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  • Behind the Algorithm: Confronting the Real Risks of Biased AI

    Behind the Algorithm: Confronting the Real Risks of Biased AI

    At SparxWorks, our passion for leveraging emerging technologies is matched by our commitment to ethical standards and unbiased AI solutions. Over the past decade, the rise of social media and mobile devices has brought incredible convenience and significant challenges. As we integrate AI into our personal and professional lives, our priority is ensuring that these powerful tools serve everyone fairly, without hidden agendas or skewed information.

    The risk of individuals or groups influencing AI outputs to align with their political or personal views is very real. That is why SparxWorks follows a strict, transparent framework to ensure our solutions minimize bias and provide accurate, trustworthy results.

    Below are five key practices we uphold at SparxWorks to select the right AI models and avoid biased services:

    1. Conduct Thorough Due Diligence

    Before we incorporate or recommend any AI service, our team at SparxWorks performs a comprehensive vetting process.

    • Founders & Leadership Research: We examine the backgrounds of the AI provider’s leadership, scrutinizing past affiliations, sources of funding, and public statements.
    • Client Feedback Analysis: We research real-world case studies and user reviews to gain a deeper understanding of each model’s performance and potential pitfalls.

    2. Demand Transparency in Data and Training Methods

    We know that an AI tool is only as good as the data it is built upon. At SparxWorks, we require complete transparency from our AI partners regarding their data sourcing, labeling, and quality checks.

    • Comprehensive Documentation: We recommend requesting that AI providers clearly outline how data is collected, cleaned, and used in model training to ensure transparency and accountability.
    • Third-Party Audits: Whenever possible, we suggest seeking AI providers that engage unbiased, third-party organizations to assess their data and models. This adds an extra layer of credibility.

    3. Evaluate the Model’s Decision-Making Process

    Understanding why a model makes certain recommendations is vital. At SparxWorks, we stress model explainability to detect and mitigate any hidden biases.

    • Explainable AI: We ask for clear explanations of how inputs lead to specific outputs or decisions.
    • Continuous Monitoring: We establish real-time dashboards that monitor the model’s performance, flag unusual results, and trigger reviews whenever anomalies occur.

    4. Implement Human Oversight

    Even the most advanced AI cannot replace the ethical judgment and contextual knowledge that human experts bring to the table.

    • Diverse Review Teams: We recommend forming multidisciplinary committees with varied perspectives to evaluate AI decisions, ensuring more inclusive and balanced outcomes.
    • Active Testing Scenarios: Regularly conducting test runs using real-world and hypothetical situations can help identify and address potential biases before they impact decision-making.

    5. Foster a Culture of Ethical AI Use

    Beyond technical best practices, we emphasize an organizational culture that respects privacy and fairness at every stage of AI development and deployment.

    • Company-Wide Standards: To ensure responsible AI deployment, we recommend establishing clear, documented policies that define the ethical use of AI, data handling, and accountability measures.
    • Training & Workshops: Regularly hosting internal training sessions can help keep teams informed about emerging risks and best practices in AI ethics, fostering a culture of responsible AI use.
    • Open Door Policy: We actively encourage our staff to voice concerns or report potential biases in our systems, ensuring a transparent and collaborative environment.

    Conclusion

    In a world where AI’s influence grows daily, sparing no effort to ensure fairness and transparency is crucial for businesses and individuals alike. At SparxWorks, we believe that thorough vetting, continuous monitoring, human oversight, and a strong ethical culture are non-negotiables when it comes to delivering unbiased AI solutions.

    As new AI innovators like DeepSeek and Gronk3 emerge, our guiding principles remain the same: analyze carefully, act responsibly, and always prioritize honesty and integrity. Through this unwavering commitment, we aim to harness AI’s transformative power and create a future where technology truly serves the greater good.

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  • Trust and Confidence: The Cornerstones of AI Adoption in Business

    Trust and Confidence: The Cornerstones of AI Adoption in Business

    The rapid expansion of AI solutions presents businesses with an incredible opportunity to enhance efficiency, decision-making, and customer engagement. However, as AI models become more powerful, businesses are rightly asking: Can we trust these platforms with our data? Confidence in data privacy isn’t just a nice-to-have—it’s essential for AI adoption at scale.

    Recent developments in AI, including the rise of models like DeepSeek and Gronk 3, offer exciting alternatives to OpenAI, Google, and Anthropic. But they also raise critical concerns: Who owns and controls these AI platforms? As AI becomes more integrated into business operations, the entity behind the technology—and their incentives—matters more than ever.

    Use case: The Risks of Data Exploitation: Lessons from Mobile Gaming

    To understand the potential risks of AI adoption, we only need to look at another digital revolution: mobile gaming. Many free-to-play games have evolved from simple entertainment into data-harvesting machines. Instead of just monetizing through ads or in-game purchases, some of these apps now track users across the internet, collecting behavioral data to sell to third parties.

    Even more concerning is how these companies circumvent regulations. A common tactic involves shifting app ownership to countries with weaker enforcement, like Cyprus, making it harder for regulators to hold them accountable. These business models prioritize surveillance over user experience, leading to justified concerns about privacy violations.

    The AI industry faces a similar challenge. If businesses entrust their customer data, intellectual property, or proprietary insights to an AI model, they need absolute confidence that it won’t be harvested, sold, or exploited—especially by entities operating under unclear jurisdictional oversight.

    Why AI Ownership Matters More Than Ever!

    AI platforms are not just tools; they are gateways to business intelligence. When evaluating AI models, companies must consider:

    1. Who owns the platform? A company’s data policies, legal jurisdiction, and governance structure determine how your information is handled. AI providers with opaque ownership or foreign control could pose compliance risks, particularly under data protection laws like GDPR and CCPA.
    1. What is their business model? Is the AI platform funded by advertising, data sales, or surveillance-driven monetization? If a product is “free” or significantly cheaper, businesses must ask: What’s the real cost?
    1. Can you trust their commitments to privacy? Public AI companies like OpenAI, Google, and Anthropic, while not perfect, have reputations to maintain and clear regulatory accountability. Comparatively, lesser-known or newer AI providers may have fewer safeguards or be more susceptible to outside influence.

    The Safer Bet for Businesses: Established AI Players

    While competition in AI is valuable, businesses can’t afford to take risks with their sensitive data. This is why platforms from OpenAI, Google, and Anthropic—despite their flaws—remain a safer bet than many emerging alternatives or Meta’s AI offerings.

    • These companies operate under strict scrutiny from regulators, investors, and enterprise customers, reducing the risk of unexpected policy shifts.
    • Their business models are less reliant on aggressive data monetization, unlike companies with ad-driven revenue models.
    • They provide clearer compliance and security measures that align with corporate data governance standards.

    For businesses looking to adopt AI without compromising privacy, compliance, and control, trusting the right AI partner isn’t just important—it’s non-negotiable.

    The AI revolution is here, but not all AI platforms are created equal. Businesses must prioritize trust and confidence in data privacy over the allure of new, untested models. Without these assurances, AI adoption could pose more risks than rewards.

    Before integrating an AI solution, ask the hard questions: Who controls it? Where is your data going? What’s the business model? In a world where data is more valuable than ever, ensuring its protection isn’t just a best practice—it’s a competitive advantage.

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  • 10 Ways AI Can Revive Retail Sales in 2025

    10 Ways AI Can Revive Retail Sales in 2025

    Retail just took a big hit to start the year—sales dropped 0.9% in January, way worse than the 0.2% decline economists expected. Blame it on the usual post-holiday slump, inflation worries, or that brutal winter freeze—it’s a tough start for retailers. If this trend keeps up, we could be looking at an even slower Q1 for the economy. But here’s the thing: AI isn’t just a buzzword anymore—it’s a game-changer. The right AI-driven strategies can help retailers bounce back by optimizing operations, personalizing shopping experiences, and boosting sales. Let’s dive into 10 AI-powered solutions that can help retailers turn things around fast.

    1. Personalized Customer Experiences

    AI analyzes purchase history, preferences, and real-time behavior to offer tailored recommendations. Retailers using AI-driven personalization have seen 10-30% increases in revenue.

    2. AI-Powered Dynamic Pricing

    Smart pricing algorithms adjust product prices based on demand, competitor activity, and even weather patterns. This helps retailers remain competitive while protecting profit margins.

    3. Predictive Inventory Management

    By analyzing historical sales data and market trends, AI can forecast demand more accurately, reducing overstock and out-of-stock issues.

    4. AI Chatbots for 24/7 Customer Engagement

    Retailers using AI chatbots can handle up to 80% of customer inquiries without human intervention, improving response time and customer satisfaction.

    5. Enhanced Visual Search & Virtual Try-Ons

    AI-powered image recognition allows shoppers to search for products by uploading photos. Virtual try-on technology (for apparel, accessories, and cosmetics) boosts conversion rates by 2-3x.

    6. AI-Optimized Supply Chains

    By identifying potential disruptions and alternative solutions, AI-driven logistics improve delivery efficiency and reduce costs.

    7. Fraud Prevention & Secure Transactions

    AI detects anomalies in purchasing behavior, preventing fraud in real time, securing transactions, and increasing consumer trust.

    8. Sentiment Analysis for Market Trends

    AI scans social media, reviews, and feedback to detect emerging trends and customer sentiment, helping retailers make smarter product decisions.

    9. Smart Store Layout Optimization

    AI-driven insights on foot traffic patterns can help retailers adjust store layouts, improving product placement and boosting sales.

    10. AI-Powered Ad Targeting

    AI refines ad strategies by identifying high-intent shoppers and delivering hyper-personalized ads, increasing ROI on digital marketing efforts.

    The Key to Retailers facing declining sales must embrace AI as a strategic partner in their revival. From optimizing supply chains to creating more personalized customer experiences, AI can reignite retail sales growth in 2025 and beyond.

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  • DeepSeek: A Promising Contender, but Not Business-Ready Yet?

    DeepSeek: A Promising Contender, but Not Business-Ready Yet?

    The excitement around DeepSeek is undeniable. Competitive pricing, open-source flexibility, and impressive early results make it a compelling alternative in the AI landscape. But before businesses rush to integrate it, critical questions remain.

    Security & Data Sensitivity

    Open-source means flexibility—you can deploy it on your own servers. But what data is shared, how it’s processed, and whether it’s truly secure are questions that still need thorough vetting. Unlike enterprise-ready solutions, DeepSeek’s security framework requires deep scrutiny before handling sensitive business data.

    Performance & Latency

    How fast can DeepSeek generate responses at scale? How well does it handle complex, multi-turn queries? Speed and accuracy are crucial in enterprise environments, but these factors are still largely untested in real-world business applications.

    Ecosystem & Integration

    DeepSeek exists alongside OpenAI, Copilot, and other enterprise AI solutions. But how well does it integrate? Will it work as a standalone LLM, or does it need custom pipelines and fine-tuning for optimal results? The AI stack is evolving fast, and businesses must assess whether DeepSeek can be a strategic addition—or just another experimental tool.

    Bottom Line

    DeepSeek’s potential is undeniable. But for businesses, it’s not just about cost or open-source access—it’s about trust, security, performance, and interoperability. More research and real-world validation are needed before it can be considered a viable alternative for production environments.

    What are your thoughts? Are you exploring DeepSeek, or do you see other emerging LLMs with greater business potential?

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  • Part 2 – Taking Your GPT to the Next Level: Capabilities and Actions to Boost Customer Retention

    Part 2 – Taking Your GPT to the Next Level: Capabilities and Actions to Boost Customer Retention

    In my last article, “Easy step by step instructions to build your own AI chatbot to increase customer retention” we explored how to set up a GPT for creating polished, on-brand customer materials. Now, let’s take it a step further by leveraging the “Capabilities” and “Actions” features in the GPT builder interface to amplify customer retention strategies.

    These features are designed to enhance your GPT’s functionality, enabling it to perform tasks, analyze data, and respond in ways that directly impact customer engagement and satisfaction. Using the same customer retention use case from my January 16 post, “AI Made Easy: Your First Steps to Building a GPT for Work or Innovation” we’ll explore how these tools can take your AI assistant from useful to indispensable.

    Step 1: Understanding the “Capabilities” Feature

    Capabilities define what your GPT can “know” and “do.” By tailoring these settings, you empower your GPT to access and use the information it needs to deliver value in customer-facing tasks.

    Enhancing Capabilities for Customer Retention:

    1. Access to Customer Data:
      • Integrate your GPT with your CRM systems (e.g., Dynamics 365 or Salesforce) to access customer profiles, purchase histories, and communication records.
      • Use this data to tailor responses, making every interaction feel personal and relevant.
    2. Knowledge Base Integration:
      • Add FAQs, product documentation, and service guidelines to your GPT’s knowledge base.
      • For example, your GPT can provide instant answers to customer questions, reducing wait times and improving satisfaction.
    3. Real-Time Insights:
      • Connect your GPT to business intelligence tools (like Power BI) to retrieve and analyze customer data trends.
      • Use these insights to craft targeted proposals or recommend proactive solutions to recurring customer issues.

    Step 2: Leveraging the “Actions” Feature to Boost Retention

    Actions enable your GPT to perform specific tasks, such as creating documents, sending emails, or scheduling follow-ups. These features help automate and streamline workflows.

    Actions to Improve Customer Engagement:

    1. Automated Follow-Ups:
      • Configure your GPT to draft follow-up emails after meetings or customer interactions.
      • Example: “Send a follow-up email to [Customer Name] summarizing the discussion and including next steps.”
    2. Dynamic Document Creation:
      • Use pre-configured templates for proposals, RFPs, and marketing materials.
      • Your GPT can automatically pull customer-specific data from your CRM and format it into a polished document.
    3. Task Management and Reminders:
      • Integrate with Microsoft Teams or Outlook to create and assign tasks based on customer requests.
      • Example: “Remind the sales team to check in with [Customer Name] on [Date] regarding their recent inquiry.”
    4. Live Customer Support Assistance:
      • Equip your GPT to provide real-time guidance during customer interactions.
      • Example: During a Teams call, your GPT can suggest relevant solutions or provide quick access to product details based on the discussion.

    Bringing It All Together

    By combining Capabilities and Actions, you can transform your GPT into a full-scale Customer Retention Assistant. Let’s revisit the customer retention goals from my January 9 article and see how these features come to life:

    1. Creating High-Impact Customer Materials:
      Your GPT pulls customer data, applies templates, and generates polished proposals or follow-ups automatically.
    2. Meeting Preparation and Insights:
      It summarizes past interactions, highlights pain points, and suggests targeted questions, ensuring your team is always prepared.
    3. Faster, More Thoughtful Responses:
      With access to your knowledge base and CRM, your GPT drafts empathetic, personalized responses, helping you resolve issues efficiently.

    What’s Next?

    To fully unlock the potential of these features, start small. Choose one workflow—such as automated follow-ups—and test how well your GPT performs. Refine its capabilities and actions based on real-world feedback, then scale up to more complex tasks.

    By customizing capabilities and actions, your GPT doesn’t just support your team—it becomes an essential part of your customer retention strategy.

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  • AI Made Easy: Your First Steps to Building a GPT for Work or Innovation

    AI Made Easy: Your First Steps to Building a GPT for Work or Innovation

    Have you been eager to build your first GPT (Generative Pre-trained Transformer) to make a real impact on your business, but you’re not sure where to start? Or perhaps you wish you had someone to guide you through the process? This overview is here to help.

    Building a custom GPT with tools like Microsoft’s GPT Builder or OpenAI’s platform has never been easier—no engineering background required. In this guide, we’ll break down the key features and configuration options, empowering you to create AI assistants that are tailored to your unique business needs.

    Key Tabs Overview:

    • Create Tab: This is where you define your GPT’s purpose and customize its personality. Add a name, description, and initial instructions to shape how your GPT interacts with users.
    • Configure Tab: Here, you fine-tune your GPT’s capabilities. Add advanced features, integrate external data sources, and define how your GPT will perform specific actions.
    • Preview Tab: Test your GPT in real time to see how it responds to user inputs. This is crucial for ensuring your GPT behaves as intended.
    • Share Tab: Once your GPT is ready, use this tab to share it with others via links or embed it in apps or websites.
    • Update Tab: Use this section to make ongoing updates to your GPT as your business needs evolve.

    Configuring Your GPT:

    Customizing your GPT’s configuration ensures it delivers the right value to users.

    • Name: Choose a name that reflects the GPT’s role or target audience (e.g., “Marketing Assistant GPT”).
    • Description: Briefly summarize the GPT’s purpose and key features to set user expectations.
    • Instructions: Provide specific instructions to guide your GPT’s tone, style, and behavior. For example, “Speak formally and focus on SaaS trends.”
    • Knowledge: Define what your GPT knows. You can include specific information, such as product details or company guidelines, and exclude irrelevant topics.

    Capabilities:

    Capabilities determine what your GPT can do:

    • Web Search: Enables your GPT to fetch real-time information from the web.
    • Canvas: Allows interactive, visual displays for brainstorming or diagramming.
    • DALL-E Image Creator: Lets your GPT generate custom images from text prompts.
    • Code Interpreter & Data Analysis: Adds the ability to analyze data or interpret code, ideal for technical applications.

    Actions:

    Actions let your GPT interact with external tools or perform specific tasks:

    • Create a New Action: Build custom workflows that your GPT can execute, like scheduling meetings or querying a database.
    • Add Actions:
      • Authentication: Securely connect your GPT to external services.
      • Import URL: Pull structured data directly from web sources.
      • Examples: Pre-built templates to accelerate development.
      • Schema: Define how your GPT processes specific types of data.
      • Get Help from Actions GPT: Access assistance for creating actions.
      • Privacy Policy: Ensure compliance by linking your company’s privacy guidelines.

    With these tools, building GPTs is no longer reserved for technical experts. Even non-technical users can create AI solutions that align perfectly with their business goals. Start small, experiment boldly, and refine your GPT to unlock its full potential for your organization.

    Over the coming weeks, I’ll be diving deeper into each of these features, offering practical tips and insights to help you on your journey. Ready to take the first step? What would you create with your GPT? Share your ideas—I’d love to hear them!

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  • 5 Essential Steps Before You Launch Your First Microsoft 365 Copilot

    5 Essential Steps Before You Launch Your First Microsoft 365 Copilot

    Let’s be honest setting up a new AI assistant can feel like preparing for a big event. Microsoft 365 Copilot is no exception. It blends your everyday tools like Word, Excel, and Teams with advanced AI capabilities. But before you start chatting with Copilot, you’ll need to lay some groundwork. Think of it as getting the stage lights, sound checks, and script all in order, so your Copilot show runs smoothly.

    1. Organize Your Information Sources
      Before using Copilot’s “knowledge” feature, ensure your SharePoint, Teams, and OneDrive files are in good shape. Make sure documents are accurately named and logically stored—Copilot’s prompts rely on this data to provide relevant answers.
    2. Check Permissions and Access Levels
      Your Copilot respects your security and compliance settings. Confirm that permissions are updated so the right people have the right access. This ensures Copilot won’t be handing out sensitive info to unintended audiences.
    3. Fine-Tune Your Prompting Strategies
      Before you go live, practice asking Copilot targeted questions. The more specific your prompts, the better its responses. Instead of “show sales data,” try “show monthly sales totals by region from the last quarter.”
    4. Confirm Data Accuracy and Freshness
      Copilot draws insights from what it can “see.” Verify that your reports and data sources are up-to-date. Clean, current data means trustworthy results from Copilot.
    5. Start Small and Iterate
      Begin with a limited pilot group and a few test scenarios. Gather feedback, refine your prompts, and then expand. This approach ensures your Copilot is truly helpful from day one.

    Copilot can be an amazing and very powerful assistant. But it needs the right set up. If you want to talk through this in more detail, DM me. With the right setup, you can watch your productivity take off. Just like having a virtual teammate at your side!

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  • Why Businesses Struggle with AI Copilots—and How to Get It Right

    Why Businesses Struggle with AI Copilots—and How to Get It Right

    AI copilots like Microsoft 365 Copilot are game-changers, promising to revolutionize the way businesses operate. Yet many companies are finding that integrating them into operations and services isn’t as seamless as advertised. Let’s talk about why—and how to overcome these challenges.

    One of the biggest hurdles is data preparation. Microsoft paints a picture of effortless AI adoption: connect your data, hit go, and watch the magic happen. But the reality is much more nuanced. Most organizations lack a clear roadmap for preparing their data, ensuring it’s clean, organized, and accessible for AI models. Without this foundation, copilots can’t deliver consistent, accurate results.

    Another challenge is complexity. While Microsoft offers tools to customize and connect copilots, navigating these capabilities requires more than basic technical know-how. Building effective prompts and customizing GPTs to meet unique business needs takes specialized skills. And let’s not overlook Microsoft’s tendency to oversimplify the process, which can leave executives blindsided when things don’t “just work.”

    Finally, there’s the issue of data security and oversharing. Recent reports show that copilots can unintentionally expose sensitive data. It’s a problem Microsoft is working on, but in the meantime, companies need robust governance to avoid unintended leaks.

    So, how do you tackle these challenges?

    1. Prioritize Data Readiness. Take a step back and assess your data. Is it structured, complete, and accessible? Invest in tools and workflows that clean and organize your data before introducing AI.
    2. Partner Strategically. Not every business has in-house expertise in prompt engineering or GPT customization—and that’s okay. Partnering with experts can accelerate your journey and minimize missteps. That’s why SparxWorks joined forces with PulseOne, a nationwide leader in Managed IT Services. Together, we’re helping businesses bridge these gaps, from data preparation to full-scale AI integration.
    3. Educate Your Team. Copilots require thoughtful implementation. Train your team to build better prompts and understand the nuances of AI behavior.

    The promise of AI copilots is real, but the path to unlocking their full potential requires careful planning. If you’re struggling with how to prepare your organization, let’s connect. SparxWorks and PulseOne are here to make the process as seamless—and impactful—as possible.

    I’d love to hear your feedback on Copilot implementations—what’s worked, what hasn’t, and any best practices (or lessons learned) along the way.

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  • Is AI going to take my job?

    Is AI going to take my job?

    Like many of you, I am thrilled by the advancements in AI, particularly large language models (LLM), which are ushering us into a new era. AI is certainly enhancing a variety of domains like services, AR, XR, VR, and marketing. However, as my grandma used to say, “You have to take it with a grain of salt.”

    With the media highlighting AI’s latest achievements, it’s easy to get caught up in the excitement. I often hear phrases like, “It’s incredible how it thinks and responds so accurately.” Let’s set the record straight: AI does not think. It operates based on intricate combinations of mathematical concepts including probability, statistics, linear algebra, calculus, game theory, logistics, regression, and more.

    Data management poses a significant challenge. Ensuring the correct data is fed into the LLM, while avoiding copyright, patent, and trademark infringements, is essential.

    AI is unlikely to take over your job unless it involves straightforward processes, such as automating checkout counters or parking stations. Instead of replacing you, AI will augment your abilities, helping you excel in your role and become more productive.

    With LLM, we have the potential to develop tools, services, and virtual assistants that can manage routine tasks. This allows employees to focus on strategic aspects of their roles, potentially improving work-life balance by reducing overtime and weekend commitments.

    For students, integrating AI with technologies like NoSQL will usher in true adaptive learning.
    This promises a tailored educational experience, adjusting content to suit individual learning needs, ensuring a deeper understanding of topics.

    Furthermore, AI will enable the creation of a genuine Omnichannel experience, as it facilitates precise content modularization, allowing smart templates to function optimally.

    While the potential is immense, it’s important to recognize that AI also presents new opportunities for misuse. A cautious and methodical approach is imperative when implementing AI in any business setting.

    Are you considering integrating AI into your business, services, or sales strategies? Need guidance on the right approach? We have extensive experience in AI (having developed an XR platform that’s programming-free) and have garnered invaluable insights over the years. AI offers tremendous benefits, but it requires careful handling. Reach out to us. Let’s harness the power of AI responsibly.

  • AR and VR Can Provide New Revenue Streams in 2023

    AR and VR Can Provide New Revenue Streams in 2023

    CES 2023 came to a close, and what a show it was. After years of remote presentations, we finally converged in Las Vegas; media companies, major manufacturers, agencies, services, developers, and the press came together again with renewed energy to resume full speed ahead “post-pandemic” business. Networking was in the air, and slowly but surely, we all got caught up and started looking into 2023-24 and the exciting opportunities ahead.

    After years of experimentation and hype, we can finally say that augmented reality and virtual worlds are now viable and within reach of consumers. The continued growth of 5G, the availability of edge computing, and the rapid adoption of W3 technologies have set the stage to incorporate digital media in all forms into the actual world, including persistence, and thanks to NFTs, we can make a digital “item” unique.

    CES 2023 Session AR and VR

    It was great to hear Eric Shamlin (Media Monks) and Emily Wengert (Huge) talk about the creative process and their approach to XR and the Metaverse. Rahul Sabnis shares news about iHeart Media events for 2023, which will include augmented reality offerings and more. We all look forward to these events with great anticipation. Silke Meixner (ZS Associates) shared key insights to help develop a sound digital transformation strategy than can facilitate and leverage W3 technologies moving forward. Ashley Crowder (Vntana) is working on solving 3D challenges, which will positively impact the business adoption of AR/VR and all virtual spaces, Metaverse, and games. Jenna Seiden (Niantic) shared exciting news for developers worldwide by announcing new tools and features to deploy AR experiences via browser. From us at SparxWorks, the news is that our platform now supports browser-based augmented reality and the use of QR codes in addition to our app-based capabilities. Our platform allows you to combine images, video, 360-degree images, 3D, audio, and text with AR image recognition, plane recognition, and more.

    At SparxWorks, we specialize in helping businesses reach and connect with their consumers at their point of need. Our customer-centric approach allows us to cultivate and extend your market share through the web, mobile, augmented reality, and, eventually, a persistent metaverse. To learn more come visit our website.