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Belgium 10-09-2026 Squad Only Physical english
AI is moving from pilots into operational use, and the infrastructure question is becoming more concrete. The challenge is deciding what needs to be built, expanded, controlled, or delayed as usage grows. Three pressure points usually appear first. - Compute capacity requires careful planning: teams need enough GPU, cloud, or specialised processing power, while keeping utilisation and cost under control. - Data movement becomes more demanding: AI needs access to data across systems, with latency, security, integration, and quality managed from the start. - Operational control becomes essential: monitoring, access rights, resilience, cost visibility, and ownership need to scale with usage. The working question is simple: how do we support AI at scale while keeping infrastructure efficient, governed, and financially sustainable? If you are working through these choices, let’s compare approaches with others facing similar constraints.
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Belgium 15-09-2026 Squad Only Virtual english
AI pilots are easier to launch than to embed into daily operations. The challenge is changing how work happens once AI becomes part of decisions, tasks, handovers, and controls. Three pressure points tend to decide whether adoption holds. - Workflow fit matters: AI has to enter the flow of work as part of the process. - Ownership needs to be clear: someone must own the process, the controls, the exceptions, and the outcome. - User trust has to be built: people need to know when to rely on AI, when to challenge it, and when to escalate. The working question is simple: how do we move AI from promising pilots into daily work while keeping clarity, accountability, and risk control? If this is part of your current reality, let’s compare choices, constraints, and lessons learned.
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Belgium 17-09-2026 Country Members Physical french
Pendant longtemps, le TCO donnait une impression de contrôle. On investissait, on amortissait, on optimisait. Les coûts étaient visibles, relativement prévisibles, et structurés autour d’une logique claire entre CAPEX et OPEX. Ce modèle ne suffit plus à expliquer où va réellement l’argent. Aujourd’hui, une part croissante des dépenses IT ne repose plus sur ce que l’on possède, mais sur ce que l’on consomme: des tokens générés par l’IA des agents qui exécutent des actions en continu des outils SaaS qui se multiplient dans les équipes du cloud qui s’adapte en permanence à l’usage Le coût ne disparaît pas, mais il devient plus diffus, plus dynamique, et souvent plus difficile à attribuer, à expliquer, et à maîtriser. C’est là que des approches comme le Technology Business Management, ou TBM, redeviennent particulièrement utiles: non pas comme un exercice de reporting supplémentaire, mais comme un moyen de relier concrètement les dépenses aux services, aux usages, et aux décisions. Et c’est là aussi que la question du “Saint Graal” revient, mais sous une autre forme. Vous vous demandez peut-être: Comment garder de la maîtrise quand la dépense dépend du comportement, des usages, et parfois même de systèmes autonomes ? Comment piloter l’équilibre CAPEX/OPEX quand le cloud, le SaaS et l’IA déplacent progressivement les coûts vers des modèles variables ? Quels coûts restent encore trop souvent hors radar: ressources métier, formation, support, intégration, sécurité, gouvernance ? Et surtout, qu’est-ce qu’un “bon TCO” veut encore dire quand il faut le mettre en regard d’objectifs parfois plus stratégiques, plus longs à mesurer, ou plus difficiles à quantifier: qualité de service, résilience, agilité, expérience utilisateur, capacité d’innovation ? Ce sont précisément ces questions qui seront au cœur de notre rencontre: une discussion ouverte entre pairs, ancrée dans la réalité du terrain, pas pour débattre de modèles idéaux, mais pour comprendre comment chacun tente, concrètement, de mesurer, d’attribuer et de reprendre le contrôle, sans réduire la valeur d’un choix technologique à son seul coût.
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CIONET Trailblazer: CISO: The Shift from Prevention to Resilience: Turning Visibility into Execution
Published on: January 28, 2026 @ 9:48 AM
CIONET Trailblazer: AI Transformation: Bridging the Cultural Divide to Achieve Competitive Advantage
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Crypto.com Delivers Accurate Sentiment Analysis in 1 Second with Generative AI on AWS
Crypto.com uses Amazon Bedrock with Amazon SageMaker Studio to run an efficient architecture that delivers nuanced, domain-specific crypto market insights to 100 million global users.
to return results from large language models
to integrate Claude 3 models
Delivers localized multilingual content
Helps engineers continually test new models
Crypto.com is a crypto exchange and comprehensive trading platform serving 100 million users in 90 countries. To improve the service quality of Crypto.com, the firm implemented generative artificial intelligence (AI)-powered sentiment analysis services on AWS to generate market insights.
Crypto.com uses Anthropic Claude 3 large language models on Amazon Bedrock for sentiment analysis and application development, and Amazon SageMaker to fine-tune its custom models. With the top models on AWS, the company can deliver accurate sentiment analysis in less than 1 second, and efficiently train and adjust new models in a rapidly evolving market.

Crypto.com was founded in 2016 with a bold mission: to bring cryptocurrency to every wallet. The company distinguishes itself from other trading platforms through its strong focus on driving user adoption, with a wide network of partners including merchants and payment gateways. Crypto.com currently serves around 100 million users in 90 countries.
With such rapid growth and a diverse customer base—all within an evolving, competitive industry—Crypto.com has embraced generative artificial intelligence (AI) to quickly deliver optimized customer experiences. The company’s AI use cases include conversational assistants for onboarding and customer queries, plus wizards to generate social media marketing campaigns.
In generative AI applications, sentiment analysis and news narrative categorization are becoming increasingly vital; particularly in the cryptocurrency space, investors need timely and accurate market intelligence to make informed decisions. Crypto.com provides a market insight service where users can access the latest news and information from both crypto and traditional news sources to gain insights from the company’s intelligence engines. Each subscription is tailored to the user’s trading level and the coins in their wallet.
Crypto.com employs a range of off-the-shelf machine learning (ML) models alongside its own custom models for sentiment analysis. However, developers encountered challenges with the limitations of open-source models and the high cost of self-hosting large language models (LLMs). They also faced accuracy issues with outputs generated by open-source models, especially when dealing with multilingual news sites. Consequently, Crypto.com sought a more effective solution to integrate and synthesize outputs from multiple ML models—both pre-trained and custom—to provide accurate, reliable, and comprehensive insights into the crypto market.

Generative AI on AWS services like Amazon SageMaker and Amazon Bedrock streamlined our adoption of the latest LLMs and AI technologies. We can now take innovative ideas from POC to full-scale production in weeks.”
Sunny Fok
SVP, Head of AI Innovation Technology at Crypto.com
Crypto.com has run on Amazon Web Services (AWS) since its launch, so when it sought new LLMs for sentiment analysis, Anthropic Claude 3 on Amazon Bedrock was a logical choice. These LLMs are highly scalable and process vast amounts of data in real-time, facilitating comprehensive market research. Initial results showed that LLMs on Amazon Bedrock returned results very quickly, typically within one second.
Integrating Amazon Bedrock eliminated the manual effort, extra cost, and computational constraints of self-hosting LLMs. Within one month, Crypto.com had implemented Anthropic Claude 3 Haiku models on Amazon Bedrock for sentiment analysis, collecting and analyzing crypto news in more than 25 languages. The firm also utilizes Amazon Bedrock for ongoing proofs of concept (POCs) and development. Sunny Fok, head of AI & innovation technology at Crypto.com, says, “Generative AI on AWS services like Amazon SageMaker and Amazon Bedrock streamlined our adoption of the latest LLMs and AI technologies. We can now take innovative ideas from POC to full-scale production in weeks.”
To ensure domain-specific knowledge is applied in model output, Crypto.com used its own data to fine-tune open-source models including Mistral AI and Meta Llama on Amazon Elastic Compute Cloud (Amazon EC2). This approach is crucial when a new coin appears on the market, as off-the-shelf models often deliver subpar results. Crypto.com then began using Amazon SageMaker as an on-demand, end-to-end ML development platform to fine-tune its custom models.
Raymond Lam, senior engineer at Crypto.com, says, “Amazon SageMaker provides the tools and APIs to easily adapt our models with a user-friendly interface. Similar to Amazon Bedrock, we only need to run machine learning jobs as needed, and it’s much easier to manage custom modeling than if we were doing it on our own.”
Crypto.com received extensive support from AWS throughout the idea generation, POC, and production phases of multi-agent deployment. "We had several discussions on what tools or frameworks to deploy for different use cases,” says Lam. “The AWS team shared existing use cases, provided sample code, and demonstrated each step. When technical issues came up, AWS solutions architects helped troubleshoot and offered suggestions, which sped up our onboarding to generative AI on AWS services."
By implementing a multi-agent consensus-seeking solution for sentiment analysis on AWS, Crypto.com can efficiently deliver accurate, comprehensive, and localized crypto market insights to its global user base. Fok explains, "We can share more instant, updated news with users on the sentiment of coins—whether they’re bullish or bearish, for example. Users become more informed, which means they can make better-planned investments." Engineers have also noted improved accuracy for large-context QE with Claude 3 models on Amazon Bedrock.
Furthermore, with Amazon Bedrock, Crypto.com benefits from highly scalable models that automate insight generation to save time and resources. “We’re becoming more efficient with ready-to-use models we can access via API, which gives us more flexibility in development,” Lam says. “With generative AI on AWS, we have more options for different use cases or requirements, so we can easily test new models as they come out.”
Crypto.com is currently exploring new use cases for Claude 3 models on Amazon Bedrock, such as processing documents, tables, and charts. Initial tests have shown promising results in terms of accuracy compared to ML-driven optical character recognition (OCR) readers on the market. The company is also actively developing new generative AI use cases, such as capturing sentiment in social media.
Customers have expressed satisfaction with the work underway, which encourages Crypto.com to keep testing ways to deploy generative AI across the organization. “The feedback from our generative AI projects has been quite amazing, both internally and from our users,” Fok shares.
Crypto.com is on a mission to accelerate the world’s transition to cryptocurrency. With approximately 100 million users across 90 countries, the Singapore-based company offers a trading platform, derivative exchanges, and more. Crypto.com also partners with merchants and payment gateways to support the development of a global crypto network.
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CIONET’s Cyber Circle: a new three-event programme exclusively focusing on the most urgent, complex, and high-impact challenges in cybersecurity today. Launched in 2026, this initiative brings together CISOs, CIOs, and senior IT executives with a strong interest in cybersecurity for three curated gatherings each year. As part of CIONET’s trusted executive community, the Cyber Circle provides a confidential, peer-driven environment to exchange insights, share real-world experiences, and address evolving cyber threats. Each session is designed to foster strategic dialogue, strengthen resilience, and elevate cybersecurity as a core driver of business value.
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The Telenet Business Leadership Circle powered by CIONET, offers a platform where IT executives and thought leaders can meet to inspire each other and share best practices. We want to be a facilitator who helps you optimise the performance of your IT function and your business by embracing the endless opportunities that digital change brings.
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Découvrez la dynamique du leadership numérique aux Rencontres de CIONET, le programme francophone exclusif de CIONET pour les leaders numériques en Belgique, rendu possible grâce au soutien et à l'engagement de nos partenaires de programme : Deloitte, Denodo et Red Hat. Rejoignez trois événements inspirants par an à Liège, Namur et en Brabant Wallon, où des CIOs et des experts numériques francophones de premier plan partagent leurs perspectives et expériences sur des thèmes d'affaires et de IT actuels. Laissez-vous inspirer et apprenez des meilleurs du secteur lors de sessions captivantes conçues spécialement pour soutenir et enrichir votre rôle en tant que CIO pair. Ne manquez pas cette opportunité de faire partie d'un réseau exceptionnel d'innovateurs numériques !
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CIONET is committed to highlighting and celebrating female role models in IT, Tech & Digital, creating a leadership programme that empowers and elevates women within the tech industry. This initiative is dedicated to showcasing the achievements and successes of leading women, fostering an environment where female role models are recognised, and their contributions can ignite progress and inspire the next generation of women in IT. Our mission is to shine the spotlight a little brighter on female role models in IT, Tech & Digital, and to empower each other through this inner network community.
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