Tag: workflows

Dell Technologies brings data centre-class AI to the desktop with Dell Pro Max with GB10

Dell Technologies has today announced the availability of Dell Pro Max with GB10, a new desktop system that makes it easier for anyone building AI tools to do it right from their desk. Capable of handling massive AI models, the new Pro Max with GB10 uses NVIDIA’s Grace Blackwell chip and comes with 128GB of unified memory and up to 4TB of storage to support models with up to 200 billion parameters.

The system is designed to remove long-standing barriers in AI development, allowing Irish research teams, startups, regulated industries, and individual creators to train, fine-tune, and deploy advanced models locally—without relying on cloud solutions or compressing models to fit hardware limitations. Dell Pro Max with GB10 comes pre-installed with key AI tools such as CUDA, JupyterLab, Docker, and AI Workbench, enabling teams to start building in minutes.

By bringing this level of performance directly to the desktop, Dell Pro Max with GB10 transforms how AI work is done. Academic researchers at Irish universities and institutions can test hypotheses and adapt models rapidly, accelerating discovery. Startups gain enterprise-grade computational power without heavy infrastructure investment, allowing small teams to prototype, validate, and scale AI projects efficiently. Regulated industries can deploy secure AI workflows on-premises, protecting sensitive data while maintaining performance on par with leading cloud solutions. Independent creators and developers across Ireland can now have the ability to develop sophisticated AI models from their own workspace, democratising innovation.

“Human ingenuity fuels AI progress, yet most teams hit hard limits on computation well before reaching their creative potential,” said Charlie Walker, Senior Director and GM, Dell Pro Max and Pro Rugged Products. Dell Pro Max with GB10 empowers customers to advance securely, accelerate insight, and innovate on their own terms. This isn’t just another workstation; it’s an AI accelerator for real-world AI challenges, built for those who won’t let limits define what’s possible.”

The Dell Pro Max with GB10 is designed with scalability in mind. For teams requiring even greater power, connecting two systems creates a single node capable of handling 400 billion-parameter models, showcasing Dell’s approach to scalable AI infrastructure.

By removing computational constraints and simplifying AI development, Dell Pro Max with GB10 enables faster innovation, more secure workflows, and broader access to advanced AI technology. This initiative demonstrates Dell Technologies’ commitment to empowering creators and organisations and supporting the growing AI ecosystem across Ireland to push the boundaries without compromising.

For morei information on the new Dell Pro Max with GB10, visit: www.dell.ie

Meet Amazon Quick Suite: The agentic AI application reshaping how work gets done

Quick Suite helps you cut through the noise of fragmented information, siloed applications, and repetitive tasks to focus on what matters.

Key takeaways

  • Quick Suite is AWS’s agentic AI application that helps employees transform how they find insights, conduct deep research, automate tasks, visualize data, and take actions across apps.
  • Quick connects to your information across internal repositories like wikis and intranets, popular applications, AWS services like S3 and Redshift, and access integrations with MCP to connect to 1,000+ apps.
  • Ask any question and get insightful answers.
  • Battle-tested by tens of thousands of Amazon employees and dozens of customers, you can use Quick for tasks consumer AI shouldn’t handle.

Read more below

We’ve all experienced how AI can transform our personal lives, but this same experience hasn’t been unlocked at work—yet. Consumer AI solutions aren’t connected to all your business data. They don’t have access to the tools you need to get things done at work. And many organizations won’t even let you use consumer offerings, because they lack critical security and privacy features.

That’s why we invented Amazon Quick Suite. It’s the AI experience people love with the security and privacy enterprises trust. Quick is your AI teammate that collaborates with you to get work done. With Quick, you can ask questions and get detailed answers, conduct deep dive research, analyze and visualize data, and create automations for workflows to save time and let you focus on the big picture. And thanks to the enterprise-grade security and privacy standards, Quick can work across all your information, so you finally get the fully featured gen AI experience you want at work, while knowing your queries are never used to train a model.

With Quick, we are entering a new era of work. Interact with Quick through an intuitive, web-based experience or integrations across your browser, Office 365, and more. Working with an AI agent is now as simple as chatting with a teammate. Make a request, ask a question, or automate a task. Quick works with you to help you go from insight directly to action. To see these capabilities firsthand, watch my video overview of Amazon Quick Suite.

We’ve been testing Quick with employees across Amazon and key customers to ensure it’s up to the demands of today’s workplace, and the results speak for themselves. Amazon employees are turning tasks that used to take days into minutes, automating the development of critical reports, and building their own benches of personalized agents. Propulse Lab, a leading marketing automation company, used Quick to streamline their customer service workflows, reducing the average time spent handling tickets by 80%—with a planned expansion of this workflow, they predict they will save over 24,000 hours annually. Based on the results they’ve already seen with Quick, DXC Technology, a global provider of information technology services, is planning to deploy it across more than 120k users, while Vertiv, a provider of critical digital infrastructure, plans to scale their users by more than 25% in 2026.

So how does Quick Suite work?

Bring everything together with Quick Index and Spaces

Quick Index makes it simple for you to connect to the sources and applications that matter. With over 50 built-in connectors for applications like Adobe Analytics, SharePoint, Snowflake, Google Drive, OneDrive, Outlook, ServiceNow, Databricks, Amazon Redshift, and Amazon S3, Quick brings together all your data securely to ensure you have full context for every decision. Using integrations with OpenAPI or Model Context Protocol (MCP) customers can connect to custom resources and 1,000+ apps by taking advantage of popular MCP servers from Atlassian, Asana, Box, Canva, PagerDuty, Workato, Zapier, and many more. You can then add additional files, dashboards, and other information to dedicated Spaces for you and your team to collaborate.

Ask questions and build agents

Once you’ve connected your data to Quick, you can start interacting with the chat assistant. You can ask Quick to write and send communications for you, or if you want Quick to write in your style or for a particular task (like writing a case study), you can use natural language or point Quick at existing guides or documentation to create a custom agent able to communicate in your intended style.

Analyze and visualize data with Quick Sight

Quick Sight makes business intelligence accessible to everyone with a new agentic experience, helping you gain insights to make better decisions. Unlike traditional business intelligence tools that work only with databases and data warehouses, Quick Sight’s agentic experience analyzes all forms of data across all your systems and apps, including your documents.

For example, a marketer can now easily look at a dashboard of their campaign data with metrics and customer feedback and ask questions in natural language about how the campaign is performing. They get a crisp analysis of the data in seconds without hours of manual statistical analysis, compiling sentiment from feedback, and summarizing the findings into a narrative—no business intelligence or data science experience required.

Dive deep into complex questions with Quick Research

Quick Research is the most accurate and reliable research agent on the market, ready to answer your most in-depth questions. It’s like having your own personal Ph.D. to provide comprehensive answers and reports to questions that require extensive research. It uses sophisticated analysis capabilities and extended processing to dive into your company’s data, and the public internet, including real-time information from 200+ outlets like The Associated Press, The New York Times, Washington Post, and Forbes. Quick Research can turn weeks-long research projects into quick-turn results, all with fully cited sources you can trust.

We tested Quick Research on DeepResearch Bench, a comprehensive benchmark for evaluating research agents, using a collective jury, where it provided the most accurate and reliable research across a range of tasks. The Last Mile Delivery team at Amazon used Quick Research to assess the potential impact of new legislation on a particular country that had been previously enacted in other countries. In 30 minutes, Quick Research delivered an in-depth analysis of how this legislation impacted other countries and their associated partner organizations, while also providing details on references and research methodology. This sort of research previously took multiple team members two weeks to complete.

Streamline repetitive tasks with Quick Flows

We all have those routine tasks, like compiling weekly reports or preparing for a recurring meeting, that take up your time every week. Quick Flows helps you use simple prompts to create automated workflows that handle repetitive tasks, reducing errors and freeing you and your team from busy work. For example, a program manager at AWS created a Flow to report on new, in-progress, and closed Asana tickets from the past week, compare them against the previous week’s status and committed items, and generate an executive summary email for leadership, saving multiple hours of manual work each week.

Handle complex multi-system workflows with Quick Automate

When these processes get complex and require hundreds of steps to be securely executed across multiple enterprise systems, like insurance claims processing or onboarding a new employee, teams wish that these tasks could be streamlined, but they lack the sophisticated automation tools and expertise to do it. With natural language prompts or by simply using existing documentation for their standard operating procedure, Quick Automate coordinates even the most complex business workflows across multiple applications, systems, or departments.

For instance, the Amazon Finance team uses Quick Automate to reconcile thousands of invoices every month. Quick Automate pulls information across multiple external transportation management systems, cross referencing this content with internal data from Amazon systems to help teams forecast cashflow, identify payment blockers, and conduct root cause analysis. The team built this automation without a dev team in days instead of weeks, and Quick made it easy to scale across multiple teams. Customers, such as Kitsa, have found the computer use agent in Quick Automate to be the most accurate solution for browser automation, helping them reliably automate their most complex and sensitive workflows across applications at scale.

Quick works wherever you are. With an intuitive web application, extensions in popular browsers like Chrome and Firefox, and extensions in Microsoft Outlook, Teams, and Word, Quick helps you find answers and act immediately in your flow of work.

Quick Suite is already transforming work for Amazon employees and customers

Quick serves people across every department and role—from sales reps to marketers, to CEOs and CIOs, to engineers and IT. Employees across Amazon, along with customers like Vertiv, DXC, 3M, Jabil, dLocal, Propulse Lab, and Kitsa, are already seeing amazing results with Quick:

Research in high gear

Jessica Gibson, vice president and associate general counsel at Amazon, sees an enormous benefit using Quick Research to help the Legal, Public Policy, and Compliance departments keep up with shifting global requirements that impact their business. From a single prompt, Quick Research helps her team synthesize complex requirements for specific geographic regions and provide recommendations at remarkable speed. “This same task used to require many hours of outside counsel, research, and writing,” said Gibson. By using Quick Research to compile these reports, her team can “stay agile while optimizing both time and resources.”

Automations that work

Kitsa, a customer that builds software to help expedite clinical trials, used Quick Automate to pore through hundreds of webpages and found that they were able to analyze sites for clinical trials in days that previously took months—with a 91% cost savings. “Compared to similar offerings like Manus and ChatGPT Operator, we achieved the highest accuracy and data coverage for our use case,” said Rohit Banga, the company’s co-founder and CTO.

Data-driven business decisions

Robbie Wright, a senior product marketer at AWS, uses Quick Flows to build a repeatable workflow to draft monthly business reviews based on business metrics from Quick Sight, campaign performance reporting from Adobe Analytics, and content from emails, and other internal documents. This saves time and helps his team make more informed decisions about ongoing campaigns faster.

“The workflow makes it simple to combine multiple sources into a concise update for our leaders,” Wright said. “I can now complete these projects 90% faster, and the quality of my reports has improved dramatically because I spend less time chasing numbers and more time providing my own insights.”

An AI-driven transformation

Jabil, a global leader in engineering, supply chain, and manufacturing solutions, is embracing Quick so that employees can use natural language to research regulatory updates across key industries faster and to optimize account collections and request for quote (RFQ) submissions. The automations in account collections and RFQs alone are expected to save about $400,000 annually as a result!

“The multi-tier AI architecture powered by Quick consolidates chatbots and information sources, increasing our manufacturing speed and flexibility,” said May Yap, Jabil’s CIO. “As part of our AI-driven transformation, these unified capabilities are helping us drive efficiencies and operational excellence.”

Complex workflows made simple

Natalie Fischbeck works in business development on Amazon’s Workforce Staffing team, and in one week she built 39 customized AI agents using Quick to help her complete complex tasks in minutes.

“Quick has given me the opportunity to create an accessible hub of institutional knowledge that would otherwise be scattered,” she said. “We now have scalable, logic-based agents that track all our leads and solutions at a high level. Because they pull from all our most recent emails and documents, they can provide dynamic updates almost instantly.”

Beyond productivity: A whole new way of working

What strikes me about these examples isn’t just the time saved—it’s how Quick is fundamentally changing our relationship with work. It’s removing the busy work that used to consume valuable time and energy and gives us the time back to focus on what matters. It brings together all the data, metrics, and institutional knowledge you need to make decisions, and helps you act on these decisions to drive outcomes.

We’ve been blown away by all the creative ways people have used Quick so far, and we’re excited to see how others will use it in the future. There are so many possibilities to dig into with these tools, and our team is hard at work finding ways to make them even more useful for customers in the future.

How Belfast’s Educational Voice Is Pioneering AI-Enhanced Animation Production for Enterprise Learning

The intersection of animation technology and business transformation is creating unprecedented opportunities for Irish tech companies

The animation industry is undergoing a technological revolution that extends far beyond entertainment. Belfast-based Educational Voice is at the forefront of this transformation, leveraging cutting-edge animation technologies to solve complex business communication challenges for Ireland’s thriving tech sector. Their innovative approach combines traditional 2D animation expertise with emerging technologies like AI-assisted production, real-time rendering, and data-driven personalisation.

As Irish tech companies scale globally, they face increasing pressure to communicate complex technical concepts to diverse stakeholders—from investors and partners to end-users and internal teams. Educational Voice has positioned itself as the crucial bridge between technical complexity and visual clarity, developing animation workflows that integrate seamlessly with modern tech stacks whilst delivering exceptional creative output. Their Belfast studio has become a hub for animation innovation, attracting tech companies from across Ireland and the UK seeking to transform how they communicate.

The convergence of animation and technology represents more than aesthetic evolution—it’s fundamentally changing how businesses approach knowledge transfer, product demonstration, and user onboarding. Michelle Connolly, founder and director of Educational Voice, observes: “We’re not just animators; we’re communication technologists. Our role is to harness animation technology to solve real business problems, whether that’s explaining complex SaaS platforms, visualising data architectures, or creating interactive training systems that scale across global organisations.”

The Technical Architecture Behind Modern Animation Production

Modern animation production has evolved into a sophisticated technical discipline requiring expertise across multiple technology domains. Educational Voice’s production pipeline integrates cloud-based rendering farms, version control systems, and collaborative platforms that mirror the workflows used in software development. This technical infrastructure enables rapid iteration, parallel production streams, and seamless integration with client systems.

The studio employs JSON-based animation frameworks that allow for programmatic control of animation elements, enabling dynamic content generation based on real-time data inputs. This approach proves particularly valuable for tech companies requiring animations that adapt to user segments, product versions, or market conditions. API integration capabilities mean animations can pull live data from client systems, ensuring content remains current without manual updates.

Render optimisation technologies reduce production timeframes by up to 60% compared to traditional methods. GPU-accelerated rendering, distributed processing, and intelligent caching systems enable Educational Voice to deliver enterprise-scale animation projects within aggressive tech industry timelines. The studio’s technical team includes specialists in shader programming, particle systems, and procedural animation—skills typically associated with game development but increasingly vital for business animation.

Version control and asset management systems borrowed from software development ensure animation projects maintain consistency across large-scale deployments. Git-based workflows enable multiple animators to collaborate on complex projects whilst maintaining creative coherence. Automated testing frameworks verify animation compatibility across devices and platforms, crucial for tech companies deploying content globally.

AI and Machine Learning: Transforming Animation Workflows

Artificial intelligence is revolutionising animation production in ways that particularly benefit tech sector clients. Educational Voice’s advanced animation services incorporate AI tools that automate repetitive tasks, enhance creative possibilities, and dramatically reduce production costs. Machine learning algorithms analyse existing brand assets to generate style guides automatically, ensuring animation consistency with established visual identities.

Neural networks trained on motion capture data enable realistic character animation without expensive mocap sessions. This technology proves invaluable for tech companies creating avatar-based training systems or virtual presenters for product demonstrations. The AI-generated base animations maintain natural movement patterns whilst allowing for creative modification, striking the perfect balance between efficiency and artistic control.

Natural language processing capabilities transform script development and localisation. AI systems can analyse technical documentation and automatically generate animation scripts that maintain accuracy whilst improving accessibility. For Irish tech companies expanding internationally, automated translation and lip-sync adjustment reduce localisation costs by up to 70% whilst maintaining quality across language versions.

Predictive analytics inform creative decisions by analysing engagement data from previous animations. Machine learning models identify which visual styles, pacing patterns, and narrative structures resonate with specific audience segments. This data-driven approach ensures animations achieve maximum impact whilst minimising revision cycles—crucial advantages in fast-moving tech markets.

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Real-Time Rendering and Interactive Animation Technologies

The shift towards real-time rendering engines traditionally used in gaming is transforming business animation capabilities. Educational Voice leverages Unreal Engine and Unity to create interactive animations that respond to user input, enabling personalised learning experiences and dynamic product demonstrations. This technology particularly benefits software companies requiring interactive tutorials that adapt to user proficiency levels.

WebGL implementation enables browser-based interactive animations without plugins, crucial for SaaS companies prioritising frictionless user experiences. These animations can track user interactions, providing valuable analytics about engagement patterns and comprehension levels. Tech companies use this data to optimise onboarding flows and identify areas where users struggle with product features.

Real-time rendering also enables live animation streaming for virtual events and webinars. Instead of pre-recorded content, presenters can manipulate animation elements dynamically, responding to audience questions and adjusting explanations based on real-time feedback. This capability has proven invaluable for Irish tech companies conducting global product launches and training sessions.

The computational efficiency of modern real-time engines allows complex animations to run on mobile devices without performance degradation. This democratisation of access ensures enterprise training content reaches all employees regardless of device capabilities—particularly important for companies with distributed workforces across varying technological infrastructures.

Blockchain and NFT Integration in Corporate Animation

While consumer NFT markets have cooled, blockchain technology offers intriguing possibilities for enterprise animation applications. Educational Voice explores blockchain integration for animation asset verification, ensuring authenticity and preventing unauthorised modifications of critical training or compliance content. Smart contracts can automatically manage licensing and usage rights for animation assets across complex organisational structures.

Decentralised storage solutions provide redundancy and global accessibility for animation libraries, particularly valuable for multinational tech companies requiring consistent content delivery across regions. IPFS (InterPlanetary File System) integration ensures animations remain accessible even if centralised servers fail, crucial for mission-critical training materials.

Tokenisation mechanisms enable granular tracking of animation usage and engagement, providing unprecedented insights into content effectiveness. Tech companies can identify exactly which animation segments drive desired outcomes, informing future content strategies with precision previously impossible. This data granularity particularly benefits companies operating in regulated industries requiring detailed training compliance documentation.

The DevOps Approach to Animation Production

Educational Voice applies DevOps principles to animation production, creating continuous integration/continuous deployment (CI/CD) pipelines that accelerate delivery whilst maintaining quality. Automated build processes compile animation assets, run quality checks, and deploy to distribution platforms without manual intervention. This approach reduces human error whilst enabling rapid updates in response to product changes.

Infrastructure as Code (IaC) principles ensure animation production environments can be replicated instantly, enabling parallel production streams for large projects. Containerisation using Docker ensures consistent rendering regardless of underlying hardware, whilst Kubernetes orchestration manages resource allocation dynamically based on project demands.

Monitoring and logging systems track every aspect of production pipelines, from render times to asset utilisation. This telemetry data informs capacity planning and identifies optimisation opportunities. For tech clients accustomed to data-driven decision-making, this transparency provides confidence in production processes and timeline estimates.

Automated testing frameworks verify animation functionality across target platforms before deployment. Visual regression testing ensures frame consistency, whilst performance testing validates smooth playback across device specifications. This rigorous testing approach mirrors software QA processes, ensuring enterprise-grade reliability for business-critical animation content.

Measuring Animation ROI Through Advanced Analytics

Educational Voice implements sophisticated analytics frameworks that quantify animation impact with precision tech companies expect. Beyond basic view metrics, advanced analytics track micro-interactions, attention patterns, and completion funnels. Heat mapping reveals which animation elements capture attention, whilst session recording shows how users navigate interactive content.

A/B testing frameworks enable systematic optimisation of animation elements. Different versions can be served to user segments with automatic winner selection based on predefined success metrics. This scientific approach to creative optimisation ensures animations continuously improve based on real-world performance data rather than subjective preferences.

Attribution modelling connects animation engagement to business outcomes through integration with CRM and analytics platforms. Tech companies can trace how animation exposure influences conversion rates, support ticket volumes, and user retention. Multi-touch attribution reveals animation’s role throughout complex B2B sales cycles, justifying investment through clear ROI demonstration.

Predictive modelling uses historical animation performance data to forecast likely outcomes for new content. Machine learning algorithms identify patterns linking animation characteristics to engagement metrics, enabling data-informed creative decisions. This predictive capability particularly benefits tech companies planning large-scale animation investments requiring board-level approval.

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Future-Proofing Animation Strategy for Tech Evolution

As technology continues evolving at breakneck pace, Educational Voice helps tech companies develop animation strategies resilient to change. Modular animation architectures enable component reuse across projects, reducing costs whilst maintaining consistency. Parametric animation systems allow for easy updates when products evolve, avoiding complete reproduction requirements.

The studio anticipates emerging technologies like spatial computing and mixed reality becoming mainstream, preparing animation assets that translate across traditional screens to immersive environments. This forward-thinking approach ensures today’s animation investments remain valuable as consumption platforms evolve.

Michelle Connolly emphasises the importance of strategic planning: “Tech companies need animation partners who understand not just current requirements but anticipate future needs. We design animation systems that grow with organisations, adapting to new technologies whilst maintaining creative excellence.”

Educational Voice (https://educationalvoice.co.uk) continues pushing animation technology boundaries from their Belfast base, helping Irish tech companies communicate complex ideas with clarity and impact. As Ireland’s tech sector continues its remarkable growth trajectory, animation emerges as essential technology for maintaining competitive advantage in global markets. The future belongs to companies that harness animation’s power to transform how they communicate, educate, and engage.

 

HighPoint Launching Revolutionary 976TB External NVMe Storage Solution for AI, Media, and Data-Intensive Workflows

HighPoint Technologies is introducing an industry-first, portable, near-petabyte NVMe storage solution. The solution leverages eight Solidigm™ D5-P5336 122TB SSDs in a HighPoint RocketStor 6542AW NVMe RAID Enclosure to deliver an astounding 976TB storage capacity in an ultra-compact high-performance design. This innovative solution is designed to deliver scalable, server-grade NVMe storage for a variety of data-intensive applications, including artificial intelligence (AI), media production, big data analytics, enterprise data backup, and high-performance computing (HPC).

Industry-First Portable near 1 Petabyte Storage Solution

The RocketStor 6542AW supports up to eight Solidigm D5-P5336 SSDs via a single PCIe connection, delivering a massive 976TB of storage capacity. This solution not only breaks through the capacity limits of portable storage devices but also ensures exceptional data read/write speed via HighPoint’s proven PCIe Switching technology. The combination of near-petabyte capacity together with ultra-fast transfer performance represents a significant milestone in storage innovation, and is ideal for enterprise workflows designed to handle massive datasets.

Ultra-Compact Powerhouse

With its near-petabyte storage capacity, compact footprint measuring just 4.84 inches tall and 9.25 in length, and dedicated PCIe x16 host to device connectivity, the HighPoint RocketStor 6542AW NVMe RAID Enclosure is ideal for environments where space is limited but high performance is essential. Whether in an enterprise data center environment, media production studio, or portable setup for an on-the-go professional, this solution delivers uncompromised storage capacity and performance while maintaining a sleek and space-efficient form factor.

Empowering AI/ML and Big Data Analytics

For artificial intelligence and machine learning (AI/ML) workloads, data throughput and processing speed are critical factors in model training efficiency. The RocketStor 6542AW, combined with high-performance Solidigm D5-P5336 SSDs can significantly accelerate AI/ML model training times. For researchers and developers working with large datasets, this solution provides a powerful boost to productivity and efficiency.

Ideal for Enterprise Backup and HPC Applications

Data security and backup efficiency are paramount in the enterprise. The RocketStor 6542AW’s redundant RAID technology and robust CDFP connectivity facilitates rapid and secure backups of large datasets. Additionally, the compact solution excels in high-performance computing (HPC) environments and is capable of supporting intensive computational workloads with minimal latency. It is particularly well-suited for scientific research and engineering simulations, where fast and reliable data access is critical.

Seamless Handling of High-Resolution Media Workflows

For high-resolution media applications such as 4K/8K video editing and rendering, storage capacity and data access speed are often bottlenecks. The solution’s 976TB of capacity and 28GB/s of transfer bandwidth effortlessly accommodates large volumes of video files, ensuring smooth playback and accelerated production pipelines. This makes it an ideal choice for High-resolution media applications such as film production, animation design, and rendering.

HighPoint Technologies: Redefining the Boundaries of NVMe Storage

“This collaboration between HighPoint and Solidigm is a game-changer in enterprise storage,” said May Hwang, VP at HighPoint Technologies. “By qualifying the Solidigm D5-P5336 SSDs in our RocketStor 6542AW, we’ve created an unprecedented solution that combines high-capacity NVMe storage with seamless scalability. AI, HPC, and data-driven industries can now harness near-petabyte storage in an ultra-compact form factor without compromising performance.”

“As Hardware RAID adoption in the AI ecosystem is becoming more prevalent, this collaboration is significant using Solidigm industry-leading, high-capacity SSDs and HighPoint’s HW RAID enclosure.” – Mike Mamo Senior Principal Engineer/ Senior Director at Solidigm

A New Benchmark for External Storage Expansion

The HighPoint solution with Solidigm SSDs sets a new standard for external NVMe storage expansion, addressing the growing demand for external, high-performance, high-capacity storage in AI, media production, and big data analytics. With its exceptional speed, unparalleled capacity, and flexible RAID technology, the 976TB external NVMe storage solution is poised to become the go-to choice for customers seeking reliable and efficient storage support in their data-intensive workflows.

Learn More

RocketStor 6542AW: 8-Bay PCIe 4.0 x16 NVMe RAID Enclosures

https://www.highpoint-tech.com/nvme-enclosure/rs6542aw

Top Challenges Faced by Data Annotation Companies

AI models need accurate data annotations to work well. However, labeling data is complex and takes a lot of time. It also comes with many challenges. Companies that do AI annotation at scale focus on three key areas: consistency, security, and cost management.

This article examines the major obstacles in data annotation and offers practical strategies for overcoming them. Manage your team or use annotation tools. These insights will help you streamline workflows and improve data quality.

Data Quality and Consistency

Accurate data annotations are key to training reliable AI models. But inconsistencies in labeling can hurt performance. Keeping data quality high is one of the biggest challenges for AI annotation companies.

Variability in Human Labeling

Different annotators may label the same data differently due to experience, fatigue, or personal bias, making it essential to define what is data annotation clearly from the start.

How to improve consistency:

  • Set clear guidelines. Detailed instructions reduce mistakes.
  • Measure agreement. Compare labels from multiple annotators to find inconsistencies.
  • Provide regular training. Keep annotators updated on best practices.
  • Use a review process. Quality checks catch errors before data is used.

Subjectivity in Labeling

Some tasks, like sentiment analysis, require judgment. This makes it harder to ensure uniformity.

Ways to handle subjective data:

  • Define strict rules. Clear criteria help annotators make the right call.
  • Use experts for complex tasks. Specialists reduce bias.
  • Aggregate multiple labels. Majority voting improves accuracy.

Managing Edge Cases

Rare or unclear data points—like blurry images or mixed sentiments—can slow down annotation.

Strategies for handling unusual cases:

  • Flag ambiguous cases. Senior annotators review difficult data.
  • Create an edge case guide. A shared reference ensures consistency.
  • Use smarter annotation tools. AI-assisted labeling reduces effort.

High-quality data annotation improves AI accuracy. A strong review system and structured workflows help maintain standards. For more on best practices, check out this guide on data annotation.

Scaling Data Annotation Operations

As demand for AI grows, companies need to understand what is annotation and how to scale their operations efficiently. Expanding a workforce is tough. You must balance speed with accuracy. Also, integrating automation can be tricky if you want to keep precision.

Workforce Management and Training

Hiring and training annotators takes time. Without proper onboarding, quality suffers, and productivity drops.

How to manage an annotation team effectively:

  • Standardize training. Create structured programs to shorten the learning curve.
  • Use tiered expertise levels. Assign simple tasks to beginners and complex ones to experienced annotators.
  • Track performance. Regular reviews pinpoint weaknesses.

Balancing Speed and Accuracy

Faster labeling increases productivity, but often reduces quality. Rushing through annotations leads to errors that require costly corrections.

How to maintain accuracy without slowing down:

  • Optimize workflows. Split tasks into manageable parts for better workflow.
  • Use real-time feedback. Automated alerts can catch mistakes early.
  • Implement a review system. A second set of eyes helps prevent major errors.

Leveraging Automation Without Losing Precision

AI-powered annotation tools can speed up data labeling, but relying too much on automation can reduce quality.

How to use automation effectively:

  • Combine AI with human review. AI handles repetitive tasks, while humans refine complex labels.
  • Train AI models with quality data. Poorly labeled data makes automation less reliable.
  • Continuously improve automation. Update and refine AI tools based on feedback.

Scaling AI annotation operations requires balancing workforce growth, efficiency, and automation. A structured approach helps companies meet growing demand and maintain high-quality labeled data.

Data Security and Compliance

Handling sensitive data comes with risks. AI annotation companies must protect client information while complying with legal regulations. Without proper safeguards, data breaches and compliance violations can lead to serious consequences.

Handling Sensitive Data

Medical records, financial transactions, and personal data often require labeling. Mishandling such information can lead to legal issues and loss of trust.

How to protect sensitive data:

  • Use encryption. Secure data storage and transfers.
  • Restrict access. Only authorized personnel manage sensitive data.
  • Anonymize records. Remove identifiable details where possible.

Meeting Industry Regulations

Various industries follow strict data protection laws. For example, Europe has GDPR, and the U.S. has HIPAA for healthcare. Violating these laws can lead to financial penalties and operational constraints.

Steps to stay compliant:

  • Understand relevant regulations. Stay up to date with laws affecting your projects.
  • Implement audit trails. Keep detailed records of data access and modifications.
  • Train employees on compliance. Regular education ensures team members follow best practices.

Securing Distributed Teams

Many annotation teams work remotely, increasing security risks. Weak policies can leave sensitive data vulnerable to unauthorized access.

Best practices for securing remote teams:

  • Use VPNs and secure connections. Prevent data leaks.
  • Restrict downloads and sharing. Ensure annotators cannot store sensitive data locally.
  • Monitor activity. Track access logs to detect unusual behavior.

A strong data security strategy protects both the company and its clients. Following industry regulations and implementing strict security measures ensures compliance and builds trust.

Cost Management and Profitability

Data annotation is resource-intensive. Juggling quality, speed, and security while staying within budget is a complex task. Poor planning can lead to high labor expenses, inefficiencies, and costly rework.

High Labor Costs

Annotation requires skilled workers, and as datasets grow, so do payroll expenses.

Ways to reduce labor costs without sacrificing quality:

  • Combine in-house and external teams for optimal efficiency. Offshore annotators can lower expenses while experts handle complex cases.
  • Optimize workforce allocation. Assign repetitive tasks to entry-level workers and difficult cases to experienced annotators.
  • Implement pay-for-performance models. Reward accuracy to improve efficiency.

Hidden Costs of Poor Annotations

Low-quality labels slow down AI training and force companies to redo work, increasing expenses.

How to prevent costly mistakes:

  • Invest in quality control early. Catching errors before AI training saves money.
  • Use AI-assisted pre-labeling. Reduces manual effort and speeds up annotation.
  • Monitor data quality regularly. Continuous checks prevent large-scale errors.

Efficient Resource Allocation

Companies also need to handle infrastructure costs. This includes computing power, storage, and annotation tools.

Ways to allocate resources effectively:

  • Scale cloud usage based on demand. Avoid overpaying for idle resources.
  • Use efficient annotation platforms. The right tools reduce time spent on labeling.
  • Automate repetitive tasks. Free up human annotators for complex work.

To balance costs and keep high-quality AI annotation, smart resource management and workflow optimization are key. Companies that streamline operations can improve profitability without compromising results.

Final Thoughts

Growing AI annotation capabilities while keeping quality, security, and costs in check is no easy feat. Companies must address issues like inconsistent labeling, workforce management, and data security. This is key to staying competitive.

A clear plan helps tackle these challenges. It combines guidelines, automation, and quality control. By refining workflows and investing in the right annotation tools, businesses can deliver accurate, reliable data while keeping operations efficient.

Intel’s Thunderbolt Share: Using Multiple PCs to Handle Demanding Workflows

The rapid adoption of artificial intelligence has significantly increased demand for developers who can create new AI-powered programs and applications. But processing, analyzing or training with vast amounts of data can strain even a powerful PC’s bandwidth, making it difficult to multitask with other applications working in the background.

It’s a pain point that can be solved by connecting two computers. Intel’s Thunderbolt™ Share software lets users easily connect two Windows® PCs to share screens and to control using a single keyboard, mouse and storage. Using a PC’s existing Thunderbolt™ 4 or Thunderbolt™ 5 port, a single cable provides secure high-speed, low-latency compute for file-sharing and screen-sharing. One of the PCs or accessories needs to be Thunderbolt Share-licensed.

“AI uses language models, and those models are huge. So maybe you’re working and tweaking a model, but then you need to send it over to another PC to test it. This whole idea of developers and AI and moving mass amounts of data is huge for Thunderbolt Share,” said Lyle Warnke, technical marketing engineer at Intel. “It’s not having to go over my Wi-Fi network, which is slow, or the cloud, which is even slower, or use an external drive, which takes time and is not very efficient. This is private. I’m not going to the cloud, no one will ever see my data because it’s simply going from one PC to the other over a cable.”

Most computers, Windows and Apple, are equipped with a Thunderbolt port. It looks the same as a USB-C port and is designated by a lightning symbol. While it can be used as a USB-C port, which typically has a 10 gigabit per second bandwidth rate to transfer information, the use of a Thunderbolt cable boosts the speed 4x to a 40 gigabit per second bandwidth or 8x to an 80 gigabit per second bandwidth with Thunderbolt 5.

Thunderbolt Share software can be downloaded and installed on Windows PCs with Thunderbolt 4 or Thunderbolt 5 ports. The software checks that at least one PC or Thunderbolt accessory, such as a dock, monitor or storage, is Thunderbolt Share-licensed by the manufacturer, then allows the connected PCs to share resources. The first licensed PCs and docks are available now, with more coming in 2025.

More Jobs Require More Than One Computer

Using multiple computers is more common than many think. But before the introduction of Thunderbolt Share in 2024, there was no easy method to connect two Windows PCs directly with a cable.

“This is the productivity part that we see for business users that’s so great,” Warnke said. “Maybe I have a desktop and it’s doing my finance stuff, but I’ve got my laptop that’s doing email and PowerPoint, but I just want to use the one desk monitor area.”

Enthusiast gamers and professional creators often use two to three computers for more flexibility, reduced system strain and the ability to dedicate machines to specific tasks. The more computers used, the better the workflow and the fewer bottlenecks.

For example, a visual artist will have a powerful desktop for intensive tasks like video editing, AI video creation, 3D rendering or graphic design. Those applications take most of the desktop’s bandwidth to run smoothly. That creator will likely also have a laptop for portability in attending client meetings, presentations or on-location shoots. And they might have a third, older PC in use as a dedicated server.

Ben Hacker, general manager of Intel Client Connectivity Division, explains it this way: “If I’m a creator, I have a laptop and a desktop simultaneously running multiple applications; my laptop may be running different applications than my desktop, but I want to use them together. Thunderbolt Share allows me to utilize my high-resolution, large monitor with both computers without having to buy a KVM to switch between the two.”

Everyday Uses of Thunderbolt Share

Screen sharing is among the most powerful operations for Thunderbolt Share, comprising over 50% of Thunderbolt Share’s usage, Hacker says. Other key features include:

  • Easily drag and drop files quickly between computers.
  • Sync entire folders between computers.
  • Get up and running on a new PC fast with an easy transfer of files from the old to the new PC.

Even for mainstream professionals, Thunderbolt Share is a time-saver. It allows anyone to quickly transfer a project directly to a home computer or to share large files with colleagues or clients without using an external hard drive.

That direct connection helps ensure security and privacy. No more worrying about misplacing an external hard drive or sending sensitive information through a third-party file transfer site that might not be able to handle large data files. Thunderbolt Share is that direct PC-to-PC connection that doesn’t need Wi-Fi or the cloud, so files stay in the user’s control.

For gamers who want to stream their content, it’s easy to see how controlling two computers with a single keyboard and mouse helps ensure high performance and smooth game play. At Intel’s Tech Showcase at CES 2025, a laptop was connected to a desktop playing the Marvel Rivals game with a Thunderbolt cable and port. It enabled seamless sharing of displays, audio, common peripherals and storage. The desktop handled the heavy task of running the game, while the laptop managed the streaming workload using Thunderbolt Share and OBS (Open Broadcaster Software) – and both could be controlled on one screen.

“We know that right now is a perfect time for Thunderbolt Share because people do have more than one PC. And with Thunderbolt being a mainstream port, it’s very likely your second PC also has Thunderbolt, and you can have this type of experience,” Warnke said. “Two PCs can be better than one. Connecting two PCs with Thunderbolt Share helps improve your multitasking and productivity. It’s easy, it’s fast and efficient.”