Seeq Reviews

The solution is used for time series data analysis. It is very useful for the oil and gas industry, where we use compressors, pumps, and gas turbines and need to monitor them remotely. Seeq is useful in such cases. I support more than 100 pieces of equipment for a customer in the oil and natural gas industry.

There are ten gas turbine generators for power generation. We have to monitor them. A gas turbine has 150 to 200 parameters to monitor. We have to monitor vibration parameters, lube oil system, and auxiliary systems and notify the organization if we notice any abnormalities.

We use Seeq to analyze the parameters daily, weekly, or monthly. If we notice any deviation, we must analyze it. For example, if we see an increase in the bearing temperature, we immediately discuss it with the on-site team. The team checks whether the lube oil is contaminated. If there is contamination, the team takes action accordingly. It could also be a bearing issue. We must run the machines without any damage to avoid catastrophic failures.

Seeq is a useful product. The systems are not operated manually in the oil and gas industry. They are operated remotely. Usually, if pumps are used, there will be two or three pumps. Two pumps would run, and one would be on standby. If a pump needs to run for 500 hours, we can calculate it using Seeq. Once the 500 hours is up, the pump stops, and the standby pump automatically starts running. It is one of the biggest advantages of the solution.

We built a detector dashboard. We set the upper and lower limits and check if things are operating within that limit. For example, if the upper limit is 75 and the lower limit is 50, and the trend is between these limits, the dashboard must show it as green. If it is below or above the operating levels, it must show a red color.

There are 100 parameters. If such a dashboard is built, it will be very useful. We only have to validate and analyze the parameters in red. We need not check the green ones. It is a revolutionary concept.

We can see increasing trends in vibration parameters using predictive analysis. We can easily find out how many days the trend will hit the upper or lower limits. Since we have time, we can inform the on-site team that monitors it. For example, we can inform the team that the lube oil tank level will drop to the lower limit within a month. So, the team gets one month’s time to top up the level easily. Such predictive analytics is very useful to the industry.

The tool is user-friendly. It is very easy to use. We can learn it in a month. However, we must understand where to use the tool. For example, if there is a mechanical engineer, they might know about pumps. They must also know what parameters must be analyzed for the pumps. Seeq is just a medium. We must do the analysis ourselves. Seeq is a friendly and useful tool. It has no issues.

We face a bit of an issue if there are any server issues or upgrades from Seeq.

I have been using the solution for more than three years.

Stability issues are rare. If the server is good, we have no issues. It is related to the PI Server. It is one of the data-collecting servers. Our customers’ servers are interconnected with the PI Server. I rate the product’s stability an eight out of ten. I have not faced many issues in the past three years. It is one of the best tools for static analysis.

The product’s scalability is good. The tool increases the functionality and productivity of the products.

The support team is proactive. If we face any issues, the support persons respond immediately. If there is a data gap issue, it is related to PI connectivity. We communicate it immediately to the support persons, and they respond within one to six hours and take the necessary actions.

Users need basic training to use the tool. They must know how to add tags and how to build things. I am a consultant for an oil and gas customer. They have Seeq, and they give us remote access to their server.

We provide support. I will recommend the solution to others. It provides more options for scatter plots. They are very useful. We only analyze x and y in scatter plots. If there is a flow, I can add two or three tags to correlate them simultaneously. These options are very useful.

Overall, I rate the tool an eight out of ten.

I represent the consulting part of our company. We support multiple customers in India, Southeast Asia, US, and Europe. Our daily requirements involve building data science algorithm applications using Seeq Data Lab, building workflows using Seeq Workbench, and developing dashboards on top of Organizer topics.

The impact of Seeq's predictive analytics features really depends on the industry. In oil and gas, for example, it's all about assets and equipment. It's very important to keep assets up and running because they operate 24/7, 365 days a year. Even an hour of downtime can result in a million-dollar loss.

Therefore, building predictive maintenance algorithms is key. The outcome of these models is predictive maintenance, and the impact is significant. We're not just talking about one asset but a fleet of assets, maybe ten, twelve, fifty, or even a hundred, depending on the organization. This helps the organization plan and schedule maintenance activities, providing tremendous value on top of existing calendar-based or preventive maintenance practices.

AI-based capabilities in Seeq:

We have used Seeq Data Lab for developing AI algorithms. There's no out-of-the-box AI available, and I believe no other similar platform has that either. I've even evaluated TrendMiner, the closest solution I know to Seeq, and even that doesn't have it.

AI is great in Seeq. You have a good feature that allows you to convert a Python algorithm built in Seeq Data Lab into a user-friendly interface, essentially turning it into a vertical application. For the end user, it's just an application; they don't have to worry about the Python code. The workflow and how they've thought through the consumption of Python code is pretty impressive.

I've worked with Seeq, Cognite, Azure, AWS, DataRobot, and many other platforms. With Seeq, I've realized that it's very easy to use. You hardly need any training, maybe three hours is more than sufficient. The best part is that the entire data science workflow is automated in Seeq. You don't even have to be a data scientist; a production engineer can focus on production and still leverage Seeq.

For example, we have a customer, an Oil and Gas company in India, where I've been involved since day one with installation, implementation, and use case development. It's been four or five years now, and every quarter we go on-site to develop new use cases. We started with ten users and now have more than one hundred and fifty, all of whom are very appreciative and are developing use cases on their own.

Compared to any other platform, Seeq is the best and most adaptive. If someone focuses on Seeq, they can very easily get their hands on it and start utilizing it for their daily workflows. I've seen more than ten customers who have completely replaced Excel with Seeq.

Seeq takes care of security very well. Since it has moved to a completely SaaS model, it has to address all cybersecurity points. Seeq adheres to SOC 2 Type II security standards when it comes to the cloud.

Data integration and security:

Regarding data integration, Seeq is built to handle time series data very well. However, that doesn't cover the entire manufacturing analytics journey. There's still a lot of non-time series, unstructured data, which is where Seeq has some limitations. 

But Seeq's philosophy has always been focused on time series data, although customers might compare it to other solutions. So, for data integration, Seeq can do what it does very well, but there are other opportunities for development in this area.

Seeq Organizer, which is used for dashboarding, has some limitations. 

In Seeq Organizer, we've realized that process engineers want dashboards with more drag-and-drop features, like Power BI. Seeq has limitations in terms of the variety of widgets and visualizations you can use. You're limited to a few types like line charts, bar charts, and pie charts (which was introduced recently). Power BI offers a wider range of dashboarding options.

It's not that Seeq is solely a dashboarding tool; it can connect to Power BI and Tableau. But customers who purchase Seeq and expect its dashboarding feature to be competitive with Power BI and Grafana might be disappointed. So, dashboarding is where I see a lot of room for improvement in Seeq.

I have experience with this solution. It has been more than four years. 

Seeq is a US company, and my company is the exclusive partner for India and global implementation partner for Seeq in Southeast Asia. I lead the entire data science group, and we've been using it extensively. 

In fact, we have supported Seeq on various product development projects for more than four years.

With the earlier on-premise version. But since it's now completely SaaS, I hope those issues are resolved. I'm still exploring the SaaS version, as customers in India and Southeast Asia are not always comfortable with cloud and SaaS due to government regulations.

With the on-premise version, we experienced difficulties with reliability and availability for almost a year, possibly due to hardware limitations. Seeq was unavailable multiple times each month. So, we had to restart the service or involve Seeq's system reliability engineers to resolve the issue. However, I don't think that's a challenge with the SaaS version.

I would rate the stability a seven out of ten, with one being the worst and ten being the best. 

Seeq has good scalability, thanks to a feature called asset trees. It's easy to scale up, but that's more of a sales pitch/jargon than reality. Replicating an asset is as simple as clicking a button, but the moment Seeq data models are involved, it becomes a pain because you have to create copies of your Datalab file for each asset.

So, scalability is fifty-fifty. Practical customer expectations involve scaling up data science algorithms, not just data analytics workflows. When we use Python (Seeq Data Lab) and try to scale it within Seeq, we face computational and replication challenges. It's not just clicking a button; there's a lot more effort involved. We initially anticipated a 40% reduction in effort, but that wasn't the case.

But sometimes, we have realized that maybe 100% of the effort is required even if we scale up.

Looking at the entire landscape of similar software solutions available in the market, I'll still rate the scalability of Seeq a nine out of ten. 

The customer service and support are very powerful in terms of response time and knowledge. It's just that with Seeq SaaS and recent organizational restructuring, everything has to go through the Seeq customer support call, which makes it a bit difficult. I only get a response once they attend to my queries. 

Lately, it's been taking one or two days to get support access, whereas earlier it was more transparent, and I could directly reach out to system reliability engineers via email for immediate support within a couple of hours. But now, they are more structured and process-oriented, which could be one of the reasons. So it takes a little bit longer for them to respond.

Positive

With Seeq SaaS, it's a lot easier to setup and deploy. Seeq itself handles setting up the instance, and I just receive the link for the dedicated customer. The only thing I need to worry about is connecting to the historians and data sources. 

Seeq has many connectors, so it's relatively easy, although it can still be cumbersome due to dependencies on the customer side. Compared to other solution providers, Seeq is much easier.

The pricing is average. Seeq has changed its strategy. Most likely, it's based on the number of sites, assets, or tags, and it varies depending on the customer. There's no standard pricing.

I would recommend it to other people. 

My recommendation:

So, typically, when there are historians, the first thing is the limitation on the number of licenses. For example, if I'm a control engineer, I have no visibility of what's happening on the quality side because quality is measured by a different team, and the systems are themselves different.

You have LIMS. Now, a control engineer who is sitting in the control room has no visibility until they get feedback from the quality control group. That feedback usually happens through WhatsApp, phone communication, or physical communication. 

With Seeq, you can monitor and trend different data streams from different sources on a single screen. There is lot of value right here. Even though it can not be quantified in terms of cost savings. This integrated visibility adds significant value for the end consumers operating the plant.  

Data Integration and Cleaning:

Next is the data processing capabilities, like data cleaning. Even if you are a data scientist, you may not be aware of all the algorithms available in the market. When it comes to time series analytics, it’s different. It's no longer just AI and machine learning; you need knowledge of time series data, how sensor data looks, and the applicable algorithms. Seeq offers automated, point-and-click solutions for these workflows. You don’t need to know data science or data preprocessing algorithms. Just click, select the parameter, and you’re done.

Faster Time to Value:

These are a couple of points where I see a lot of value. Customers often try to set up their own digitalization groups and build everything on their own instead of buying Seeq. They might try to develop or reinvent the wheel, which never happens. Everything remains in Python. If that effort is spent on Seeq, they can start developing and realizing value in the first month, not in the span of years and weeks.

Overall rating:

Overall, I would rate it a seven out of ten. And the reason is, Seeq was great five years back when there was no competition and digitalization was just emerging. Now, other companies are developing products like Seeq, and some features could be better and more efficient. 

Seeq needs to stay competitive by understanding customer expectations, which will keep changing. Seeq needs to conduct surveys and incorporate critical features and customer expectations into their product development roadmap.

My main use case for Seeq is process monitoring, which includes easy visualization of time series data, low to no-code custom functionality and scripting capabilities, and automated data extraction pipelines. These features are useful for process manufacturing and batch or experimental monitoring.

Seeq positively impacts my organization by enabling us to analyze significantly more data and use advanced analytic techniques that were previously reserved for programmers, opening that capability up to scientists who had no programming experience.

We have been able to use Seeq to identify some operational savings, although I cannot provide additional details on those specific instances.

Seeq enables decision-making in the organization where previously expert-level knowledge of the process at hand was required. Distilling the process information so that someone with minimal expertise can intervene and understand what is happening is impactful and translates to real labor savings.

One of Seeq's greatest features is the capsule functionality. This is a unique way of looking at time series data, as it allows you to define specific mathematical or logical conditions that must be met to identify a specific slice of time that bounds your signals. It enables you to compare these slices to similar slices of time where similar conditions were met, either on the same trace or across different traces. This time-domain comparison is particularly useful when dealing with continuous streams of data.

Seeq also offers APIs, so if you are willing to put in development efforts, you can get data flows and automation working as needed. The company is responsive to adding new features and engaged with their user community, seeking feedback across all applications and industries. They are a great company overall.

Beyond capsule functionality, Seeq is adding AI capabilities and audit trail capabilities for the pharmaceutical industry. Their Data Lab functionality is particularly valuable, as it allows you to bring data into a Python environment to perform custom manipulation. They also offer worksheet functionality, which is a way of creating dashboards where you perform data manipulation and then create dashboards all within the same interface. This integrated approach is very convenient.

Workbooks are the primary feature for my needs, allowing me to ingest massive amounts of time series data, perform custom calculations, and use capsule functionality. The workbooks function as a great visualization aid for my data.

Seeq can improve by incorporating more advanced data pipelines. While they have a strong training program and academy, additional hands-on training would be beneficial. The product essentially functions as a supercar that you can drive as fast as needed, so improvements depend on how much users want to get from the tool.

Seeq is already distilling something very complicated into something that is relatively easy to use. The à la carte approach allows users to employ just the time series visualization functionality, some calculations, the worksheets, Data Lab, and other features as needed.

One challenge was the limited capability to have custom input or script input within the web user interface. More advanced flows required using the Data Lab API or other deprecated APIs. Seeq's core strength is tapping into SQL databases from a process historian. If you are not using that primary use case, workarounds are available, but you need automation engineering support.

I chose a rating of eight because Seeq is definitely above average compared to other offerings. The company ethos, customer support, and ability to listen to the market to drive product improvements are positive factors. The feature set is quite comprehensive and unique, though not perfect. The user interface could be simplified, and the non-conventional ways of loading data via the user interface could be improved.

I discontinued the use of Seeq in December 2025, but I had previously used it for approximately two years.

Seeq is stable.

Seeq's scalability is extremely strong based on their product architecture, which is well designed.

Customer support is excellent. They are willing to help, and they have strong documentation, user conferences, and training academy videos. However, the product is challenging to use given the multitude of features, and not everyone can immediately start using it, particularly on the administration side. They are very helpful, even though substantial self-service is required.

I previously used a combination of Microsoft Excel and custom Python scripting before Seeq. Excel was unable to handle the massive amounts of data we were dealing with eventually and had issues refreshing visualizations despite offering low to no-code capabilities that we needed for analysts with limited computer backgrounds.

Before choosing Seeq, I evaluated other options, although the specific names escape me at the moment. InfluxDB was on the list, but one of my colleagues was responsible for that evaluation.

I would advise others looking into using Seeq to consider their use cases and build an internal repertoire of commonly used features. It is beneficial to build camaraderie among your user base so they can exchange techniques and formulae. One of Seeq's powerful features is its ability to facilitate collaboration across your team. I gave this product a rating of eight out of ten.

We use Seeq to convert real-time data into graphs for monitoring and surveillance. It helps us track real-time data and visualize it effectively. In the oil and gas industry, we use Seeq for process optimization. We analyze real-time data to understand downtime and other issues from the previous day at specific wells. This data helps us make informed decisions and plan future actions based on past performance.

Seeq helps with decision-making by providing real-time data. One benefit we've seen is with real-time data. For example, a few weeks ago, we had a planned event for a well, but we couldn’t perform it because the healthy temperature was too low. With Seeq, we could monitor the temperature and pressure in real-time, which helped us avoid potential issues that could have occurred if we hadn't used Seeq.

I like Seeq because it’s very useful for production surveillance. It provides detailed insights, like the ability to view data down to the minute, which is helpful for tracking when a well goes offline. This feature is particularly beneficial for our needs. However, we don’t use Seeq for predictive analytics; we handle that with other software.

Regarding AI, I wish Seeq had AI capabilities. For instance, if Seeq could analyze graphs and offer recommendations, it would be advantageous. Currently, there is no AI feature in Seeq.

In terms of improvements, it would be great if Seeq could enhance its data export features and possibly integrate more functionalities to reduce the need for multiple software tools. I would like a specific feature related to predictive analysis, which Seeq currently lacks.

I’ve been using Seeq for about eight to nine months now.

Regarding stability, I've noticed that Seeq can sometimes be slow, even when I'm using a good Wi-Fi connection. I’ve reported this issue, and it seems the problem might be with the software rather than my connection.

As for scalability, I haven't had any experience with increasing capacity or adding new users, as our team size has remained the same. We haven’t tried scaling Seeq yet, so I can’t provide feedback.

About ten to twelve people in my team and a few others in the organization use Seeq.

I haven’t contacted technical support for Seeq so far, but it might be useful in the future.

I wasn’t involved in the initial deployment; our IT team handled that. As for onboarding, I found it to be straightforward. Initially, it was a bit challenging, but it was easy to learn once I got used to it.

We have an IT team that handles the maintenance. We use Seeq across multiple locations and departments.

Regarding other data and analytics services, we use Kappa for analysis and Seeq for surveillance monitoring. The two are pretty different, so I can’t compare them directly. Seeq’s features are specific to monitoring, while Kappa is used for analysis.

I would recommend Seeq to others in the oil and gas industry for production optimization. It’s essential for that purpose. Overall, I would rate Seeq around an eight or nine. If I had to choose a whole number, I’d go with nine.

In the initial six to eight months, I used it for process analysis, like identifying the different tags through a historian and then making some use cases out of them. This is a very simple yet powerful tool that can connect to the historian in real time, and then we can perform some formula-based calculations. It is very useful.

So, I started with basic process analysis and then went into the predictive part, which is also very useful. I've used the prediction part to create different inferences in my industry. 

Some lab results and analyses take time, like eight hours or seven days. So, to create real-time inferences for such signals, Seeq was very useful through its predictive capabilities.

The basic process analysis could be done anywhere on any software or platform nowadays. But the techy part of Seeq is its strongest point. 

Time-series analysis can be done in any computer software, but having the predictive part included in the package is a plus point, according to me.

I have a couple of suggestions for the organization, and I've told them as well.

Nowadays, time-series analysis for manufacturing units is a primary need. What they're looking for is the incorporation of artificial intelligence and machine learning into these tools and then getting some insights out of it, which is closed-loop. Till now, we have been facing open-loop solutions. But how to apply these insights or inferences into my manufacturing unit, which is running 24/7?

Being an engineer and a person who comes from the industry, I would rather believe in first principles than a data science model. To break the ice, they need to come up with more of the predictive part of it using machine learning techniques, which can be closed-loop solutions, which can help operators and automation engineers to apply these insights into the units rather than keeping it open-loop.

I have been using it for a year now.

Stability issues happen multiple times because it may be the on-premise tool issue. It all depends on the server's capability. If the hardware is not sufficient and multiple users are using it, it breaks down, the server load increases, and then nobody is able to work on it.

However, for the cloud part, it is not that easy to integrate a cloud solution into a manufacturing unit because our OT network has so much automation. OT-to-IT integration itself is a big question. So, sometimes, some organizations believe in going to the cloud, and some may not be willing to.

In terms of software stability, I'd rate it maybe seven out of ten.

Considering the right person is using it, and they're trained, it is very scalable. On the scalability part, I'll give it around nine out of ten.

A certain amount of training is required to use the tool. It's not like you can just directly jump on it and create some models or even do basic process analysis without taking training. So, preliminary training is required to use this tool. And to master it, more rigorous use and a couple of use cases development are required to go ahead with the flow.

For the deployment part, currently they have closed on-premise support. Now they're only offering cloud solutions. But previously, they used to provide both on-premise support and cloud. 

When I worked on this tool, we had the facility of on-premise support. 

But it requires a certain amount of hardware and infrastructure to be developed. Sometimes it becomes messy and clumsy to establish this hardware and connect it with your historian. I think they might have stopped this on-premise support because it becomes very difficult and technical for a non-technical person to establish and connect these tools with your hardware. 

And also, if the number of users increases, the server gets loaded and everything stops. So that is an issue for the hardware. But they have stopped the on-premise support and totally went into the cloud. So, that problem might not be happening right now.

It has a moderate cost compared to other tools and solutions out there in the manufacturing sector.

Overall, I would rate it an eight out of ten. The reason why I'm cutting points is purely because they're still not applying deep learning techniques for the predictive part of the solution. They have to integrate deep learning techniques to solve some of the more sophisticated issues in the industry.

My recommendation depends on the goal of an organization or a person. What they want to achieve through this tool. If they're just going for routine process analysis, they might not be very enthusiastic about it. 

But when it comes to technology integration with automation and developing various solutions in the company, if that maturity is present in the organization, they'll definitely take it seriously.

In that aspect, I'd recommend they should use it and explore the capabilities of the tool. However, I'll advise you that it's not a one-stop solution for all analyses. You have to use other tools and solutions to get a holistic approach or direction.

I first used Seeq for a customer who was starting up their facility. We used it to diagnose problems such as water utilization and tuning control loops, along with various other process issues.

The conditions are extremely helpful. You can configure conditions to identify the data you want and filter out what you don't want, which is very efficient and saves a lot of time compared to using Excel or other applications. It performs very well with large datasets, so it doesn't get bogged down when you have a lot of data. The Datalab environment makes what it can do essentially unlimited.

They have many features addressing various issues. For example, something I developed plots the gains in a prediction model, which could be a built-in capability in Seeq. Artificial intelligence support exists for the Datalab environment. 

The advanced functionality requires some training.

I have been using Seeq for three or four years.

I've had issues remedied, but any software on the market has bugs. There is no perfect software. I ran into one yesterday because it was a new feature. I tried to use it, but it had an issue. There are some bugs, but compared to most software, it's pretty good.

I rate the solution’s stability a nine out of ten.

The solution is very scalable. 

Support is as good. They are very supportive as we're a partner. We have an application engineer assigned to us. We discuss it every two weeks. Their technical support is outstanding.

Positive

The deployment is very easy because it's cloud-deployed. You're essentially just connecting data sources. If deployment means having it run and connected to a data source, then it takes about a day or less than a day.

The solution is certainly worth the investment. Some of the software licenses out there are for industrial use, such as a distributed control system. Some of the licenses are much higher than what Seeq might be. Seek is flexible in pricing; it's hard to generalize.

In terms of data integration, Seeq is outstanding. You can connect to any data source you want, making it easy to achieve connectivity. Seeq is a cloud-deployed solution with appropriate mechanisms to ensure security. One key aspect is that data flows only one way: into Seeq. Data does not come out of Seeq, so you don't have to worry about Seeq altering your data, stopping motors, or closing valves.

I recommend the solution.

If you are considering buying it, my advice is to get your hands on it and test drive it. However, it's important to understand that no software is perfect. If you're looking for something that meets your every dream, you'll probably not find that. But if you're looking for something complete, let me explain.

Some data analytics solutions are only part of a solution. For example, they may only do predictive modeling, connect data sources, visualize data, or align things in time. Some people think those features by themselves constitute an entire data analytics package. Seeq is a complete solution. It's built for collaborative engineering, allowing others to see the work you did and share it easily. You can deploy it across the enterprise so that everyone sees the results.

Overall, I rate the solution a ten out of ten.

I use the solution for trend visualization and real-time data. We use Seeq to connect various pipelines and create dashboards.

The tool's most valuable feature is connectivity and visualization. Seeq is a very good product for analyzing time series data using its time intelligence and time performance features.

Seeq is easy to begin with. If someone explains it to you shortly and clearly, it is very understandable, and anyone can use it.

I have noticed that Seeq sometimes struggles with connectivity issues, leading to data gaps. This is an area that needs improvement. While connecting to data, sometimes, due to connection loss or issues with the tool itself, the tags may not display any trends, resulting in missing information.

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

The tool breaks down at times. 

My company has more than 100 users. 

I haven't contacted the tool's technical support yet. 

My company chose the solution because of its scalable time series data. 

The tool's deployment is easy and is completed in three to four hours. Updates are needed every five months, especially if you want to add new features or specifications.

Based on my experience, I recommend using Seeq, especially if you need to visualize and monitor real-time data. I rate the overall product an eight out of ten. 

We used real-time sensor data to predict equipment failures and other issues. The data came from a fracturing process, including lubrication systems, and was processed in Seeq. Seeq has three apps: one for visualization, Workbench (a point-and-click tool), and DataLab, which is Python-oriented. I used all three to build use cases.

For process optimization, predicting equipment failure helps save costs by preventing downtime. This directly impacts production and reduces non-production time. Seeq is cloud-based and connects to various data warehouses like Amazon, Microsoft Azure, and others. You can integrate it with around 200 data providers, which gives flexibility for data storage. However, Seeq doesn’t store data; it requires connection to external databases.

What I liked most about Seeq was its user-friendly design. The Workbench app was made for everyone, not just data scientists. Engineers and business analysts could use it to build analytics solutions quickly without needing to code. Seeq offers many examples and training resources, like online help, chat support, and office hours. They even provide consulting when needed. I appreciated the range of functions available, especially how Seeq handles time series data. You can apply functions and see results immediately, without altering the original data, which allows you to undo steps and go back if needed.

Seeq's ability to set up predictive models and monitor variables, setting alarms for thresholds, was particularly useful. You could easily apply solutions to different processes, such as monitoring pumps or generators. The platform’s flexibility extends to data conversion and filtering, and it has an outstanding feature where it doesn’t modify the raw data during analysis.

In terms of decision-making, Seeq’s daily training sessions helped users maximize its capabilities. Less than 10% of users had certification at the time, but there were efforts to increase that number. Seeq also offered consulting to help users develop their projects.

As for improvements, I felt there should be a more efficient way to address repetitive customer questions, perhaps using chatbot technology to streamline responses. In terms of functionality, I noticed that certain machine learning methodologies, like Principal Component Analysis (PCA), were missing from the Workbench. While it was possible to perform these analyses in DataLab, adding such features to Workbench would enhance user experience and allow for more complex calculations without needing to switch environments.

Last year, I worked with Seeq in the oil and gas industry, specifically for electric mains. 

Overall, I rate the solution a nine out of ten.