Did you know? Less than 10% (possibly even lower) of industrial equipment ever wears out, meaning most mechanical failures could potentially be avoided with predictive analytics and cost savings of 30%-40%. These statistics highlight how drastically data analytics has altered HVAC industry processes.

Before: When HVAC Relied More on Intuition

While sometimes effective, the approach of relying on intuition often resulted in inefficiencies and ongoing issues for technicians – as evidenced by key challenges in the past:

• Limited diagnostic tools
• Trial-and-error troubleshooting
• Longer repair times
• Higher likelihood of misdiagnosis

Here is an example that exemplifies these limitations: one winter a heating system malfunctioned during an especially frigid spell and needed repair immediately; when a technician arrived without advanced diagnostic tools he spent hours testing different fixes from experience until finding one which temporarily fixed it but the same issue recurred two weeks later due to lack of data, prompting him to use bandaid solutions rather than finding lasting fixes that addressed its root cause.

Today: Data-Driven Revolution in HVAC

Predictive Maintenance: Staying Ahead of Problems

A major breakthrough in HVAC servicing, predictive maintenance utilizes data analytics to detect issues before they manifest into system breakdowns or energy cost increases, providing timely interventions that prevent system failure.

Benefits of predictive maintenance include:

• Reduced system breakdowns by up to 70%
• Lower Maintainance costs by about 25%
• Proactive scheduling of service appointments
• Prevention of costly emergency repairs
• Extension of overall HVAC system lifespan

Predictive maintenance systems collect information from various sensors within an HVAC system. The sensors monitor factors like temperature, pressure, vibration, and energy consumption – and over time learn what “normal” operation looks like to detect subtle differences that indicate potential trouble spots early.

Efficiency Optimization: Maximizing System Performance

Data analytics not only prevent breakdowns; they’re also invaluable in optimizing HVAC system performance. By studying patterns of system operation and making adjustments that improve energy efficiency and prolong equipment lifespan.

Key aspects of efficiency optimization include:

• Continuous monitoring of system performance for any inefficiencies
• Real-Time inefficiency analysis
• Automated adjustments help maintain the optimal performance of equipment
• Long-term trend analysis for system improvements

Real-Time Monitoring for Quick Action

Internet of Things (IoT) devices enable continuous real-time monitoring of HVAC systems via IoT devices. With real-time monitoring at hand, HVAC system performance can now be monitored in near real-time to give instantaneous immediate feedback, giving rapid responses for issues when issues arise,

Advantages of real-time monitoring include:

• Instant feedback on system performance along with remote technician access for accurate troubleshooting as well as proactive maintenance for future performance improvements to avoid possible issues or potential future maintenance expenses
• Improved overall system reliability

Real-time monitoring can play an invaluable role in critical environments where HVAC performance is vital – such as data centers where even temporary interruptions in cooling could cause equipment failure and data loss, leaving any deviation from optimal conditions unchecked, with real-time monitoring detecting deviations immediately and offering solutions quickly.

Data-Driven Troubleshooting: Precision Problem Solve

When issues do arise, data analytics have revolutionized the troubleshooting process. Technicians now have access to historical data and system details which enables more precise problem-solving

Advantages of data-driven troubleshooting include:

• Faster identification of issues
• more accurate diagnoses from the first visit
• Reduced need for multiple repair attempts
• Improved ability to address root causes rather than symptoms

Gone are the days when technicians were met only with vague descriptions from customers when arriving on-site to address problems; now they can access an abundance of data before even arriving such as historical performance data, past issues and repairs records, real-time diagnostic information from system sensors and comparisons with similar systems in their vicinity.

Reduced Human Error: Enhancing Accuracy

Skilled technicians remain critical components of HVAC servicing; however, data-driven approaches have greatly decreased human error by providing objective, clear data that assists technicians in making informed decisions more quickly and catch potential mistakes before they become serious issues

Here are some results of reduced human errors:

• Better overall accuracy in diagnostics and repairs
• Improved customer satisfaction due to fewer repeat visits
• Less manual checks and checklists used
• More consistent service quality across technicians

For instance, they might identify an unexpected trend in heat exchanger performance that might otherwise go undetected during visual inspection – this way potential issues are promptly and thoroughly addressed by technicians. When combined with analytics technology, HVAC data offers great potential.

The Future of HVAC Data

Data-driven HVAC systems have demonstrated their advantages today, but the future holds even greater promise. Key trends emerging within HVAC data include:

AI and ML

• Analysis of large amounts of data collected across sources
• More accurate predictions regarding system performance
• Even accurate predictions regarding potential problems within systems
• Custom optimization strategies developed specifically for each system

Smart Buildings

• More interconnected HVAC systems that communicate with other building systems
• Interconnected HVAC systems that communicate with other building systems
• Personal devices for personalized comfort control also can be integrated

Energy Efficiency Mandates

• Information crucial for compliance with increasingly stringent energy efficiency regulations
• Automated reporting, process intelligence, and verification of energy savings
• Optimization strategies to achieve or exceed efficiency targets

According to Technavio, the global HVAC market is projected to expand by USD 90.5 billion between 2025 and 2029, attesting to increasing recognition of data-driven systems’ benefits within HVAC operations.

Decarbonization and HVAC Data

One of the key applications of HVAC data analytics is in pushing toward decarbonization. As climate change presents challenges of its own, efforts at lowering buildings’ carbon footprints have become an urgent goal – HVAC systems play a significant role here as they account for much of building energy use.

Data analytics play an integral part in helping commercial entities reduce HVAC carbon footprints, particularly by optimizing energy use without sacrificing comfort.

• Energy Use Optimization: Data-driven systems allow operators to make adjustments that optimize HVAC usage to minimize energy waste without sacrificing comfort levels.
• Integration With Renewable Energy Sources: Connected HVAC systems can adapt their operations to make optimal use of on-site renewable energy sources such as solar panels.
• Demand Response: HVAC systems utilizing data collection capabilities can take part in utility demand response programs to reduce load during peak times and help balance out the grid.
• Tracking and Reporting Carbon Emissions: Advance analytics provide accurate real-time carbon emissions monitoring solutions, helping organizations meet their sustainability objectives more easily.

As regulations surrounding building emissions become stricter, data’s role in managing and reducing HVAC-related carbon emissions will only become more significant.

Tools and Technologies

In order to harness the potential of data in HVAC operations, new tools and technologies have emerged. For example, Field Promax is an HVAC field service management software solution, used by HVAC businesses to streamline operations with data. It includes features such as:

• Data tracking and analysis. Track service calls while keeping an archive for trend analysis and performance optimization.
• Technician Route Optimization: Analyzing data to plan the most economical routes for service calls, cutting travel time and fuel consumption significantly.
• Efficient Schedule Management: Balancing workloads while matching technician capabilities with job requirements to increase first-time fix rates and enhance first fix rates.
• Customer History Tracking: For each HVAC customer, this solution maintains detailed records that enable more customized and effective service delivery.

Another example is BuildingIQ, an advanced energy management platform that leverages AI and machine learning to optimize HVAC performance. It offers features such as:

• Predictive Energy Optimization: Uses real-time and historical data to adjust HVAC settings proactively, reducing energy costs and improving efficiency.
• Automated Fault Detection: Identifies potential HVAC system issues before they become costly problems, ensuring timely maintenance and minimizing downtime.

How Comidor Helps Improve HVAC Operations

Comidor enhances HVAC operations by leveraging intelligent automation, low-code application development, and data-driven insights. in this way, Comidor empowers HVAC businesses to improve operational efficiency, reduce costs, and enhance customer satisfaction. Here’s how:

1. Workflow Automation for HVAC Service Management: With Comidor, businesses can automate job scheduling, dispatching, and technician assignments to optimize efficiency. As a result, workflow automation reduces manual errors and ensures timely service execution.
2. Real-Time Data Monitoring: The platform can integrate with IoT sensors and 3rd-party systems to track HVAC system performance in real-time. Businesses can predict maintenance needs and prevent costly breakdowns through AI-powered analytics.
3. Smart Resource Allocation & Route Optimization: AI-driven insights optimize technician schedules and travel routes. Also, AI-powered solutions reduce fuel costs and response time for on-site service calls.
4. Customer Request & Issue Management: Businesses can streamline customer service requests and ticketing through automated workflows. This leads to enhanced response times with intelligent case prioritization and resolution tracking.
5. Compliance & Reporting: With Comidor, businesses can automate regulatory compliance checks and maintenance logs. What’s more, the advanced analytics and reporting features provide real-time insights for energy efficiency, and performance.

Conclusion: The Data-Driven Future of HVAC

As we’ve seen, data is revolutionizing the HVAC industry in multiple ways::

• Smarter and more efficient operations through predictive maintenance and real-time monitoring
• Precision troubleshooting that replaces guesswork with data-driven insights
• Reduced downtime leads to improved customer service and satisfaction
• More reliable systems and reduced energy bills for customers, leading to enhanced customer experience and reduced energy bills for them.
• Increased to meet sustainability goals and regulatory requirements

Switching to data-driven HVAC systems represents an invaluable opportunity. For HVAC businesses, data-driven HVAC means increased efficiencies in operations, better resource allocation, and the delivery of higher-quality service. For customers, it means reliable systems with lower energy bills that improve comfort levels and reduce stress levels.

As HVAC moves forward, data’s role will only continue to expand. From AI-powered process optimization and integration with smart building systems to other possibilities such as predictive maintenance. HVAC professionals who embrace such technologies will lead the industry forward successfully.

The post Optimizing HVAC with Data: Cut Costs & Boost Performance appeared first on Comidor Low-code Automation Platform.

Top Machine Learning Platforms for 2024

Several machine learning platforms have stood out for their robust capabilities and innovative features, which are applicable across various industries. Let’s take a look at some of them.

Google AI Platform: Popular for its comprehensive tools and services, Google AI Platform supports both deep learning and machine learning models. Google Cloud Services allows seamless integration for organizations that want to scale AI solutions across large datasets.

AWS SageMaker: SageMaker supports a broad set of machine learning algorithms, including those for deep learning. It’s a fully managed service preferred by every developer and data scientist with the skills and proficiency to build, train, and deploy machine learning models. It’s not as user-friendly as other platforms, but it has more advanced features.

Azure Machine Learning: This Microsoft platform specifically caters to enterprise-level ML deployments. It offers more extensive model management tools and a strong emphasis on hybrid cloud environments. Users also have access to various ML frameworks and infrastructures.

IBM Watson: IBM Watson is known for its powerful cognitive capabilities. This technology incorporates advanced ML and data analysis but is best known for its strength in natural language processing and automated reasoning.

Comidor: A low-code digital modernization platform, Comidor is preferred by many users for its ease of use. It integrates AI and ML with Business Process Management (BPM), making it a fitting choice for organizations leveraging AI in their business processes. Because there’s no need for extensive coding skills, it provides various resources and cost benefits to its users.

Considerations in Choosing Machine Learning Platforms

Now that we’ve provided you with a list of top machine learning platforms, it’s crucial to choose the right one for your automation or process improvement project. Here are key criteria to consider ensuring you select a platform that best fits your needs:

1. Ease of Use: Look for platforms that offer user-friendly interfaces, clear documentation, and strong community support. There are many low- or no-code platforms that you can use, especially if your team does not have exposure to extensive machine learning experience.
2. Scalability: If you’re expecting growth shortly, opt for a platform that can adjust to the growth of your data and processing needs. Request a demo to ask if the platform can handle large datasets and complex computations without delays or disruptions. It would be difficult to migrate to a new platform once the old one slows down, so choose wisely!
3. Integration Capabilities: Most ML platforms now have integration options. But the question is, “To which systems and tools?” Learn whether it can work with the technologies you already use for data storage, databases, and even cloud services. With seamless integration capabilities, deploying ML models should be more straightforward.
4. Model Building and Training Tools: Despite built-in features, ML models will still need fine-tuning. If you don’t have an in-house team to handle these adjustments, you should at least make sure your platform comes with a complete suite of tools for building, training, and validation. This includes support for various algorithms, pre-built models, and automated features for model tuning.
5. Deployment Options: If you’re using the machine learning platform in production, it must adapt to various scenarios. It should be easily distributable whether on-premises, in the cloud, or in hybrid setups.
6. Security and Compliance: Depending on your location or industry, the platform needs to comply with various security standards and relevant regulations. This is especially important if you handle massive amounts of sensitive or personal data.
7. Data Preprocessing Features: When training your machine learning tools, you also need additional tools for data cleaning, transformation, and augmentation. This will allow you to enhance the system in case of additional variables.
8. Performance Monitoring and Maintenance: You can’t improve what you can’t observe, so pick a platform with robust monitoring features. This will allow you to maintain and upgrade the system without affecting your operation hours or performance.
9. Cost Effectiveness: Always ask for the overall cost of using the platform. Work with providers who are transparent and upfront with all the fees included, such as subscription fees, computation costs, and any other associated charges. Remember, you may compromise your ROI if the cost does not align with your budget.
10. Innovative and Cutting-Edge Technologies: The platform should be future-proofed with regular updates. When choosing an ML provider, ask about licenses and the expected years of support.

Integration With Other Technologies

Workflow Automation and BPM

ML platforms, workflow automation, and BPM work hand in hand to improve efficiency in the workplace and decision-making. ML analyzes large amounts of data to predict outcomes, and the results can be used in BPM tools to develop better business strategies. Having all this information will allow your business to pinpoint weaknesses in your processes and come up with ways to address them. Over time, you should be able to refine your processes further to get the best outcome that is aligned with your goals.

Intelligent Automation and Robotic Process Automation (RPA)

Companies use Robotic process automation (RPA) to handle repetitive tasks automatically. ML platforms boost these capabilities by adding AI to address more complex tasks and not just simple, routine jobs. If your tasks involve managing large-scale resources—such as processing and analyzing bulk emails, monitoring warehouse supplies, and receiving low-stock notifications—you’ll benefit from process management tools equipped with ML and AI capabilities. This will allow you to reduce errors and mitigate employee exhaustion over tasks considered “donkey work”.

Machine Learning in Digital Marketing

ML platforms revolutionize how companies optimize their online presence and improve engagement strategies. For instance, machine learning used in SEO services allows the analysis of vast datasets that predict consumer behavior. Companies also use for tailoring content and optimizing keyword strategies. The direct effect is higher search engine rankings and marketing campaigns that resonate with target audiences. Some digital marketing professionals even use ML to automate and refine ad placements and content recommendations to reach the right people.

Future Trends and Predictions

Machine learning is undergoing a rapid evolution, and we don’t see it stopping or slowing down any time soon. As we look towards the future, we see more reasons for organizations to get in on the trend as soon as possible. One of the most exciting trends is the increasing convergence of machine learning with big data technologies. We’re seeing this integration now but expect heightened accuracy and significantly reduced latency in these processes moving forward.

There’s also the integration of AI with blockchain technology. For companies in the financial and supply chain sector, we’re seeing more enhanced security and transparency in AI operations in the future. Ideally, the goal is to mitigate trust and security issues associated with AI deployments.

The future of ML is not just about technological growth but also about giving better access to these AI technologies. This enables a broader range of businesses to benefit from these innovations. The continued advancement in machine learning will lead to smarter, more autonomous applications that can fundamentally change how businesses operate and compete in the digital age.

Author Bio
Marc Bartolome is a seasoned Digital Marketing Strategist and Growth Consultant at SEO Services Australia, where he spearheads a dynamic team of experts. Known for his strategic acumen and innovative approach, Marc consistently achieves outstanding outcomes that surpass customer expectations. With a keen eye for emerging trends and a commitment to excellence, he ensures that every campaign not only reaches but also expands its intended impact.

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Comidor platform offers the ability to build your own Low-Code App through App Builder, and include both AI and ML components, in order to determine the writer’s attitude, get predictions, classify text and enhance digital process automation.

In this article, we will give two AI/ML application cases of real business problems where we have included AI and ML in the solution.

Case 1. Loan approval process

Business Problem

A loan approval process starts when a potential borrower reaches out to the organisation. The first-phase employee should input all customer details and check the customer’s creditworthiness. In the next phase, a second-level employee should review all data and decide whether to approve or reject the loan request, which might be demanding, of high-risk, and time-consuming especially for a new employee.

There was not a central system that could handle and manage all loan requests and process steps. The main need in this case was to enhance the accuracy of the decision-making process.

The solution

As a solution to the above, Comidor offers a Low-Code application to monitor all Loan approval processes in one place along with a workflow that orchestrates all process steps.

In the workflow we have included:

• Public form for process initiation by the potential borrower outside of the Comidor environment
• Task allocation to the responsible users and groups
• ML Predictive Model that predicts the loan approval decision based on historic data and variables such as the annual salary and credit score of the borrower

• User forms & fields for data input and display
• Gateways and conditions for path determining
• Automated emails

The Loan approval process steps in detail are:

1. The loan request process is triggered by the customer on their personal web banking portal, with Comidor embedded public forms. The customer adds personal details and the loan information, and selects the type of loan and loan interest.
2. The first-phase employee is notified about the new Loan request, reviews it and adds further information (Credit score)
3. Based on the predefined range of variables in the loan process and historical data on the approval process, the Comidor ML Predictive model provides an instant, high-confidence
   suggested decision.
4. Then, the next-level employee is informed about the loan request and the available ML prediction. The employee can then take the final approval/rejection decision.
5. Finally, the customer receives an automated email with the final decision about the loan request.

What we achieved:

• Big data analysis
• Robust credit decisions within minutes
• Automation of the loan request process
• Pattern identification
• Human error elimination
• Improved and faster risk assessment
• Customer-Self service

Case 2. Customer request management

Business Problem

The Customer request management process starts when a new customer need rises. In this case, there are 4 types of customer requests: individual, corporate, support and complaint.
There was a lack of one central channel of communication between the company and its customers. Resolution time could take too long due to the huge volume of requests and therefore, complaints were increased.

The solution

For this business problem, the solution is a Low-Code application to monitor all Customer requests in one place, along with a reporting dashboard. A workflow that orchestrates all process steps is also included.

In the workflow we have included:

• A public form allowing non Comidor users to trigger internal processes
• Automated emails with process details
• ML text classification model that assists in request categorisation
• AI Sentiment analysis that analyses customer’s sentiment
• Scripts to change the priority of the request upon certain conditions
• Task allocation to the responsible users and groups
• User forms & fields for data input and display
• Gateways, conditions for path determining, and loops
• Timer for auto-closing the process after a certain period of time

• Real-time monitoring and reporting of all customer requests
• Involvement of non Comidor users in internal processes
• Lower resolution time with automatic request categorization
• Increased productivity since manual steps have been removed
• Better customer experience due to automatic prioritization

Find more information about AI/ML and Workflow elements that you can include in your workflows.

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Machine learning is an application of artificial intelligence (AI) that focuses on the development of computer programs that can access data and use it to learn for themselves. Comidor provides ML models such as Predictive ML Models and ML Text Classification Models in order to enhance Digital Process Automation. Use existing data to train your ML Models and get predictions on specified user fields. With Comidor ML Text classification model, assign tags or categories to text according to the field content.

With Comidor ML models you can:

• Enhance process automation
• Eliminate errors
• Save processing time

Comidor ML Text classification can be used for:

• Topic labeling
• Spam detection
• Intent detection

ML Predictive Models

To access ML Predictive Models, go to App Factory Icon > RPA & AI/ML > Predictive Models.

1. Click on the + Icon at the top of the screen to open the Create Form.
2. Type a Title for your ML Predictive Model.
3. Link the Model with a Connected Application or select the respective Entity.
4. Choose your preferred Classifier: 
   • Comidor offers a variety of algorithms that sort data into labelled classes or categories of information, such as J48, DecisionTable, DecisionStump, etc.
5. The Target field refers to the field you want to make the prediction for. Depending on the entity you have selected above, it will populate relevant fields to choose from.
6. Add 2 or more Training fields:
   • The values of these fields will be used by the classifier.
   • The user fields available here are linked to the entity you have chosen.
7. Limit training data with a condition to define specific values to be used by the classifier.
8. Select the desired Save option (refer to the Quick Reference Guide).

Edit ML Predictive Models

1. Go to App Factory Icon > RPA & AI/ML > Predictive Models.
2. Select the ML Predictive Model to edit.
3. Click on the Edit button to open the Edit Form.
4. Edit the information you want and click on the desired Save option (refer to Quick Reference Guide)
   

   

Train ML Predictive Models

After creating your ML Predictive Model, you have to train the Classifier based on the data entered in Comidor. The training process might take a while. The more data we have, the better the Accuracy we achieve.

1. Go to App Factory Icon > RPA & AI/ML > Predictive Models.
2. Select the ML Predictive Model to train.
3. Click on the Train button. Click on this option as many times as you wish, until you reach an acceptable Accuracy rate.
4. Click on the Save button that is appearing next to the Accuracy percentage. The ML Predictive Model is ready to be used in a workflow.

Delete ML Predictive Models

1. Go to App Factory Icon > RPA & AI/ML > Predictive Models.
2. Select one or more ML Predictive Models records.
3. Click on Delete to delete one or multiple ML Predictive Models at the same time. A confirmation pop-up box appears.

ML Text Classification

To access ML Text Classification, go to App Factory Icon > RPA & AI/ML > Text Classification.

1. Click on the + Icon at the top of the screen to open the Create Form.
2. Type a Title for your ML Text Classification.
3. Link the Classification with a Connected Application or select the respective Entity.
4. The Target field refers to the field you want to make the prediction for. Depending on the entity you have selected above, it will populate relevant text fields to choose from.
5. Add Training field:
   • The values of this text field will be used by the Text classifier.
   • The text user fields available here are linked to the entity you have chosen.
6. Limit training data with a condition to define specific values to be used by the Text Classifier.
7. Select the desired Save option (refer to the Quick Reference Guide).

Edit ML Text Classification

1. Go to App Factory Icon > RPA & AI/ML > Text Classification.
2. Select an ML Text Classification to edit.
3. Click on the Edit button to open the Edit Form.
4. Edit the information you want and click on the desired Save option (refer to Quick Reference Guide).

Train ML Text Classification Model

After creating your ML Text Classification model, you have to train the Classifier based on the data entered in Comidor. The training process might take a long time to fetch accurate results. The more data we have, the better Accuracy we achieve.

1. Go to App Factory Icon > RPA & AI/ML > Text Classification.
2. Select the ML Text Classification Model to train.
3. Click on the Train button. Once you click on this option, you can see the Accuracy rate of the Text Classifier. Click on this button as many times as you wish, until you reach an acceptable Accuracy rate.
4. Click on the Save button. The ML Text Classification Model is ready to be used in a workflow.

Delete ML Text Classification

1. Go to App Factory Icon > RPA & AI/ML > Text Classification.
2. Select one or more ML Text Classification records.
3. Click on Delete to delete one or multiple ML Text Classifications at the same time. A confirmation pop-up box appears.

Add a Predictive ML in the workflow design, to trigger an ML Predictive Model and get a prediction for the Target Field of a workflow, or a Text Classification to classify the selected Target field based on the Text Classification Model.

To access Workflows go to App Factory Icon > Workflow Automation > Workflows

Predictive ML

• Drag-and-drop the Predictive ML.
• Give a Title to the component.
• Give the Parent Stage which is the stage of the parent process as soon as this step is reached.
• Select which Model you would like to run at this step, from the list of the ML Predictive Models that you have already created.

Text Classification

• Drag-and-drop the Text Classification element.
• Give a Title to the component.
• Give the Parent Stage which is the stage of the parent process as soon as this step is reached.
• Select which Model you would like to run at this step, from the list of the ML Text Classification Models that you have already created.
• You will also see the Target field and the Training field(s) that were selected in the ML Text Classification Model in view-only mode.

Find out more on how to create and manage workflows step by step and learn all about Comidor Workflow Elements.

The post ML Predictive Models & ML Text Classification appeared first on Comidor Low-code Automation Platform.

Most business processes include authorisation steps and document approvals.
With the Comidor Signature Models & Digital Signature component you can automatically include digital signatures to PDF documents.

Incorporate the Comidor digital signatures in your business processes with the following steps:

1. Draw or upload your personal signature
2. Create a signature model
3. Connect the digital signature component in your workflow

With Comidor Signature Models & Digital Signature component you can:

• Enhance process automation
• Eliminate errors
• Save processing time

1. Draw or upload your personal signature

Every user can specify their own digital signature, which can be used in PDF documents of a workflow.

• Go to the user icon on the top right of your screen > Settings.

• In the pop-up window, select the Signature tab.
• You can click on the “Design your signature” button.
  

  

  • In the pop-up window, you can draw your signature with your mouse.
  • Click on the “Clear” button to erase your drawing or on “Sav”e to store it.
• Alternatively, click on the “Upload your signature” button to upload an image file from your desktop.

• Click on “Save Changes” so as to store the signature settings.
  • A refresh of your browser is also required.

2. Create a signature model

In the second step, you should create one or more signature models, to provide PDF templates with the pages and exact parts of the document where you wish the signatures to be added.

To access Signature Models, go to App Factory Icon > RPA & AI/ML > Signature Models.

1. Click on the + Icon at the top of the screen to open the Creation Form.
2. Type a Title for your Signature Model.
3. Choose a Document in PDF format as your template.
4. Select the desired Save option (refer to the Quick Reference Guide).

After saving your Signature Model, you can get a preview of the document you uploaded.

1. The document you have uploaded in the creation form is displayed per page so you can select which page you would like to view.
2. Click on the “Add signature” icon, to add as many signatures needed to be included in your document.
   1. A signature box will be displayed in your page with an incremental number as title (Signature 1, Signature 2, etc).
   2. You can drag-and-drop the signature box and place it easily in the spot you wish to be displayed.
   3. The signature box can be also resized (from the bottom corner) or deleted.
   4. Any change you made in your model is automatically saved.

Edit Signature Models

1. Go to App Factory Icon > RPA & AI/ML Automation > Signature Models.
2. Select the Signature Model to edit.
3. Click on the Edit button to open the Edit Form.
4. Edit the information you want and click on the desired Save option (refer to Quick Reference Guide)
5. Any change you made in your model view form (signature spots, size, etc.) is automatically saved.
   

   

Delete Signature Models

1. Go to the App Factory Icon > RPA & AI/ML > Signature Models.
2. Select one or more Signature Models records.
3. Click on Delete to delete one or multiple Signature Models at the same time. A confirmation pop-up box appears.

3. Connect the digital signature component with your workflow

Add a Digital Signature component in the workflow design, to include digital signatures in PDF documents of your process after specific steps/approvals.

There are two options:

• Include a digital signature from a user  (as defined in their user settings); the workflow will add it automatically to the place of the document as per the signature model.
• Send an email to a recipient(workflow field), with a link. The email recipient by clicking on the link will be re-directed to another page with the full document. There, the recipient can read the whole document, switch from one page to another, draw his signature, place it in the document, resize it and finally submit it. The workflow will await until the signature is added.

To access Workflows, go to App Factory Icon > Workflow Automation > Workflows and choose the design tab.

Digital Signature

• Drag and drop the Digital Signature in the workflow pool.
• Give a Title to the component.
• Give the Parent Stage which is the stage of the parent process as soon as this step is reached.
• Signature Document: choose the binary field, where the user will upload the document in PDF format without signatures. After the digital signature component runs, the selected binary field will store the PDF document with the signature.
• Select which Model you would like to run at this step, from the list of the Signature Models that you have already created.
• User’s Signature: choose which user’s signature you want to add to your document.
• Signature position at Document: refers to the number of the signature that you need to place in your document (Eg. 1, 2, etc.).

• Check the option Send Email if you want the digital signature to be added by an external user.
  • Define the Email Recipient; choose from a list of text/email fields. This field should get value in a previous step of the workflow.
  • Type your Email body. With the rich text, you can add colours, styles, and font sizes and fully customize your email message.

Find out more on how to create and manage workflows step by step and learn all about Comidor Workflow Elements.

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