Digital Twins & Quantexa: A think piece
This is an innovation think piece that lives solely in Q Labs. Q Labs is a space for innovation products and experimental thinking, as such they are for awareness and co-innovation interest only. Q Labs Status: Ideation Background Digital Twins are a major buzzword in the technical world, especially in the innovation space. At its core, a Digital Twin (DT) is a digital or virtual representation of a real-world system, process, or entity, fully mapped into the virtual space. A great way of visualizing this is by looking at the laptop or computer in front of you. It is real and exists in the physical space. Now, think about all the data it holds, its connections to its power supply, the internet, its internal fans, batteries and wires. Consider its current temperature, any damage it has received, its diagnostics, version and size. Imagine accessing all this information in a single virtual view. What if you could combine all this data to provide suggestions on how to maximize its outputs? Going a step further, you could use this information to simulate how it would react in a new environment, would it break, what are its limits? This is where the power of a Digital Twin comes into play. It offers a perfect virtual copy with the structure to easily access information you need and the power to simulate situations. The market for digital twins is still emerging, but expanding rapidly. Gartner projects that by 2034, global revenue for simulation digital twins will reach $379 billion. Major technology players are already investing heavily in digital twins—and for good reason. While the concept is new, much of the required technology is already available. However, building a near real-time, high-fidelity digital replica of a system capable of automating tasks, delivering large-scale analytics, and informing strategy is no small feat. Quantexa, however, has the unique capabilities and expertise to take on parts of this challenge. I have highlighted a few key sections below that we are focusing on. Supply Chain Focus: A Supply Chain Digital Twin is a virtual representation of a company’s entire supply chain, enabling organizations to simulate, analyze, and optimize operations in real time. Unlike DTs of physical objects, such as wind turbines, modeling an "invisible" system like a supply chain is significantly more complex. A supply chain DT integrates data from factories, warehouses, transportation networks, retailers, and even customer interactions to predict how various market conditions and supply chain parameters will influence outcomes. By mirroring real-world supply chain dynamics, businesses can stress-test scenarios, improve forecasting accuracy, and mitigate risks. The benefits of an effective supply chain DT are substantial. By optimizing inventory levels, organizations can reduce costs while maintaining higher service levels, leading to increased revenue and improved profit margins. Enhanced forecasting also boosts the success rate of new products and services, giving companies a competitive edge. Despite these advantages, many systems are inaccurately labeled as digital twins, often due to a lack of understanding of what a true DT entails. A well-designed supply chain DT should not only provide real-time insights but also deliver actionable intelligence, allowing businesses to make data-driven decisions with confidence. Simulations: Simulations are a fundamental component of Digital Twins, enabling organizations to test scenarios, optimize processes, and minimize risks when making data-driven decisions. By creating a virtual environment that mirrors real-world conditions, simulations allow businesses to explore "what-if" scenarios. Following the Supply Chain Focus, we can use these to simulate how a supply chain would be affected by demand fluctuations, equipment failure or disruptions. Giving the ability to get predictive data on these scenarios without impacting actual operations. This capability allows companies to identify vulnerabilities, improve efficiency, and test strategies before implementing them in the real world. Beyond risk mitigation, simulations can be a driving force for improvement and future innovation. Businesses can experiment with emerging technologies in a controlled digital space. They can try out new processes, or test and fine-tune operations, providing validation for changes that would otherwise be theoretical. Whether optimizing inventory levels, evaluating the impact of sustainability initiatives, or stress-testing logistics networks, simulations empower organizations to stay ahead in an ever-changing market. This is an area the innovation team have already started to prototype. If you would like to learn more, please see our dedicated blog and demo Simulation Lab. News Intelligence: In our connected world it is rare for any system to exist in a vacuum. As such having a DT without having outside sources connected to it, is like building a wind turbine before checking the average wind speed in that location. Integrating a Digital Twin with external data sources and news articles unlocks an extra layer of potential. News intelligence enhances Digital Twins by continuously analyzing global news data to extract insights relevant to supply chains. This integration enables organizations to identify emerging risks, predict disruptions, and assess market sentiment in real time. By incorporating geopolitical events, natural disasters, supplier issues, and economic shifts into their models, businesses gain a dynamic, adaptive view of their operations. This real-time awareness improves resilience, allowing companies to proactively mitigate risks, optimize logistics, and maintain operational stability in an increasingly unpredictable world. This is an area the innovation team have already started to prototype. If you would like to learn more, please see our dedicated blog and demo Q News Intelligence for Supply Chain Monitoring. Its Future As you can probably tell there is a lot of work and exploration still to be done in this space. Digital Twins are not only a huge investment but are still an uncertain one. Splitting this huge idea into bite sized chunks helps create achievable and value driven results on a continuous scale. The future in this space is expansive and having decision intelligence at Quantexa’s core places us in a prime position to lead the way in this growing market. We do, however, know that these great achievements don’t happen in a vacuum and as such would love to hear from yourselves about Digital Twins, potential use cases within your company and reactions to this popularized buzz-word. Please click here to get in touch and leave us any feedbackInnovation History of Q Assist
Q Labs Status: Graduated ✅ The Q Assist Story Since last year, Q Assist–our context-aware Gen AI suite–has been a key item on our product roadmap, with a major new release expected in the first half of this year. With the launch of Q Labs in March 2025, we thought now would be a good time to look back at Q Assist’s journey from an innovation prototype to a mature production offering. For the latest information on Q Assist as a product offering, please refer to the blog: Your AI Copilot is Only as Good as Your Data Foundation The initial idea for Q Assist came to us in March 2023–a couple of months after OpenAI introduced ChatGPT–from one of our colleagues who had created a very convincing mock-up version of an LLM-powered assistant for Quantexa in PowerPoint! Impressed by this, the Product Innovation team rolled up their sleeves and implemented an MVP as a browser extension in a matter of a couple of weeks, that sat alongside the Quantexa UI and fed the data and insights surfaced by Quantexa to an LLM, allowing the user to ask questions, generate summaries or reports, and choose from a library of pre-defined prompts using the Prompt Gallery. Equipped with the MVP, we started engaging with our stakeholders internally and together we planned a series of validation and feedback gathering steps with our customers and partners. Numerous demos and conversations with potential users later, we had a clear understanding of the potential use cases, the challenges, and the benefits of Q Assist, which then allowed our colleagues in engineering, product management, design, and marketing to plan the next phase of development for Q Assist, ensuring we have a robust product, engineering, and GTM strategy for a successful release of Q Assist as a core feature of our platform. Crucially, we stayed close to our customers and partners throughout this process, as we believe innovation must be market oriented in order to be successful, and see co-innovation with customers and partners as a core tenet of how Q Labs operates. In addition to the many validation sessions with our customers, we launched a lighthouse program in June of last year to work very closely with clients such as HSBC and BNY in order to ensure we build Q Assist in such a way that makes a significant impact on the value they get from Quantexa and ultimately drive tangible efficiency and effectiveness gains for their organisations. What's Next This all brings us to QuanCon 2025, where we’re making a major announcement around Q Assist as a core product capability, so please make sure you tune in to QuanCon as we won’t be stealing their thunder here! Lessons Learnt Looking back, we identify several factors that made Q Assist a successful innovation project, which are worth sharing with other innovation teams out there: Look for good ideas everywhere: Good ideas can be found practically everywhere, and innovators can catch them if they keep an open mind and widen their exposure. Effective prototyping is your best friend: Rather than spending months building prototypes, sometimes it’s much more effective to spend weeks building a working prototype, and months validating it with potential users and customers. Close collaboration with customers and partners from an early stage: Innovation can be much more effective if it’s done collaboratively with clients who have a need for your idea. Don’t be embarrassed to share your prototypes or concepts early– the startup mantra of “if you’re not embarrassed by your first release, you’ve waited for too long” applies to innovative efforts in larger companies too. In fact, this is what encouraged us to set up Q Labs as an innovation identity for Quantexa. Internal alignment across innovation, product, and GTM teams is crucial for ensuring a continuous and smooth transition from innovation prototypes to robust production implementations. Support from senior leadership is also crucial for ensuring innovation projects can move at pace, and not be slowed down by internal processes. Get in Touch If you are an innovator or work as part of an innovation team, we would love to hear about your experience and learnings, so please feel free to comment below or reach out to us. Please click here to get in touch and leave us any feedbackSimulation Lab
This is an innovation prototype that lives solely in Q Labs. Q Labs products are experimental and pre-roadmap, as such they are for awareness and co-innovation interest only. Q Labs Status: Experimentation Background In an increasingly complex business environment, simulations are a key component of any organization’s toolkit for making optimal decisions. Combining simulations with domain models (ontologies) rooted in an organization’s data fabric, allows businesses to explore and evaluate many “what if” scenarios–like changes in market conditions, competitive moves, or operational disruptions–without real-world consequences. Simulations serve many use cases across industries: Industry Use Cases Banking Credit & Market Risk: Stress-test portfolios under adverse economic scenarios to anticipate defaults and downturns. Operational Risk: Model internal processes to identify vulnerabilities and improve internal controls. Threat Intelligence: Simulate emerging risks (e.g., cyber fraud) to enhance proactive risk management. Insurance Catastrophic Events: Simulate geopolitical and natural disasters to forecast potential claims distributions. Underwriting Optimization: Use simulation insights to adjust premium pricing and design effective reinsurance strategies. Government Cybersecurity: Test how cyber-attacks might impact critical infrastructure and public safety. Policy Impact: Simulate long-term economic and social effects of policy interventions. Regulatory Oversight: Inform regulation design to bolster national resilience and public welfare. Corporations Supply Chain Resilience: Model disruptions from geopolitical events and external shocks to identify bottlenecks and optimize logistics. Geopolitical Event Modelling: Anticipate impacts from global events to support strategic planning. Threat Intelligence: Integrate competitive and operational risk data to enhance agility and decision-making. Given the significant role of simulations in Decision Intelligence, we set out to explore potential innovations in this area. Motivation Despite the wide use of simulations in the industry, most simulation approaches–whether based on rules or machine learning models–remain primarily quantitative and rely on human experts to encode their domain knowledge into models and model parameters. The advent of Large Language Models (LLMs) has opened new possibilities in the world of simulations. Given the breadth of their training data –often the entire textual contents of the internet– and their reasoning abilities, LLMs are inherently good at understanding the relationship between events and entities and can understand cause-and-effect relationships for instance, to generate many potential “effects” for a cause event and vice versa. By leveraging this quality of LLMs we can augment human experts’ ability and knowledge to consider a wider range of scenarios. Moreover, by using LLMs as a bridge between qualitative scenario generation and quantitative analysis, we can automate the large-scale evaluation of many “what if” scenarios. Algorithms such as Monte Carlo Tree Search (MCTS) allow us to identify the most promising scenarios to flag to human experts, bringing significant efficiencies to the process of building robust and large-scale simulation systems. How it Works Simulation Lab is our LLM-powered platform that transforms raw data into actionable future scenarios. Here’s a quick rundown of its process: Entity-Event Graph Construction: The system builds a dynamic graph that connects real-world entities (such as companies or public agencies) with a series of time-ordered events. Each event is assigned a likelihood and a severity score, while “leads to” relationships capture the causal flow between events. Scenario Generation: Using our large language model, Simulation Lab expands an initial set of user-defined events. Users can guide the LLM with focused prompts (e.g., “focus on geopolitical consequences”) and adjust parameters like likelihood, severity, and randomness (temperature) to explore a wide range of potential outcomes. Interactive Exploration: A user-friendly, point-and-click interface lets experts build and refine their entity graphs. This visual tool allows you to map out relationships between key actors and drill down into how specific events might cascade through your network. Actionable Insights: The result is a curated set of future scenarios that highlight emerging risks and opportunities. These scenarios can then be used to inform decision-making processes, from risk management to strategic planning, as well as to create alerts for early detection of any leading indicators. By merging expert knowledge with the generative power of LLMs, Simulation Lab bridges the gap between qualitative foresight and quantitative analysis, enabling more robust decision intelligence. Conclusion In conclusion, Simulation Lab represents a forward-thinking approach to decision intelligence by merging the generative power of LLMs with expert insights. This simulation environment transforms raw data into dynamic, actionable scenarios, empowering organizations to proactively anticipate risks and seize opportunities in an ever-changing landscape. As we continue to refine and expand this experimental prototype within Q Labs, we invite collaboration and feedback to help shape its evolution and maximize its impact on strategic planning and risk management. Get in Touch If you like to hear more about Simulation Lab, please get in touch with us, as we are actively exploring different options for how to take it forward and are open to collaboration. Please click here to get in touch and leave us any feedbackKnowledge Assistant: grounding copilots in an organization's knowledge
This is an innovation prototype that lives solely in Q Labs. Q Labs products are experimental and pre-roadmap, as such they are for awareness and co-innovation interest only. Q Labs Status: Experimentation Background A key problem for many companies is consolidating internal knowledge. This knowledge exists in multiple areas, from formal document sites and community blogs to code repositories and bug fix conversations. Searching for the right information can be a time-consuming task and, for less experienced employees, sometimes inaccessible. We aim to unify a company's knowledge, making it quickly and efficiently accessible. Copilots already provide a convenient chat interface people can use to find information, and have become an essential tool for enhancing productivity, supporting decision-making, and reducing the workload of their users. Whether you use them daily or believe they will take over your job, it is hard to argue with the change that this technology has brought about. But like most technology, copilots are only as good as the data they are working from, grounding this tech in context has the ability to unlock their true potential. Without a deep understanding of the specific environment, data, or nuances of the task at hand, even the most advanced copilot can falter. Combining the knowledge of a company with said copilot is an efficient step to solve this knowledge gap. Knowledge Assistant enables contextual knowledge to transform a copilot from a generic assistant into a trusted and effective collaborator. The Uses Following extensive research and feedback discussions, we have highlighted below some validated uses of this Knowledge Assistant alongside some of the expected benefits. Persona Use Cases Benefits Support team: working in the support and triage space A customer support ticket comes through regarding a new product for which no one on the team is an expert. They use Knowledge Assistant to find where the detailed documentation is and who made it in case they need further details. Reduces response times to customers, supporting and improving KPI’s Reduces support teams' workload by supporting information collation and identification. Improve the diversity of information available on hand to support tickets Enhances support information response Customer: a technical delivery person working and learning in Quantexa product. A delivery engineer new to a project and working in a different time zone from the rest of the team. They find a bug that isn’t related to the libraries they have used before, and the support team has logged off for the day. They use Knowledge Assistant to search for information and instantly receive a high-level overview of what the bug could be and a link to a support conversation solving this bug for someone else. It also tells them what the acronyms stand for in this product. Time save on accessing this information Give the ability to send a question and get an instant response in the IDE Reduce repeated queries and support tickets by providing instant access to self-help resources. Improvement in the user experience when looking for Quantexa specific information. Accelerate junior engineers learning Internal Sales: support and content creation A pre-sales person who has just received an RFP related to an area of the organization they are unfamiliar with. They use Knowledge Assistant to quickly access non-technical information on this area, including the relevant products they should include. They also ask Knowledge Assistant to include any RFP responses for similar questions from Salesforce. Increase time to access data, efficiently accelerating initial responses Accelerate time to create new content by having consolidated materials Improve ease of creating new content Ensure tone of all sales content is the same Content creator: product specific educational material A content creator in the organization is looking to see which area of the product lacks educational materials. They use the statistics from Knowledge Assistant to discover that the topmost common questions all relate to a certain product. They then use Knowledge Assistant to create draft content based on these gaps using the writing style of the other educational material and the documentation. Support identifying gaps in knowledge as well as gaps in documentation Accelerate upkeep of information in educational material Time to alter all content when change is needed Ensure tone of all content is the same The Prototype So Far Knowledge Assistant is still in the prototyping phase of its innovation lifecycle. To start, we have built a copilot add-on that connects Quantexa’s documentation site and Community within GitHub’s copilot. These are two very distinct sources of Quantexa knowledge, so provide a good test to demonstrate how the LLM responds to this context. We utilize Microsoft’s Chat extensions feature, which allows VS Code extension developers to augment copilot in various ways, including introducing new participants (mentioned with @) This allows us to bring in additional data from other sources and pass them to the underlying LLM used within copilot. BEFORE Knowledge Assistant: AFTER Knowledge Assistant: We use chat extensions within a VS Code extension to introduce new chat participants with 3 different modes: @knowledgeassistant : retrieves and combines knowledge from Quantexa docs and Community. @knowledgeassistant / qdocs: retrieves knowledge from Quantexa Docs using a custom RAG pipeline powered by ElasticSearch and LlamaIndex. @knowledgeassistant / qcommunity: retrieves knowledge from Quantexa Community using its underlying search API. This works exceptionally well, as showcased in the example shown below. Before Knowledge Assistant, the copilot had no knowledge of Quantexa-specific terminology, whereas after it not only clearly defines what this library does but also shares links to additional articles on this subject. Its Future As you can probably tell this prototype presents a world of opportunities and advancements. We are looking into expanding this to more data sources, utilizing analytics to detect repeated questions, spot areas requiring more documentation, and eventually adjusting how responses look for particular types of questions. If you would like to hear more about Knowledge Assistant, please get in touch with us. We are actively exploring different options for how to take it forward and are open to collaboration. Please click here to get in touch and leave us any feedback
