Training and onboarding content was being created almost entirely by hand. This meant long turnaround times, inconsistent quality, and high costs. Without automation or reusable modules, scaling training across teams was slow and resource-heavy. As training needs increased, the gap between business demand and delivery kept growing, limiting overall agility.

Action

AI-Orchestrated Content Creation – Key Actions

The system orchestrated 14 specialized AI agents to manage the end-to-end workflow, from concept to deployment. Trainers could provide inputs conversationally or through guided forms, which the system converted into structured outlines, instructional steps, assessments, and media assets. Built-in QA agents validated instructional integrity, tone, and brand alignment, ensuring consistency across all deliverables. Once complete, the system produces deployment-ready, version-controlled content, eliminating manual rework and cutting production cycles to just hours.

The post Transforming Workforce Learning with AgenticAI Platform first appeared on Aventior.

Clinical trial data is fragmented across registries and documents stored in XML, JSON, CSV, HTML, and PDFs, with key facts buried in narrative text. Manual extraction and normalization of eligibility criteria, endpoints, sites, investigators, and amendments slow down decision-making, increase costs, and raise the risk of trial failure. A solution was needed to automate ingestion, harmonization, and analysis while preserving compliance, transparency, and data quality.

Action

AgenticAI in Trial Analytics – Key Actions

The system constantly collected and cleaned trial data from different registries and document formats, turning it into a single, reliable source. This made it possible to optimize protocol design and compare ongoing studies in real time. It also highlighted enrollment progress and flagged potential risks early. Eligibility rules were transformed into easy-to-use filters to quickly estimate how many patients could qualify and to spot gaps in representation. Sites were automatically scored and mapped based on their track record, capacity, and diversity coverage. Altogether, the platform created a continuous cycle of intelligence, plan, gather, check, enrich, recommend, and monitor, to deliver timely, actionable insights.

The post AgenticAI-Powered Clinical Trial Analytics Platform first appeared on Aventior.

The era of straightforward automation has given way to the sophisticated, dynamic world of Agentic AI systems. Today’s artificial intelligence no longer merely follows scripts or performs repetitive tasks; instead, it autonomously analyzes data, makes real-time decisions, and continually learns from new information. This evolution from Robotic Process Automation (RPA) to autonomous Agentic AI marks a radical shift in operational strategies across industries.

Unlike conventional AI solutions, Agentic AI doesn’t just execute predefined rules; it proactively predicts scenarios, adapts strategies in real-time, and enhances operational efficiency at unprecedented levels. Yet, this autonomy introduces complex, dynamic vulnerabilities that traditional cybersecurity measures can’t effectively counteract. The speed at which AI autonomy is advancing far outpaces most organizations’ cybersecurity readiness, creating urgent strategic imperatives for IT leaders.

Understanding Agentic AI’s Autonomous Power

Agentic AI systems leverage advanced machine learning algorithms, deep neural networks, and reinforcement learning models to independently evaluate vast datasets, identify patterns, and take actions without human intervention. By minimizing latency and optimizing decision-making, these systems revolutionize business operations, from predictive analytics and customer engagement to operational automation.

However, autonomy also means that AI systems are susceptible to manipulation without continuous oversight. Their dynamic nature and adaptability, while advantageous, expand the attack surface significantly, presenting fresh challenges in cybersecurity that demand comprehensive strategic responses.

The New Threat Landscape: AI vs. AI

The emergence of Agentic AI systems has created a radically different threat environment, one where artificial intelligence is both the defender and the attacker. Cybercriminals now leverage AI to launch increasingly sophisticated attacks, and the very autonomy that makes Agentic AI powerful also introduces novel vulnerabilities. The following four areas outline the key aspects of this evolving threat landscape:

1. Sophisticated AI-Driven Attack Vectors

Cybercriminals are weaponizing AI to create hyper-realistic deepfake videos, voice clones, and personalized phishing campaigns that can fool even security-conscious employees. These AI-powered attacks are far more convincing and scalable than traditional methods, making them particularly dangerous for organizations unprepared for this new reality.

Cybercriminals now harness AI capabilities to craft hyper-realistic, highly convincing threats:

• Deepfake Attacks: Utilizing generative adversarial networks (GANs), attackers create authentic-looking fake videos or audio that impersonate executives, manipulate public opinion, or deceive employees into fraudulent activities.
• AI-Powered Phishing: Customized phishing attacks driven by AI algorithms predict and exploit individual vulnerabilities, significantly increasing the success rate of these targeted campaigns.
• Automated Vulnerability Detection: Malicious AI systems rapidly scan networks for weaknesses and execute automated attacks, reducing detection windows dramatically
  

2. Vulnerabilities in Autonomous AI Systems

The very autonomy that makes Agentic AI powerful also makes it vulnerable. These systems can be manipulated through:

• Adversarial Attacks: Slight alterations in input data designed to confuse or mislead AI systems, causing catastrophic decision-making errors.
• Prompt Injection: Exploiting how AI processes natural language prompts to bypass security controls or access unauthorized information.
• Data Poisoning: Corrupting datasets to subtly bias AI learning processes, resulting in erroneous decisions and system degradation over time.
  

3. Expanded and Dynamic Attack Surface

Agentic AI systems are inherently dynamic; they adapt, learn, and evolve continuously based on new data and interactions. This constant evolution means the system’s internal state and behavior can change in real time, resulting in a fluid and unpredictable attack surface.

Unlike traditional software, where vulnerabilities are relatively static and patchable, Agentic AI introduces a moving target for security teams. Each system update, new data source, or external interaction may introduce unforeseen risks. Traditional security tools are ill-equipped to handle this level of variability.

Key Risks:

• Constant Change: AI systems evolve continuously, making their behavior and vulnerabilities less predictable.
• Self-Modifying Behavior: Agentic AI adapts to its environment, which can lead to unintended attack vectors over time.
• Opaque Decision Paths: AI decision-making often functions as a “black box,” making threat analysis and root cause detection difficult.
• Traditional Security Gaps: Rule-based and perimeter-focused security tools are not designed to secure evolving, autonomous systems.
• Hidden Dependencies: AI models may rely on third-party data streams or APIs, which expand the attack surface through indirect vectors.

Strategic Imperative:
Organizations must adopt real-time, AI-driven cybersecurity systems capable of continuously learning and adapting alongside the AI systems they protect.

4. Regulatory and Compliance Challenges

As AI technologies evolve, so do the regulations that govern them. Around the world, lawmakers are introducing new frameworks to ensure that AI is used safely, ethically, and transparently. These regulations are especially focused on systems that operate autonomously or influence critical decisions.

The European Union’s AI Act is a prime example. It outlines strict obligations for high-risk AI applications, including requirements around transparency, data governance, and human oversight. In the United States, the NIST AI Risk Management Framework provides detailed guidance for assessing and managing risks throughout the AI lifecycle.

For organizations deploying advanced AI systems, these requirements are not optional. Falling short can result in serious consequences:

• Financial penalties for non-compliance
• Suspension of AI services or operations
• Loss of credibility and customer trust
• Legal exposure and potential litigation

Security and compliance must be built into the foundation of any AI initiative. It is no longer effective to treat them as separate efforts or address them late in the process. Leading organizations are integrating regulatory alignment into every stage of AI development, from planning to post-deployment monitoring.

Key Insight: Compliance must be embedded into the AI strategy from the beginning. Addressing it only after deployment increases risk, slows progress, and can damage the organization’s long-term resilience.

Building AI-Ready Cybersecurity: A Strategic Framework

As artificial intelligence becomes more embedded in our daily operations, the way we approach cybersecurity must evolve. Traditional security measures are no longer sufficient to protect the dynamic and autonomous nature of modern AI systems. To navigate this new landscape, organizations need a comprehensive strategy that addresses the unique challenges posed by AI. Here’s a breakdown of five essential pillars to guide your cybersecurity efforts in the age of intelligent automation.

1. Integrate Security Throughout the AI Lifecycle

Security isn’t a one-time setup; it’s an ongoing process that should be woven into every phase of your AI systems, from data collection to deployment and beyond.

• Data Integrity: Ensure that the data feeding your AI models is accurate and free from tampering. Implement validation checks and monitor for anomalies that could indicate data poisoning attempts.
• Model Training: Incorporate adversarial training techniques to make your AI models resilient against malicious inputs designed to deceive them.
• Deployment Oversight: Once deployed, continuously monitor your AI systems for unusual behaviors or unauthorized access attempts. This proactive approach helps in early detection of potential threats.
• Operational Resilience: Develop adaptive response mechanisms that allow your systems to quickly contain and recover from security breaches, minimizing potential damage.
  

2. Adopt AI-Powered Defense Systems

As cyber threats become more sophisticated, leveraging AI to bolster defense is not just beneficial, it’s essential.

• Real-Time Analytics: Utilize AI-driven platforms that can analyze vast datasets instantaneously, identifying subtle anomalies that might escape human detection.
• Predictive Threat Modeling: Implement AI systems capable of anticipating potential threats, allowing you to strengthen defenses before vulnerabilities are exploited.
• Automated Incident Response: Speed is crucial in mitigating cyber threats. Automated systems can execute response protocols swiftly, reducing the window of opportunity for attackers.

Organizations adopting AI-enabled Security Operations Centers (SOCs) consistently report fewer security incidents, minimized breach impacts, and significantly faster threat mitigation.

3. Establish Robust Governance and Risk Management

Effective governance ensures that your AI systems operate within defined ethical and regulatory boundaries.

• Technical Oversight: Routine audits, penetration testing, and independent evaluations to maintain security posture.
• Ethical Frameworks: Establishing transparency, accountability, and ethical guidelines for AI use, fostering trust among stakeholders.
• Compliance Management: Stay abreast of evolving regulations like the European Union’s AI Act and frameworks such as the NIST AI Risk Management Framework to ensure ongoing compliance.
• Risk Assessments: Continuously evaluate and mitigate risks associated with AI deployments, adapting your strategies as necessary.

A well-structured governance framework not only safeguards your organization but also builds confidence among clients and partners.

4. Prepare and Empower Your Workforce

The sophisticated nature of Agentic AI demands enhanced skills and cross-functional collaboration within cybersecurity teams:

• Upskilling Programs: Robust training that bridges cybersecurity, data science, AI development, and ethical considerations, equipping teams to address complex threats comprehensively.
• Cross-Disciplinary Collaboration: Encouraging seamless cooperation between IT, cybersecurity, compliance, business strategy, and operations teams, ensuring holistic security practices.
• Continuous Learning: Establishing structured programs for continual education, staying ahead of rapidly evolving technologies and emerging threats.
  

5. Design a Future-Ready Security Strategy

The Agentic AI revolution is still in its infancy. Over the next decade, proactive, predictive cybersecurity will become mandatory. Strategic leaders must transition from reactive defense to a comprehensive predictive risk management model that embraces:

• Predictive Analytics: Leveraging AI to anticipate threats, rather than merely responding to them after the fact.
• Agile Response Models: Developing flexible cybersecurity frameworks capable of adapting quickly to new, unforeseen threats.
• Integrated Security Ecosystems: Merging technology, human oversight, ethical governance, and predictive intelligence into a unified cybersecurity approach.
  

Conclusion: Securing Your AI-Driven Future

The rise of Agentic AI presents both unprecedented opportunities and complex cybersecurity challenges. As these intelligent systems become integral to business operations, it’s imperative for organizations to proactively adapt their security strategies. By embedding robust security measures throughout the AI lifecycle, leveraging AI-driven defense mechanisms, establishing comprehensive governance frameworks, and fostering a culture of continuous learning, businesses can position themselves to not only mitigate risks but also to thrive in this new era.

At Aventior, we are committed to guiding organizations through this evolving landscape. Our expertise lies in developing tailored cybersecurity frameworks that address the unique needs of AI-driven environments. If you’re ready to fortify your organization’s defenses and embrace the future of intelligent automation securely. Let’s work together to build a resilient and secure AI-powered future.

To know further details about our solution, do email us at info@aventior.com.

 

The post Navigating the Agentic AI Era: A Cybersecurity Guide for IT Leaders first appeared on Aventior.

Clinical research platforms have gained significant importance due to the critical role they play in helping contract research organizations, regulators, and sponsors gain visibility and understand the outcomes of critical clinical trial data. With COVID-19 continuing to surge in multiple regions around the globe, platforms that digitize data collection for studies not only help to bring efficiencies to the process but are also critical in ensuring clinical trial participation rates remain healthy while much of the population continues to practice physical distancing.

Traditionally, clinical studies involved patients physically visiting clinics and filling out paper surveys and questionnaires. Observers would need to trust that patients remain in compliance with the study. Observation data would be recorded on paper and then transcribed into a database for analysis. The challenges and risks with this are significant – inaccurate or misleading outcomes in clinical studies can have serious public health implications. In times of a pandemic, it can be near impossible to find patients willing to consent to regular office visits, and it may be physically impossible to do so while maintaining adequate social distancing measures.

Aventior was hired by a clinical research automation company to tackle this problem by completely digitizing the clinical trial data collection process. The vision was to help institutions unlock the power of their decentralized studies, allowing sponsors and CROs to remotely capture data from participants and sites during, in between, and in lieu of in-clinic visits – securely through one unified platform.
Aventior’s experience in developing technology for the life sciences industry would make them a leading candidate for this effort.

Approach

There are multiple components that must come together to deliver a centralized clinical study platform. The functionality and architecture need to be such that it can deliver a fit-for-purpose solution built to support the needs of any individual study.

In this case, the client was interested in utilizing serverless technologies as the base platform architecture. This would allow development teams to focus more time on the business logic and less on hosting and server maintenance – the net result being time savings and the ability to launch new features more frequently. To support this need, Aventior developed the application using open-source, technology hosted on Amazon Web Services with scalable infrastructure.

Compliance with regulatory bodies (GCP and 21 CFR Part 11) was another critical component of the platform. Due to the sensitive nature of the information being collected and stored within the platform, robust documentation and testing procedures were needed to ensure adequate privacy and security were built into the application.

With speed to market being an important factor raised by the client, Aventior utilized agile development methodologies, multi-shift operations, and AWS cloud features for fast integrations, to deliver the project within a matter of months.

Impact

With clinical trials being a key mechanism for how modern society advances its practices in healthcare diagnosis, prevention, treatment, and therapy, the impact of technological advances in this field is profound. Clinical trial data helps to inform our scientists and doctors in their decision-making, and the data itself must be accurate and representative of the individuals who will use the new therapies or approaches. Digitizing the clinical trial and information gathering process not only improves the efficiency of trials, but it also helps to improve the accuracy of trial outcomes.

Protocol compliance by participants is an important aspect of clinical trials. Without this, outcomes may be misleading, and results can be skewed. Electronic reminders/alerts, context-sensitive messaging, and compliance feedback help to ensure participants stay in compliance with program protocols.

Patients using ePRO reportedly demonstrate protocol compliance as high as 94%, compared to 11% with paper.[1]

Transcription errors are a risk with any manual paper-based process, and the impact of errors can be severe when dealing with clinical trial data. Paper-based studies would require site personnel to manually transcribe data into the trial management system.

With eCOA, any inconsistencies, missing data, or data quality issues can be caught and corrected in real-time.

Regulatory bodies have strict recommendations around data collection techniques and methods to ensure the data collected is reported according to protocol requirements. eCOA inherently meets regulatory quality guidelines due to it being Attributable, Legible, Contemporaneous, Original, and Accurate (ALCOA).

Adoption of eCOA can help to reduce the number of queries by regulators regarding the capture of clinical trial data. 

 The overall user experience for participants improved drastically both due to increased accessibility and convenience. Whether patient-reported observations or clinician-reported observations, participants can avoid unnecessary travel by leveraging electronic surveys and virtual visits. 

Patients leveraging eCOA saw significant savings in personal time that they would otherwise spend on visiting clinics and doctors. 

The improvements in user experience contributed to increases in retention rates. Participants engaging in studies using eCOA were more likely to complete the study due to reduced frictions throughout the entire process.

Patient retention improved by 30% on average as a result.

 These factors ultimately helped to improve the overall efficiency and effectiveness of studies, making eCOA the logical approach for future studies to come. 

Researchers have experienced a reduction in clinical trial time and costs, leading to both improved economics and speed.

 By working with Aventior, the client has been able to deliver on their vision of helping institutions unlock the power of their decentralized studies, through a secure platform that digitizes clinical studies data collection and provides outcome visibility in real-time.

The post Digital Clinical Research Platforms first appeared on Aventior.

Digital pathology driving medical breakthroughs and a new set of challenges

Digital pathology is rapidly becoming the new standard of care, thanks to recent approvals from the Food and Drug Administration (FDA) for applications such as primary disease diagnosis.

Regarded as the bridge between science and medicine, pathology involves teams of scientists and medical staff studying biological samples to understand drivers of illnesses and diseases. It plays an important role in investigating the effects of new drugs as well as understanding the characteristics of viruses and bacteria.

The use of modern technology in pathology has not only improved the efficiency, precision, and granularity at which biological specimens can be captured, it now enables a more automated means for how the metadata associated with specimens is extracted, analyzed, and stored. Computer algorithms can drastically accelerate our ability as a species to identify, prevent, and treat factors in our environment that can harm us.

“This convergence of advanced imaging, automation, and powerful analytics like natural language processing (NLP), machine learning, and artificial intelligence (AI) in healthcare and life sciences organizations are bringing together the tools needed for scientists and clinicians to unlock medical breakthroughs at a pace like never before,” says David Dimond, Chief Innovation Officer Global Healthcare & Life Sciences at Dell Technologies.

Advancements in digital pathology have create a new set of challenges however as researchers begin to adopt new digital workflows. The shift to digital workflows means specimens that were captured using older technologies would require conversion into a digital format in order to remain useful.
Aventior’s experience in developing and implementing technology for companies in the life sciences industry made us a perfect fit to solve the challenges faced by a Massachusetts-based biotechnology company developing gene therapies for severe genetic disorders and cancer.

Over the course of the years, the company had collected tens of thousands of biological specimens as part of their research. Tissue-based studies generate large amounts of histology data containing biological information in the form of imagery and metadata. These digital pathology slides are labeled using text for their identification, and older technologies used printed or handwritten labels for specimen labeling.

As such, it becomes virtually impossible for pathology teams to quickly search and find specimens they are looking for or categorize them based on tissue, disease, markers, and other attributes. Certain specimens can be extremely difficult to come by, and the ability to access archived specimens is critical in this field.

Aventior worked with the client to address this challenge by developing a solution that utilized a combination of techniques that would allow them to efficiently capture, store, and access biological specimens for analysis.

Approach

AI used in label extraction for digital pathology

With tens of thousands of slides stored in various formats and handwritten identification, extracting and organizing the information efficiently would not only require a series of steps, it would also involve the use of image processing techniques, artificial intelligence (AI), and optical character recognition (OCR).

Based on the requirements at hand Aventior developed a solution which would include four key steps:

1. The first step involves extracting label text from files stored in Mirax file format. It scans all the data files (.dat) to find the data file that is associated with the label file. The associated data label file is converted to a raw PNG image.

2. These raw PNG files are then process for better text extraction which includes image rotation, image enhancement and image thresholding modules. The platform would also perform the morphological transformations like erosion and dilation on the text if needed.

3. The text from the processed image would then be extracted using OCR techniques.

4. The extracted text would then be appended in the user defined structure data format, stored in a database with search capabilities, where a manual validation may be conducted to verify the quality of the extracted data.

A proof of concept was first developed to test the approach. The test involved the processing of 1000 slides with an Excel file as the output. Once validation of the platform had been completed, additional functionality was developed to support the output of the data directly to an SQL database.

Using this approach, Aventior was able to design, develop, and launch the platform to the client within the time span of a couple of months.

Impact

More value and efficiency from existing specimens

Aventior’s AI-based automated label extraction platform quickly allowed the client to enhance their research capabilities. Pathology teams saved time by avoiding having to manually review specimen slides, which meant more time could be spent analyzing data and gaining important insights. 

Processing times were reduced by 80% compared to the manual process.

In the manual process, the information would be stored in disparate locations, making it difficult to identify outliers and trends. Through the use of the platform, data is now being stored in a much more harmonious manner, enabling teams to find and analyze data faster.  

Data harmonization helps users to identify outliers and trends in a much more efficient manner.

Another benefit that had emerged was improved accessibility to the data, as researchers no longer had to be in the same physical location as the slides to access the information. This allowed pathologists to extract additional value out of the archived specimen data as information could be shared more rapidly across a wider range of applications.

Easy search and access of the datasets support further research and analytical activities.

At Aventior, we believe the use of AI in healthcare will continue to accelerate medical breakthroughs. As more healthcare centers continue to adopt the use of digital pathology, one can only imagine what new capabilities will be unlocked in future years to come – and that is why our company is committed to supporting the adoption of AI and machine learning in healthcare and life science industries.

The post AI-Driven Label Extraction: Transforming Digital Pathology first appeared on Aventior.

Ability to integrate a configurable Analytics Dashboard within the portal.

A dashboard allows viewing insights and understanding performance at a glance. The insights that we view on the dashboard are based on carefully identified key performance metrics as per an organization’s objectives and processes. Dashboards are a reporting tool that helps businesses in monitoring their performance and make informed decisions regarding their business.

An analytics dashboard is a reporting tool that helps in analyzing huge volumes of data, that allows investigating trends, get deeper insights and forecast outcomes. Therefore, the data that is used by the analytics dashboard has to be up-to-date and accurate.

It is rightly said by the former CEO of HP, Carly Fiorina that, “The goal is to turn data into information, and information into insight”.

The raw data is converted into information and the dashboard presents this information that can be consumed by the user. The user can use the information to study the performance and get in-depth insight to take calculated action. If management can make informed decisions then they can focus on areas where attention is required without any guesswork.
Every organization has its own set of objectives and goals to achieve. An analytics dashboard helps in understanding and planning how to achieve their goals. Also, the company must get to view their data more interactively, have the ability to drill-down further to get more clarity and identify trends.

Aventior has extensive experience in developing technology for the life science industry. A clinical research company hired Aventior to develop a configurable analytics dashboard to gain insight, view key performance indicators (KPI), and report on the site/study/participants.

Approach

The client has an extensive amount of information vital to the customers. Aventior had to develop a dashboard that provides a mechanism to control the display of the new study dashboard for each product as well as customer instance with answers to relevant business questions. The dashboard should also allow the user to configure widgets of visualization across various KPIs. To integrate these functionalities, the technology used by Aventior is Vue.js for the front end, which is an open-source JavaScript framework for developing user interfaces, and for the backend, JAVA integrated with Node.js.

The dashboard developed was in two phases:

Phase 1.0 – Base-Standard Dashboards

Phase 2.0 – Pro-unlock additional dashboards/configurable dashboards

Aventior used the calculation code for each priority grouping KPIs. Aventior team tests and fixes all the KPI visualization bugs and deploys the code to stage/UAT/Prod environments.

Impact

Developing an analytics dashboard involves creating a user-friendly platform, allowing users to configure widgets of visualization, and representing KPIs in a glance. Aventior developed a dashboard that allows users to configure widgets of visualization based upon multiple roll-ups and slices of data across various KPIs. The platform provides a mechanism to control the display of the new study dashboard for each product and customer instance. The users with appropriate access rights to use the portal can view the dashboard. They can view the key performance indicators (KPIs) through interactive visualization, that is displayed on a configurable dashboard. The users can gain insight and reports of their study, site, or participants. The users can configure the types of visualizations, display the visualizations, and download the data generated from the Metrics Dashboard KPIs.

Aventior has developed a configurable dashboard that gives users the ability to access a group of KPIs. The KPIs are organized around a few relevant business questions that the dashboard metrics address. The dashboard groupings that address various concerns are as below:

1. Overall Study Executive Dashboard: Does it predict the health of a study?
   A grouping of high-level metrics related to enrollment, compliance, and data completion is represented by this dashboard. It provides insights such as:
   • Determine the expected completion date for all activities
   • View in a glance enrollment curves by country and site
   • Identify daily activities that cause non-compliance
2. Study Enrollment Dashboard: Does it display target achieved and forecast enrollment goals?
   This dashboard represents a grouping of detailed analytics that represents enrollment trends. at a glance, the below trends can be viewed both country-wise and site-wise:
   • enrollment curves
   • site activation and first visit progress
   • eConsent comprehension quiz metrics
   • review screen failure ranking
3. Activity and Overall Engagement Dashboard: Does it help find out if any user is struggling with any particular activity areas in the technology?
   This dashboard represents a grouping of detailed analytics representing engagement trends. This includes:

• • Identifying any area in particular with different engagement levels by site and country
  • Find if telehealth visit durations are adequate for the study
  • If users are spending a consistent amount of time in the app by site and country
  • Identify statistical impact in activities and training across countries and sites

4. Study Compliance Dashboard: Does it identify current or predicted compliance issues?
   This dashboard represents a grouping of detailed analytics representing potential compliance issues and trends. It helps identify:

• • Daily activities that are causing non-compliance
  • Data collection completion metrics to see if data entry is lagging

5. Data Management Dashboard: Can it identify the status of data collection, cleaning, verification, and approval?
   This dashboard represents a grouping of detailed analytics representing data management statuses and trends, such as:

• • Activity completion percentages
  • Activity completion times
  • Issues and trends involving data queries
  • Timeliness of queries being closed
  • Timeliness of activities and forms being entered, verified, approved, and signed off

6. Study Completion Dashboard: Can I view trends that may impede study completion goals?
   This dashboard represents a grouping of detailed analytics representing completion trends. They are:

• • View the expected completion date for all activities
  • Spot discontinuation trends
  • Review missing data and query trends by site and overtime

Aventior developed an Analytics Dashboard for the client that is integrated into their portal. The configurable dashboard answers all relevant questions which arise after the study configuration.

The post Analytics Dashboard first appeared on Aventior.

Chemical supply chains forced to adapt to new expectations

As global commerce continues to make its migration from “the main street” to “mainframe”, organizations across all industries have been investing vigorously in ways to digitize their operations. eCommerce has become a norm in everyday life for most consumers, and as expectations for the same frictionless experience continue to make their way towards other industries, the chemicals industry has been no exception.

The next generation of purchasing managers increasingly values speed, transparency, and ease of doing business above other factors. Despite this fact, eCommerce adoption in the industrial B2B setting has been evolving at a much slower pace when compared to industries that sell directly to consumers. Industrial manufacturing companies are challenged with the changes needed to existing habits, processes, and systems, to adapt to the evolving expectations.
A few years ago, one company that had been manufacturing and distributing high purity chemical products for over 25 years

recognized that the traditional way of doing business was no longer able to keep up with the evolving needs of customers and suppliers.

Too many links in the chemical supply chain, combined with manual processes along the way, not only prevents companies from delivering a better customer experience, but also limits the visibility and insights that manufacturers need to optimize inventory and production.

What was needed was a truly digital point of sale and supply chain experience for all stakeholders involved, capable of providing real-time pricing, inventory, and demand signals.

To stay in front of the competition, the company asked Aventior to help design, develop, and launch the first truly digital marketplace for the chemical industry.

Solution

Combining scalability with industry-specific needs

Over the course of 10 months, Aventior had completed the design, development, and launch of the industry’s first truly digital eCommerce marketplace for the chemical industry. With scalability and user experience at the forefront, the platform also needed to incorporate specific features and functionality to support the chemicals industry.

Third-party connectivity was another critical component of the platform – it had to be capable of sending and receiving information seamlessly with external systems to deliver an end-to-end digital experience, whether it be with logistics providers, suppliers, buyers, and other third-party tools. The platform needed to support REST and SOAP web services, as well as other more traditional file transfer techniques.

Aventior collaborated with the client to finalize all the critical components for the platform. Once features, functionality, and UX designs were established, Aventior conducted an assessment to assist the client in finalizing the platform architecture and solution approach.

Based on the assessment, the client had settled on the following hybrid approach:

• Leverage a world-class eCommerce platform such as Magento (Enterprise Edition).
• Customize the platform to deliver the required features and functionality.
• Develop third-party integration capabilities to support the required connectivity.
• Platform hosting on AWS to provide unlimited server capacity and reliable encryption and security.

The solution agreed upon would enable the team to focus on developing innovative features and functionality that would sit on top of an already robust foundational architecture, which drastically reduces the time and cost for delivery.

Using this approach, Aventior was able to deliver the product on time, on budget, and on spec.

Impact

Delivering a truly connected chemical supply chain  

With the launch of the first (truly digital) eCommerce marketplace for chemicals, the client was able to rapidly onboard new suppliers and products within the first few weeks of launch.

Customer experience improved dramatically for buyers. Suppliers saw transaction velocity for purchase orders reduced drastically from multiple days to less than 15 minutes. Repeat sales increased. Suppliers now had the ability to obtain real-time demand signals, enabling them to be smarter about inventory and pricing.

By working with Aventior to bring their vision into a reality, the client has been able to establish themselves as a leader helping chemical manufacturing companies improve sales and profitability with a 21st-century approach.

The post Digital Marketplace for Chemicals & API first appeared on Aventior.

Mobile Application for part-time workforce management

Gyggs is an on-demand platform for small business owners, managers and part-time workers who reside in Massachusetts and surrounding states to post and apply for job opportunities. The platform allows small business owners from the service industry including, the hotel industry, laundry services, groceries, convenient stores, and liquor deliveries, to look for part-time workers who can work in shifts.

Approach

The mobile platform is designed as a native iOS and Android mobile application with persona and role-based design. Business owners can create a job requirement in the mobile platform. Those looking for opportunities can look for work opportunities by searching on the Gyggs platform. They can create their profile; to easily apply for the job in their desired location.

Impact

Easy access to find a professional through online location services

The mobile application can be leveraged by the business owners to publish a job opportunity or a gig to find the right professional. Professionals can easily search for gigs posted by business owners through location services. They can apply for these opportunities or gigs using their smartphones. The platform is easy to access and use by both parties – business owners as well as job seekers. It is designed to bring efficiencies and improve profitability for the service industry. The easy access and availability of professionals at the fingertips solve the challenges in the availability of labor.  This platform helps in creating wonderful opportunities for part-time employment with the flexibility of work hours/days. The business owner can enter the hours and time for which they need a professional. The professional can apply for the gig if they are happy with the time and close the deal.

Aventior has developed an easy-to-install and use platform for busy business owners and professionals. It is available globally on Android as well as on iOS for download. Users have to simply register on the platform and are ready to find the gig they would love to do.

Features of the Gyggs platform

Simple Onboarding – The onboarding process is simple and automated. The owner or manager can post their requirements, including the gig details, hours required, and the hourly rates of the gig.

Smart Matching – The algorithm checks for labor requirements mentioned by the business owners, and it is matched with the skills of the professional. The algorithm finds a suitable match, and the gig is displayed to the professional. The professional who accepts first will get the gig.

User Friendly – The platform is easy to use. It gives an awesome user experience.  

Full Automation – The platform is completely automated, and users can have a stress-free experience for onboarding, matching the right profiles, and pay settlements.

Popularity of the Gyggs Platform

The platform is live and is used by professionals and business owners and managers to hire part-time employees. The success of this platform is, it has a great user experience and its stress-free management of on-boarding and payment activities. The platform was initially open to users in Massachusetts; however, its popularity has led Aventior to cover the surrounding areas too. Aventior is moving in phases to make the platform available in other areas.

The post Smart Solution for Part-Time Workforce Management first appeared on Aventior.

Opportunity

Data Collection, analysis, and reporting of road crashes to improve policy and road safety

As an initiative to study where and how road accidents occur, Aventior in partnership with World Bank and other global leaders developed a platform. This platform collects and analyzes data about the accidents that occur at a particular place. The platform collects data on a real-time basis from accidents as well as road crimes. The data collected by this platform can be accessed by the municipal, state/provincial, and national agencies. The data collected by this platform can be used by various state and national agencies for the improvement of policy, budget allocation, decision making, engineering solutions, deployment of enforcement, and road safety interventions. 

Approach

For the DRIVER 2.0 program, Aventior planned an on-site visit to the World Bank’s office in Singapore and its offices in Manila, Philippines. The preliminary discussions we held to define the objective and its implementation. Through this face-to-face interaction with the World Bank’s core team gave Aventior invaluable inputs to understand the current issues and to enhance the platform accordingly. 

The initial stage of this project was amidst Covid-19. All countries including India were fighting the repercussions of Covid-19. Most of the companies had started working remotely. Team Aventior worked from India and Team World Bank had teams working from 3 different continents. Despite different time zones, the efficient teams managed to work judiciously and delivered the project on time. Timely demos of the project DRIVER 2.0 were also conducted. Team Aventior presented the demos in the Ivory Coast as well as to the iRAP team.

Team Aventior overcame the hurdles caused due to Covid 19 and delivered the project DRIVER 2.0 and deployed it in various countries all this while working from home.

DRIVER 2.0 has been deployed in countries like the Philippines, Mumbai, India, and various regions in the Asia Pacific. 

Aventior architected and designed this platform on a best-in-class web portal, which is designed to scale for global implementation. This platform is being migrated to a new technology stack to improve the scalability and availability of the data. This platform is developed using Python 3.x as backend technology and Angular 8.x as frontend technology. 

It has been implemented with iRAP to integrate with iRAP star-rating data and the iRAP toolkit API. To implement the iRAP star-rating data on version 2 of the platform’s map, Aventior coordinated with the iRAP team and used the SDKs provided by them. The SDKs have the APIs to fetch the iRAP star-rating data. The SDK is then integrated into Python Django and created the APIs, which were then used in Angular to show the iRAP data on the map. 

Aventior has designed and implemented new UX screens including Android and iOS apps.

This project’s total engagement has been 18 months and Aventior has successfully delivered Phase -1 and is now working on phase – 2. 

Impact

Reducing Road fatalities to zero around the globe

A GRSF initiative to reduce road fatalities to zero around the globe by 2030, Aventior in partnership with World Bank had undertaken this project to develop and implement a platform to collect data from accident spots and provide them to the authorities for further decision making. The cloud-based web and mobile platform is designed to collect, store, and integrate road accident and crime data. This platform caters to country-wise localization including Brazil and some of the Asian countries including Laos, the Philippines, Vietnam, and India. 

This platform will allow authorities at the state government and national agencies to make favorable decisions to make roads safer for vehicle owners. The aim to reduce road accidents to zero is an ambitious project and it can be achieved with the right amount of information on a real-time basis. The data will help in making decisions to improve traffic control in the busiest streets, awareness of dangerous intersections, narrow lanes, and curves. 

Aventior has delivered all milestones of this project on time. The opportunity to work with the finest core team of World Bank is an experience in itself. Team Aventior and Team World bank successfully converted this ambitious project into a reality. 

The post Driver 2.0 – Application for Road Safety first appeared on Aventior.

In the age of information, where do we keep all the data

Data storage is a critical aspect of any business. In a digital world, data is arguably one of the most important assets that a company has. Businesses of all sizes are faced with an increasing amount of data, and it is not only attributed to economic growth – companies are constantly discovering new ways to leverage data to refine their products, services, and analyze customer behavior, which means more data needs to be both captured and stored. More than 63% of enterprises store 100TB of data or more and the number continues to grow.

A good storage management solution serves multiple purposes. It allows businesses large and small to simplify data management, maximize server performance, back up critical data, and increase application availability. For this reason, the use of storage area networks or SAN remains common when it comes to enterprise data storage management solutions.

SANs are essentially dedicated networks connecting data storage tools with larger networks, facilitating the pooling of storage among data centers. This simplifies the management of data storage, through centralization, while offering the benefits of a distributed system. SAN differs from cloud storage in the sense that SANs are hosted within a company’s firewall, whereas cloud storage is hosted through virtual servers on the internet. Greater control over security has kept SAN the preferred option for many companies. However,

companies that specialize in enterprise storage solutions have now caught on to the potential benefits of integrating locally hosted storage appliances with the benefits offered by cloud storage solutions.
A Santa Clara-based company specializing in data storage solutions recognized the need to invest in this capability. The company engaged Aventior to assist with the development of a prototype solution that would offer its customers the ability to virtualize data storage and management of their physical devices while maintaining total control over hosting and security of the appliances themselves.  

Approach

Integrating local storage appliances with the scale of the cloud

The solution developed by Aventior would utilize a combination of AWS CloudFormation and AWS IoT Greengrass to create a dynamic and infinitely scalable architecture to support the desired functionality.

AWS CloudFormation allows the use of programming languages in the modeling and provisioning of the resources needed for applications. This enables a single source of truth for AWS and third-party resources.

AWS IoT Greengrass seamlessly extends AWS to edge devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. Data could be transmitted back and forth from appliances to both EC2 instances and S3 storage buckets, allowing the application to create “digital twins” of local storage appliances that live in the cloud.

A custom user interface was also needed to enable the management of local appliances and the integrated cloud-storage functionality. 

By combining these technologies, Aventior was able to develop a prototype for the client in a span of 2 months. The prototype would allow physical storage devices to distribute data storage to AWS while providing a virtual interface to facilitate the management of the devices.

Once the prototype was proven to deliver the desired functionality, it was then further enhanced to support portability to other cloud services, such as Microsoft Azure and Google Cloud. This enabled greater flexibility for customers that had a specific preference with respect to the cloud hosting provider. 

Impact

Accessibility and scale while maintaining control and security

As demand for enterprise data storage continues to grow alongside a desire for increased accessibility, the solution developed by Aventior would allow the client to better align their product offerings with the needs of customers.

Companies large and small could now benefit from the security and control that a local storage appliance offers, while still achieving the scalability of cloud-based data centers.

Because appliances are hosted within a company’s network, behind their firewall, companies can maintain total control over security while at the same time reaping the benefits of infinitely scalable cloud-based data centers.

Prior to the new solution, clients could monitor the health of storage appliances online but could not start and stop services remotely, making it difficult to conduct important maintenance tasks such as installing new patches or conducting software updates.

The new solution would allow companies with distributed storage appliances to centrally manage critical maintenance tasks such as software updates and patch installations without the need to be physically present, saving them time and money.

Remote access also meant companies could act faster and be more coordinated in the management of storage appliances. This is critical for companies with servers based in locations where there is a higher risk of natural disasters.

Enterprise clients with a global footprint could now manage their devices at any site at the “flip of a switch” from virtually anywhere, resulting in faster turnaround times both for maintenance-related or disaster-related events.

With data being regarded by many as perhaps the most important commodity of the 21^st century, companies are continuously investing in better ways to protect this critical asset. By working with Aventior, the client was able to develop a solution that would help organizations around the world to protect and scale their data storage in the most efficient and effective manner possible.

Aventior specializes in the integration of modern IT techniques with enterprise needs to support growth, innovation, and development across all major sectors. Utilizing our expertise in IoT, artificial intelligence, geospatial solutions, and data science, we help companies unlock new capabilities and work with them to turn vision into reality. Contact us today to see how we can help you achieve your goals.

The post Scalable & Secure Data Storage Integration for Global Enterprises first appeared on Aventior.