From Insight to Action: Prototyping a Customer Intelligence Agent with Agentic AI
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 Agentic AI is the next frontier in artificial intelligence and has huge potential to transform organisations. While generative AI excels at creating content, agentic AI is about doing. It combines the reasoning power of Large Language Models (LLMs) with the ability to execute tasks, taking automation and human-computer collaboration to the next level. These autonomous systems can plan, act, and adapt to achieve complex goals with minimal human supervision. Quantexa is in a unique position to unlock and accelerate the value of this technology. For agentic AI to be effective, it requires high-quality, context-rich data to make reliable decisions. This is where our Decision Intelligence Platform provides a critical advantage. By creating a trusted data foundation through our world-class entity resolution and knowledge graphs, we provide the essential context that AI agents need to act with confidence. Ultimately allowing us to coordinate across multiple disparate systems. As such, Quantexa is currently exploring a wide variety of agentic use cases. This particular prototype focuses on the world of sales and customer relationship management, allowing us to test and demonstrate the technical capabilities Quantexa can bring to multi-system collaboration. The Challenge for Relationship Managers In today's data-driven world, Customer Intelligence (CI) is paramount. Relationship Managers (RMs) are expected to have a deep, 360-degree understanding of their clients to identify risks, spot opportunities, and provide exceptional service. However, the information they need is often locked away in disconnected systems—CRM platforms like Salesforce, internal databases, news feeds, and public registries. RMs spend countless hours manually gathering and piecing together this information, a process that is both inefficient and prone to missing crucial insights. Use Case: A Proactive Customer Intelligence Agent Imagine an AI assistant that eliminates this manual effort. Our prototype Customer Intelligence (CI) Agent is designed to be just that: a proactive partner for RMs. This agent can be tasked with open-ended goals, such as “provide product recommendations for company X” or " Summarise latest engagements with HSBC and point out latest investment opportunities? ". The agent then autonomously plans and executes a series of tasks to gather the necessary intelligence, using Quantexa as the source of contextual insights and glue to connect across systems, enabling the delivery of comprehensive responses for the RM. How It Works This CI Agent demonstrates the power of an AI agent backed by Quantexa, which acts as both a data-and-context engine and a suite of analytical tools. The agent, which is LLM-agnostic, is integrated with a full-stack chat application and has access to a range of tools to accomplish its goals. These tools include: Salesforce: Retrieving and updating information on Accounts, Contacts, Products, and creating new Leads and Opportunities. Quantexa: Leveraging our platform’s powerful search and graph expansion capabilities to uncover hidden relationships, risks and opportunities. News & Web Search: Searching the public web and premium news sources for the latest information on companies and people of interest. Companies House: Retrieving official information on UK-based companies to verify details and understand corporate structures. The project is built on Vercel’s open-source Chat SDK, providing a familiar, ChatGPT-like experience that can be integrated with multiple LLM providers. In the demo we can see the agent identifying opportunities for a particular company, Quantexa – but in reality, this could also be uncovering insights and taking action across a cohort of customers e.g. “identify customers in my portfolio that would benefit from our new Invoice Finance product”. The Benefits The importance of this project is in showing the great reach of what can be achieved using an AI agent backed by Quantexa both as a data glue as well as a set of investigation tools alongside other software products like Salesforce. Reduce manual effort and allow relationship managers to focus on proactive, personalized engagement with clients Accelerate revenue growth by automatically identifying signals for needs for products and services and driving timely outreach Enhance client experience by providing tailored service Get in touch If you would like to hear more about this CI Agent, or discuss how Quantexa’s Decision Intelligence platform can enhance your own AI agents, please get in touch. We are actively exploring different options for how to take this prototype forward and are excited to collaborate with partners and customers on this journey.Simulation 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
