How the convergence of satellite and mobile networks is creating a seamless global connectivity fabric, unlocking new opportunities for operators and industries worldwide. Welcome to part three of our 鈥淓ngineering Smart Networks & Operations鈥� mini-series.

For decades, the worlds of mobile and satellite communications have orbited each other at a distance. Mobile operators built dense terrestrial infrastructures to serve growing populations, while satellite service providers focused on broadcasting, broadband backhaul, and niche Internet of Things (IoT) use cases. These once-separate orbits are aligning, and the result is a new era of seamless, global connectivity that will benefit telecoms operators and industry users alike.

A New Constellation of Collaboration

The convergence between the satellite and telecom industries is being driven by a new wave of partnerships that cut across traditional value chains. Mobile network operators (MNOs) are now working directly with satellite network operators (SNOs), the larger satellite manufacturers, and ground infrastructure provider ecosystem to deliver cellular direct-to-device (D2D) coverage that extends far beyond the reach of cell towers alone. And all of this is being underpinned by an evolving set of 3GPP standards that integrate both worlds.

As a result, this vast and evolving D2D ecosystem now spans the new generation of integrated satellite players including Starlink, Amazon Leo (Kuiper), and AST SpaceMobile; legacy players such as Viasat, SES, Eutelsat; satellite manufactures including Thales Alenia Space, Airbus, MDA and Lockheed Martin; ground station specialists like iDirect, Gilat and Hughes Network Systems; and major chipset companies such as Qualcomm, Mediatek, Apple and Samsung.

How Starlink Changed the Orbit of the Industry

No discussion of this transformation can ignore the Starlink effect. By deploying a massive constellation of low earth orbit (LEO) satellites with enhanced ability to 鈥榟and off鈥� signals between each other, SpaceX鈥檚 Starlink disrupted the traditional economics of satellite communications. Smaller, cheaper satellites operating at lower altitudes have enabled global broadband coverage with lower latency, while the company鈥檚 integrated model, controlling everything from the spacecraft to the user terminals and launch vehicles, has allowed it to innovate and scale at unprecedented speed.

But Starlink鈥檚 most significant move may be its foray into Direct-to-Device (D2D) services. By partnering with mobile operators such as T-Mobile in the U.S., it has shown how ordinary 4G and 5G smartphones, as opposed to dedicated satellite-compatible handsets, can connect directly to satellites using a thin slice of terrestrial spectrum in the L- and S-bands, effectively extending the mobile network into space. This 鈥榗ell tower in the sky鈥� model blurs the boundary between terrestrial and non-terrestrial networks (NTNs) and signals the start of a more universal connectivity fabric.

And while Starlink鈥檚 head-start came before appropriate standards were in place, the telecom world has been quick to respond. 3GPP has now incorporated NTN capabilities into its standards roadmap for 5G and beyond. This has delivered a more mature set of 5G NR NTN specifications (R17/R18/R19 and onwards) and NTN NBIOT specifications, all supported by the large vendor ecosystem.

This alignment means that by the latter half of the decade, standard smartphones could feasibly communicate seamlessly with both terrestrial and satellite networks, eliminating connectivity gaps whilst offering near equivalent services.

Why Integration Matters for Industry

For mobile operators, NTNs represent more than just extended coverage, they offer a vital opportunity to grow new revenue streams from existing assets. As traditional connectivity services become commoditized, integrating NTNs allows operators to offer premium, global-grade reliability and reach.

For industry users, the implications are profound. Connectivity can now be treated as a global constant rather than a variable. That means smarter logistics chains, safer offshore operations, more connected vehicles, and data-rich industrial ecosystems that never lose sight of their assets, even in the most remote corners of the planet.

While telcos stand to benefit the most in the short term, other sectors are close behind. Automotive manufacturers have already embedded 4G and 5G SIMs into vehicles. With NTN connectivity, they can now ensure those cars remain connected wherever they go, across deserts, mountains or other terrains, by partnering directly with satellite operators as roaming partners.

Other beneficiaries include maritime and aviation, where better bandwidth and interoperability between terrestrial and satellite networks will enhance passenger services and operational safety; energy and natural resources, where private 5G networks can now extend to offshore rigs or remote mining sites via satellite backhaul; and IoT providers, who can deploy sensors in areas previously unreachable by cellular networks.

Bridging Two Worlds: 乌鸦传媒 Engineering鈥檚 Unique Position

As the boundaries blur between telecoms and satellite ecosystems, few companies have experience in both. 乌鸦传媒 Engineering, whose roots in satellite communications stretch back to the early 1990s, occupies a rare position at the intersection of these worlds. We combine decades of telecom systems integration expertise with deep engineering knowledge of satellite platforms, ground networks, and standardization processes.

乌鸦传媒 Engineering is supporting both satellite operators and telcos as they adopt 5G NTN technology, becoming a trusted engineering partner throughout this transition. For telcos, our work includes helping MNOs assess potential satellite partners, enabling TN鈥揘TN integration, and delivering hybrid networks tailored to vertical markets. We are supporting remote operations for global energy providers, enhancing drone coverage for defence organizations, and improving connectivity on the move for aviation and maritime industries. For satellite network providers and infrastructure vendors, we help build 5G NTN payloads and ground systems using our 5G RAN/Core technology stack, and integrate next-generation infrastructure with legacy platforms, telco networks, and satellite operations systems, managing the final system to ensure its performance.

Our heritage in both telecoms and satcoms allows us to act as interpreters and integrators between two sectors that are only just beginning to understand each other, ensuring that non-terrestrial and terrestrial networks converge smoothly, securely, and at scale.

A Connected Horizon

As the next 3GPP Release draws near and D2D services mature, the notion of 鈥渘o coverage鈥� may soon become obsolete. The integration of terrestrial and non-terrestrial networks promises to democratize connectivity on a planetary scale, a development as strategic for nations as it is transformative for industries.

For operators, it represents a path to renewed growth. For enterprises, it means uninterrupted digital operations wherever they do business. And for technology partners like 乌鸦传媒 Engineering, it is a chance to help shape the architecture of a network that finally spans the entire globe, from the factory floor to earth orbit.

To learn more about how we engineer smart networks and networks operations, contact us at engineering@capgemini.com

AI is reshaping the way enterprises operate, but one fundamental challenge that still exists is that most applications were not built with AI in mind.

Traditional enterprise systems are designed for transactions, not intelligent decision-making, making it difficult to integrate AI at scale. To bridge this gap, MongoDB and 乌鸦传媒 are enabling businesses to modernize their infrastructure, unify data platforms, and power AI-driven applications. This blog explores the trends driving the AI revolution and the role that 乌鸦传媒 and MongoDB play in powering AI solutions.

The challenge: Outdated infrastructure is slowing AI innovation

In talking to many customers across industries, we have heard the following key challenges in adopting AI:

• Data fragmentation: Organizations have long struggled with siloed data, where operational and analytical systems exist separately, making it difficult to unify data for AI-driven insights.
  
  In fact, according to the , 59 percent of C-suite executives said their organizations’ data is somewhat or completely siloed, which results in inefficiencies and lost opportunities. Moreover, AI workloads such as , , and recommendation engines require vector databases, yet most traditional data architectures fail to support these new AI-driven capabilities.
  
• Lack of AI-ready data infrastructure:The lack of AI-ready data infrastructure forces developers to work with multiple disconnected systems, adding complexity to the development process.
  
  Instead of seamlessly integrating AI models, developers often have to manually sync data, join query results across multiple platforms, and ensure consistency between structured and unstructured data sources. This not only slows down AI adoption but also significantly increases the operational burden.

The solution: AI-ready data infrastructure with MongoDB and 乌鸦传媒

Together, MongoDB and 乌鸦传媒 provide enterprises with the end-to-end capabilities needed to modernize their data infrastructure and harness the full potential of AI.

MongoDB provides a flexible document model that allows businesses to store and query structured, semi-structured, seamlessly, a critical need for AI-powered applications. Its vector search capabilities enable semantic search, recommendation engines, RAG, and anomaly detection, eliminating the need for complex data pipelines while reducing latency and operational overhead. Furthermore, MongoDB鈥檚 distributed and serverless architecture ensures scalability, allowing businesses to deploy real-time AI workloads like chatbots, intelligent search, and predictive analytics with the agility and efficiency needed to stay competitive.

乌鸦传媒 plays a crucial role in this transformation by leveraging AI-powered automation and migration frameworks to help enterprises restructure applications, optimize data workflows, and transition to AI-ready architectures like MongoDB. Using generative AI, 乌鸦传媒 enables organizations to analyze existing systems, define data migration scripts, and seamlessly integrate AI-driven capabilities into their operations.

Real-world use cases

Let’s explore impactful real-world use cases where MongoDB and 乌鸦传媒 have collaborated to drive cutting-edge AI projects.

• AI-powered field operations for a global energy company: Workers in hazardous environments, such as oil rigs, previously had to complete complex 75-field forms, which slowed down operations and increased safety risks. To streamline this process, the company implemented a conversational AI interface, allowing workers to interact with the system using natural language instead of manual form-filling. This AI-driven solution has been adopted by over 120,000 field workers, significantly reducing administrative workload, improving efficiency, and enhancing safety in high-risk conditions.
• AI-assisted anomaly detection in the automotive industry: Manual vehicle inspections often led to delays in diagnostics and high maintenance costs, making it difficult to detect mechanical issues early. To address this, an automotive company implemented AI-powered engine sound analysis, which used vector embeddings to identify anomalies and predict potential failures before they occurred. This proactive approach has reduced breakdowns, optimized maintenance scheduling, and improved overall vehicle reliability, ensuring cost savings and enhanced operational efficiency.
• Making insurance more efficient: GenYoda, an AI-driven solution developed by 乌鸦传媒, is revolutionizing the insurance industry by enhancing the efficiency of professionals through advanced data analysis. By harnessing the power of MongoDB Atlas Vector Search, GenYoda processes vast amounts of customer information including policy statements, premiums, claims histories, and health records to provide actionable insights.
  
  This comprehensive analysis enables insurance professionals to swiftly evaluate underwriters’ reports, construct detailed health summaries, and optimize customer interactions, thereby improving contact center performance. Remarkably, GenYoda can ingest 100,000 documents within a few hours and deliver responses to user queries in just two to three seconds, matching the performance of leading AI models. The tangible benefits of this solution are evident; for instance, one insurer reported a 15% boost in productivity, a 25% acceleration in report generation 鈥� leading to faster decision-making 鈥� and a 10% reduction in manual efforts associated with PDF searches, culminating in enhanced operational efficiency.

Conclusion

As AI becomes operational, real-time, and mission-critical for enterprises, businesses must modernize their data infrastructure and integrate AI-driven capabilities into their core applications. With MongoDB and 乌鸦传媒, enterprises can move beyond legacy limitations, unify their data, and power the next generation of AI applications. For more, watch this by Steve Jones (EVP, Data-Driven Business & Gen AI at 乌鸦传媒) and Will Shulman (former VP of Product at MongoDB) to learn about more real-world use cases. And how 乌鸦传媒 and MongoDB are driving innovation with AI and data solutions.

Read more about our collaboration with MongoDB here.

Generic proposals are losing deals

Insurance RFP responses are starting to feel like they鈥檝e been photocopied over and over. Brokers and clients today are no longer just flipping through proposals hoping to find a winner鈥攖hey鈥檙e expecting them to speak directly to their unique needs. The days when you could get away with templated, one-size-fits-all responses are behind us. In insurance, trust is built on understanding, and understanding is signaled through specificity.

In fact, many proposals don鈥檛 even get past the first skim because they sound like they were written for any client, not this client. The root issue is that generic responses signal a lack of investment in the relationship. Insurers risk losing out on high-value deals, wasting time and resources crafting responses that don鈥檛 convert. As our work with numerous global insurers has revealed, many of these generic documents鈥攅specially cover letters and executive summaries鈥攚ere not even being read by brokers due to their lack of relevance.

Generative AI for hyper-personalization in insurance

Now, let鈥檚 imagine a private, enterprise-trained generative AI assistant that doesn鈥檛 just regurgitate past language, but crafts messages so tailored they make your clients feel like VIPs. That鈥檚 the magic of a custom, private GenAI assistant.

This assistant is no off-the-shelf chatbot. It鈥檚 trained on your historical RFP data, your previous client interactions, your industry nuances, and even your internal product literature. It understands how you communicate and what your clients care about. More importantly, it learns and evolves. With the help of Agentic AI, a modular framework powered by specialized AI agents, this assistant goes far past simple auto-fill. It reads the RFP, summarizes the client ask, constructs the top winning themes, and proactively drafts personalized responses, summaries, and even intelligent suggestions for improvement.

This is where hyper-personalization becomes real. By utilizing structured and unstructured data alike, the Gen AI assistant pulls out the most relevant insights and shapes them into messaging that resonates. It compiles data from its entire knowledgebase to craft a tailored solution to the client鈥檚 problem. It’s not guessing, it’s contextualizing. That means proposals land stronger, faster, and with far better chances of hitting the mark.

MongoDB: The motor powering AI-driven personalization

Behind the scenes, plays a crucial role in making all this magic possible.

Their flexible document model allows for rapid ingestion of diverse data types including past RFPs, client correspondence, marketing decks, and everything else imaginable. This structure is perfect for insurers juggling massive volumes of semi-structured and unstructured data.

is particularly crucial here.  It enables the Gen AI assistant to rapidly identify, rank, and re-rank the most relevant information based on contextual relevance, delivering responses that are both timely and precise.

Its globally distributed architecture鈥攁vailable across AWS, Azure, and GCP in over 115+ regions鈥攎akes it an ideal foundation for building large-scale, enterprise-grade Gen AI applications. By embedding Vector Search directly into the core database, MongoDB eliminates the need to sync data between separate operational and vector databases. This simplification reduces complexity, minimizes the risk of errors, and significantly shortens response times.

Keeping both operational and vector data in a single system also improves performance through reduced latency and advanced indexing capabilities. For organizations building out agentic Gen AI capabilities, MongoDB further supports Graph RAG (Retrieval Augmented Generation) architectures, enhancing contextual accuracy and scalability across use cases.

However, insurance is a heavily regulated industry and data security is critical. MongoDB also offers enterprise-grade encryption, access controls, and supports compliance with key data privacy regulations.

Case study: Less robotic, more calibrated and compelling RFPs at a global insurer

A recent standout example of our custom, private GenAI assistant in action comes from a global insurer who started with a modest request: Can we hyper-personalize our RFP cover letters better? The ask was simple and they were merely looking for a few bullet points to make things feel less robotic.

What we were able to create for them was a revolution in how they respond to RFPs. In just five weeks, our team implemented our custom, private GenAI assistant that not only delivered personalized bullet points but also crafted full executive summaries and tailored cover letters. These were not piecemeal templates鈥攖hey were coherent, compelling, and calibrated to the specific opportunity at hand.

The feedback we received was immediate and enthusiastic. The Chief Innovation Officer and the Sales leadership team pushed for scaling the solution to other areas. It wasn鈥檛 just a productivity gain, it was a reputation builder. Brokers began to take notice. The insurer wasn鈥檛 just responding faster; they were responding smarter.

Business impact, check! Strategic outcomes, check!

By implementing a custom, private GenAI assistant, insurers gain access to a scalable, cloud-native platform that integrates easily with existing systems鈥攚hether it鈥檚 a CRM, document management platform, or internal knowledge base. Beyond the technical flexibility, the real impact lies in how this approach transforms stagnant, siloed data into living insights that power tailored client engagement.

The platform supports more consistent and efficient proposal development by reducing manual effort, accelerating turnaround times, and improving the quality and relevance of responses. Teams can focus less on reformatting and more on building client relationships. Meanwhile, the built-in security and governance measures ensure that every interaction meets enterprise compliance standards, protecting both client data and institutional knowledge.

Insurers using this model report stronger broker engagement, better win rates, and faster RFP response times. Operational costs drop due to reduced manual formatting and response drafting. From a technical perspective, compared to full LLM inference on raw content, thanks to targeted document retrieval and short-form reasoning tasks.

As organizations use this solution over time, feedback loops from won/lost deals can be fed back into the model for retraining, improving response quality and alignment. As the assistant matures, it can serve as a strategic enabler across adjacent workflows鈥攃laims review, renewal briefs, or even sales coaching.

The future of insurance RFPs

Custom private GenAI assistants represent a rare intersection of technical maturity and business impact. When combined with MongoDB鈥檚 robust data orchestration capabilities and 乌鸦传媒鈥檚 proven technology blueprint, this solution becomes more than a digital enhancement鈥攊t becomes a strategic advantage.

Organizations that embrace this model transition from reactive, templated proposal development to proactive, context-rich client engagement. With the ability to generate intelligent, personalized content at scale, they not only improve operational efficiency but also strengthen their competitive position in a high-stakes market.

This isn鈥檛 just about responding faster鈥攊t鈥檚 about responding better. As expectations around relevance, precision, and value continue to rise, the future of insurance RFPs will belong to those who invest in intelligent automation and meaningful personalization.

The path forward isn鈥檛 generic. It鈥檚 personal, scalable, and ready to deliver lasting impact.

Read at leisure. Download a copy of this expert perspective.

To drive business value, it is important to leverage all the data from within your organization as well as from partners outside of it. Such a collaborative data ecosystem is an alignment of business goals, data, and technology, among one or more participants, to collectively create value that is greater than each can create individually. It is both combining and collaborating on that data.

With a little help from your friends

John Lennon and Paul McCartney met by chance in 1957 when Lennon鈥檚 band The Quarrymen was performing in Liverpool. McCartney then joined The Quarrymen and, after the band had already changed its name to The Beatles, they were by chance discovered by Brian Epstein, at that time a local record store manager who became the band鈥檚 manager in 1962.

The way we see data ecosystems is similar: it is sometimes about a chance encounter and then bringing various elements together. We could refer to the well-known Beatles song from 1969 Come Together as the unifying theme of this article but instead let鈥檚 choose another one, namely With a Little Help from My Friends, which was released in 1967. In the context of this story, a little help comes in the form of a little data. Bringing together data from your friends (customers, suppliers, partners, vendors, whoever) is what we would call 鈥渙rganized serendipity.鈥�

Imagine you鈥檙e a retailer operating in a competitive market needing to stay on top of trends, having to make sure your shelves (whether physical or virtual) are filled and are appealing to your customers. As an example, out-of-stocks remain the single largest problem in retail. The challenge with keeping products stocked involves a complex value chain that must anticipate and respond to dynamic market forces. Extreme weather, local events, and even activity from social influencers can quickly alter the demand for a product. In an optimal world, suppliers, distributors, retailers, and other partners would have visibility to changing dynamics and consumption in real-time, enabling them to optimize their operational decisions on-the-fly. And yet supply chains across retail and consumer goods still operate much as they have for decades, making decisions on data that is days or weeks old. It is this delay between changes in demand and our ability to respond that lead to out-of-stocks.

The main sources for retail data are operations by the retailer, data from their ecosystem, competitive data from syndicated sources, and external environmental data from governments and commercial sources.

• Retail operational data comes as a result of business operations, and includes everything from customer-facing retail sales data, advertising, e-commerce, customer support, reviews, and loyalty to back-of-house data from inventory, distribution, planning, and other management systems.

• Retailers operate in a complex value chain, with data coming upstream from suppliers, wholesalers, and distributors, and integrating downstream with advertising and delivery partners.

• Competitive data sources help retailers understand how their key competitors are operating in similar areas. Competitive distribution, assortment, pricing, promotions and advertising, sales, and other sources help retailers index their performance.

• Environmental data helps retailers understand the context in which consumers are making decisions. This includes environmental data such as weather, local economic forces, census information, local events and foot traffic data, legal and regulatory changes, social data, keyword searches, and more.

Finding a cost-effective technology

No two organizations leverage the same data in the same way. The differences in their strategies, operations, competitors, geography, and the systems that support them are designed to help the company succeed. But this means that no two businesses have the same data ecosystem. Companies may exchange data in key areas but increasingly the differences in data between companies is perceived as a competitive advantage. Legacy data-sharing technologies were designed to support the lowest common denominator of collaboration but have struggled to meet the needs of real-time data sharing, quality, and governance and decisioning. Companies want the flexibility to communicate in real-time with a variety of information and across platforms.

The key to achieving this is to select a cost-effective technology that enables the broadest range of sharing options without proprietary technology or vendor lock-in, facilitates real-time data sharing and collaboration, ensures the control of quality and governance of data, and enables companies to focus on immediately leveraging all types of data to drive better decisions.

A retail lakehouse simplifies collaboration

A data lakehouse is a modern data-management architecture that combines the features of both data lakes and data warehouses. It is a unified platform for storing, processing, analyzing, and sharing large volumes of data, both structured and unstructured, in its native format, with support for batch and real-time data processing.

Databricks鈥� Lakehouse is built on open-standards and open-source, which avoids proprietary lock-in. This importantly extends to data sharing and collaboration. Databricks introduced Delta Sharing, which is an open-source project started by Databricks that allows companies to share large-scale, real-time data between organizations in a secure and efficient manner.

A Lakehouse is the optimal method for data collaboration as it addresses the critical needs in retail.

• Real-time collaboration. Not only can companies share data that is being continuously updated, but Delta Sharing also enables sharing without movement of data.

• Collaborate on all of your data. Unlike legacy systems, Delta Sharing enables companies to share images, video, data-science models, structured data, and all other types of data.

• Centralized data storage. The Lakehouse architecture makes it easier for different users or groups to access and share data from a single source of truth, eliminating data silos and enabling seamless data sharing across various stakeholders.

• It supports quality and compliance. A Lakehouse architecture helps ensure data integrity, traceability, and compliance with regulatory requirements, which are important considerations when sharing data with external users or organizations.

• It simplifies data management and discovery. The Lakehouse architecture includes a robust data catalog and metadata management system that helps in documenting and organizing data assets.

鈥淐ollaborative data ecosystems hold immense potential for retail companies looking to thrive in an increasingly competitive and data-driven industry.鈥�

With Delta Sharing, companies can securely share data with other organizations without having to copy or move data across different systems. Delta Sharing uses a federated model, which means that data remains in the original location and is accessed remotely by the recipient organization. This approach allows organizations to maintain control over their data while still sharing it with others.

Collaborative data ecosystems hold immense potential for retail companies looking to thrive in an increasingly competitive and data-driven industry. By leveraging these ecosystems, retailers can optimize their supply chain, gain valuable customer insights, make informed decisions, foster collaboration, and ensure data security and compliance. As more organizations recognize the value of such ecosystems, we can expect the retail industry to become even more connected, efficient, and customer-centric.

INNOVATION TAKEAWAYS

EMPOWERING COLLABORATION

By leveraging data from within and outside their organization, businesses can create collective value that surpasses individual capabilities, fostering collaboration and innovation.

BRIDGING THE GAP

Outdated supply chains hinder retailers from effectively responding to dynamic market forces, making real-time data sharing imperative for optimizing operational decisions and reducing out-of-stock issues.

LAKEHOUSE ARCHITECTURE

A modern data-management approach, the Lakehouse architecture combines data lakes and data warehouses, enabling real-time collaboration, centralized storage, and simplified data management for improved decision-making.

DELTA SHARING

Delta Sharing, an open-source project, empowers companies to securely share large-scale, real-time data without data movement, unlocking the potential for seamless collaboration, compliance, and valuable insights in the retail industry.

Interesting read?

乌鸦传媒鈥檚 Innovation publication, Data-powered Innovation Review | Wave 6 features 19 such fascinating articles, crafted by leading experts from 乌鸦传媒, and key technology partners like ,  ,  ,  and . Learn about generative AI, collaborative data ecosystems, and an exploration of how data an AI can enable the biodiversity of urban forests. Find all previous waves here.

鈥淎s manufacturers face increasing pressure to deliver faster, smarter, and more sustainable operations, the way we design and build factories is undergoing a radical transformation. At 乌鸦传媒, we鈥檝e been working with global leaders to rethink traditional approaches – leveraging digital twin technology to bring agility and intelligence to the factory floor.鈥� 

鈥�  Alexandre Embry  

A global consumer products company wanted to make building new factories simpler, smarter, and more efficient. Instead of starting from scratch each time, we helped them create a digital tool that lets teams design and compare factory setups virtually, choosing everything from product types to packaging lines. With built-in visuals, data dashboards, and AI-powered insights, the tool is now helping them plan better, move faster, and make more informed decisions. 

Reimagining factory design for the digital era

Designing a new factory is a complex, capital-intensive endeavor. Our client wanted to eliminate disruption points and boost both capital efficiency (CapEx) and operational efficiency (OpEx). The question: how could they standardize factory design globally while tailoring it to specific consumer goods?  

So, we innovated the process from the ground up. Instead of treating each new factory as a bespoke project, we built a plant configurator that lets engineers design production lines using a modular and digital-first approach. From selecting product types and packaging sizes to choosing suppliers and automation levels, users can now configure entire factories digitally, complete with 3D models, scanned documents, and real-time KPI dashboards. 

Building the Digital Twin: How we made it real 

We assembled an innovation team of business experts, data modelers, business analysts, 3D and digital twin specialists, and programmers, to develop the Digital Twin Configurator. Our solution helps create new digital twin content dynamically, on demand. To achieve this, we leveraged our Digital Twin Cockpit solution based on Microsoft assets and developed as part of 乌鸦传媒鈥檚 AI Robotics and Experiences Lab. It merges the assets built in our Lab with Microsoft data, AI and cloud standards, such as Copilot, Power BI, and several Azure components, enabling faster and consistent review of source standards and produced plant models. 

The tool guides users through each step of setting up a new production line鈥攍etting them choose product types, factory layouts, and equipment options, much like customizing a kitchen. Teams can compare different designs based on cost, energy use, and water consumption. The AI speeds up data entry, and built-in dashboards help track key metrics like emissions and operating costs. 

One of the biggest challenges was making sure the tool could handle many different factory types and still keep everything connected from the first design to final construction. 

Results delivered and the road ahead 

Our client now has a centralized, standardized, and replicable architecture for factory design. The digital twin configurator enables: 

• Setting up factories faster and more efficiently 

• Making smarter decisions about where to invest and how to maintain equipment 

• Comparing different factory setups using key data like energy use, water consumption, and operating costs 

The system is already helping top management make data-driven decisions. As the configurator evolves, it鈥檚 poised to become a blueprint for global factory design鈥攕calable, smart, and sustainable. 

Learn more about our AI Robotics & Experiences Lab