooth. Been using the product for a few months without any major hiccups.

Earlier, we had to manually validate AI responses before sharing them. That slowed down adoption and limited usage. With Kyvos, teams are comfortable using AI outputs directly.

What stood out to me is how with Kyvos semantic layer, AI responses feel more aligned with business contexts. Instead of just pulling from raw tables, answers reflect actual metrics that we already use. Kyvos also fits well with existing workflows, since teams don’t have to learn a new way of asking questions.

The initial setup requires coordination across teams, but after that we haven’t faced any issues.

With Kyvos’ LangChain framework, it’s easy for our team to embed analytics into GenAI apps. Their built-in toolkit helps us seamlessly connect to all our data and build apps that deliver powerful insights.

Kyvos’ connectivity with LangChain frameworks makes it easy for our AI team to embed analytics into Gen AI applications. We use their built-in toolkit to connect to all our data and build AI apps that generate powerful insights.

No major concerns. We have been able to scale usage without performance or quality issues.

Earlier, building and updating financial models was time-consuming, especially when dealing with multiple dimensions and dependencies. That made scenario analysis slower and harder to iterate on. With Kyvos, we can work with the same complexity more easily, which has reduced effort and made planning cycles smoother.

We deal with complex financial data with multiple levels and dependencies and earlier it took a lot of effort to model and maintain that structure. With Kyvos semantic layer, it’s easier to handle those layers without breaking things into smaller pieces.

There’s a bit of a learning curve initially when working with more complex models, but it becomes manageable with use.

Earlier, getting campaign insights often involved going back and forth for updated reports. Kyvos has helped us react faster, make quicker adjustments and spend more time improving campaigns.

We track campaign performance across channels and the questions keep changing as campaigns evolve. Kyvos semantic layer has helped us to quickly move from a high-level view to detailed insights. This is especially helpful when campaigns are running live.

It took a little time to get comfortable with it, but after that it’s been straightforward.

Our main challenge was dashboard responsiveness. Users often avoided drilling into data because it took too long and we ended up creating multiple summary reports as a workaround. With Kyvos, teams can explore data more freely without running into performance issues.

We started using Kyvos semantic layer mainly because our dashboards were getting slower as data volume increased. Some of our reports involve detailed drilldowns and performance would drop when users tried to explore beyond summary levels. After moving to Kyvos, those same dashboards feel noticeably faster and more interactive.

Another thing I have seen is that performance stays stable even during peak usage. Earlier, mornings were especially slow when multiple teams accessed reports together. That’s no longer an issue and users can interact with dashboards without waiting between clicks. We also didn’t have to change our BI layer, which made adoption easier for the business.

No real complaints at this point. It’s been working as expected for our use case.

We have a lot of very large and complex data sets to look at, and typical BI tools limit our ability to look into the data quickly. We needed a solution to look for insight through conversational analytics that was scalable and that had a good relationship with our business context. Kyvos was by far the best option available after looking through the other choices made available to us. The results have been that our teams have been able to get much quicker insight with greater accuracy through asking questions in the way we would normally ask other people.

The way that Kyvos allows our teams to interact with data is incredible in how simple it is to just ask a question in plain and simple terms, without having to create a lot of detailed reports. The ability to ask follow-up questions while still in the same discussion without having to re-create the earlier context is huge and helps us hurry up how long we would spend creating these types of conversations.

I think it would be beneficial for more very specific examples of conversational analytics scenarios done as demonstrations to be available to view. Other than that, the process of using Kyvos has been very smooth.

As our organizational data runs into petabytes, we were finding it hard to train our AI agents on the complete data. This was creating blind spots and impacting contextual understanding and accuracy. Kyvos has helped us address this problem and we have been able to ship production-grade AI applications much faster.

We can plug our LLMs and AI agents seamlessly into Kyvos semantic layer (instead of connecting them to multiple raw data sources). It gives AI access to all our enterprise data with standardized business logic and definitions. This helps reduce hallucinations and improve accuracy of responses.

Slight learning curve for some of the advanced features, but once we got a hang of them things were fine.

Slow dashboard responses were a common complaint before Kyvos, specifically at peak times. This was limiting our ability to make time-critical decisions using BI. Now, it is easy to process large volumes of data and we're able to provide our stakeholders with critical insights faster than ever.

The biggest impact for us has been speed. Our data is on Snowflake, and in the past, we saw complex queries slowing things down particularly when lots of users queried the data. After implementing Kyvos, the difference was immediate. Even heavy, multi-dimensional queries return results in seconds.

So far, we haven’t faced any critical issues.

The overall experience has been great. We have been using Kyvos to run complex analytics use cases and get faster query responses on Looker. It connects directly to all our data sources and the dashboards get refreshed much faster than before.

Kyvos makes it easy to look at any amount of data without slowing down. The layout is simple and our team can use it with other tools they already know. It saves time and helps us get the information we need faster.

More pros than cons really! Whatever teething issues we had were addressed by their support team quickly.

Earlier, a lot of decisions were delayed simply because data wasn’t available. With Kyvos, that lag has reduced significantly.

In our organization, we rely heavily on data during weekly and monthly business reviews. What I like most about Kyvos semantic layer is that it allows teams to get answers without slowing down the conversation. Its also made data more accessible across functions. Teams don’t need deep technical expertise to work with data, which has increased overall adoption.

Initially, it can feel limiting that you’re not building everything from scratch and are working within what’s already set up. Over time though, that actually brings more consistency in how teams approach analysis and avoids unnecessary variation across reports.

Our team had tried a few conversational analytics tools in the past, but the responses were mostly generic and shallow. Kyvos has solved this problem, allowing our business team to get rich, contextual insights - without any delay. We can also save our conversations and continue in the same context whenever we resume.

We’ve been using Kyvos to obtain insights from our data using natural language prompts and are quite impressed with the kind of contextual responses we receive. The system also gives analytical summaries in plain English, which is very helpful.

Don’t particularly dislike anything till now. They also introduce new features with frequent releases.

Since we work with extremely large datasets, we were facing a lot of performance issues. After using Kyvos, we get insights much faster. The query response time has really gone down, and we can scale more easily.

Kyvos helps us perform complex analytics and get faster query responses on Looker. We’ve been able to connect Looker directly to all our data sources and work with dashboards that get refreshed instantly.

More pros than cons really! Whatever teething issues we had were addressed by their support team quickly.

Kyvos reduced duplicated semantic logic and simplified governance across departments using different reporting tool.

Our organization uses multiple data platforms, so interoperability was important for us. Kyvos semantic layer worked well across our cloud ecosystem and helped standardize analytics access without forcing us into a single BI stack. I also like that it connects with existing BI tools instead of requiring users to completely change their workflows.

Cross-platform environments naturally involve additional coordination between infrastructure and analytics teams. Kyvos still handled that better than many solutions we evaluated.

We were concerned that moving to a new semantic model would require rebuilding a large number of existing dashboards. That would have taken time and disrupted business users who rely on those reports daily. With Kyvos, we were able to point existing dashboards to the new model with just one click.

One thing I appreciate is that we can represent fairly complex business structures in one place. Previously, we had business logic spread across multiple models and reports. That made updates harder and introduced inconsistencies when metrics changed. With Kyvos, we moved that logic into a single semantic model, which reduced duplication and made it easier to support cross-domain analysis.

Modeling flexibility is strong, so it’s worth spending time upfront to organize definitions properly. Once that structure is in place, though, it’s easier to manage changes and extend the model later.