-
Building internal AI-based recommendation engines
Building internal AI-based recommendation engines involves leveraging machine learning and data science techniques to create personalized, dynamic systems that suggest products, content, or services tailored to individual user preferences. These engines enhance user experience, increase engagement, and drive business growth by predicting user interests based on past behavior, contextual data, and patterns. Understanding Recommendation Engines…
-
Building internal campaign overviews with generative models
Building internal campaign overviews with generative models offers a transformative approach to marketing strategy and communication within organizations. Leveraging the power of AI-driven content generation enables teams to quickly synthesize complex campaign data into clear, actionable summaries, enhancing alignment, decision-making, and efficiency across departments. The Role of Internal Campaign Overviews Internal campaign overviews serve as…
-
Building LLM agents to identify gaps in customer journey
In today’s competitive business landscape, delivering a seamless customer experience is more crucial than ever. As customer expectations continue to rise, brands must not only meet them but anticipate and resolve potential issues proactively. One of the most effective ways to do this is by identifying and addressing gaps in the customer journey—those points where…
-
Building LLM-based bots for internal polling
In today’s rapidly evolving enterprise landscape, timely and accurate internal polling can significantly influence strategic decisions. Traditional methods, such as emails and forms, often fall short in terms of participation rates, real-time insights, and user engagement. Leveraging large language models (LLMs) to build conversational bots for internal polling presents a powerful, scalable solution. These AI-driven…
-
Building LLMs that coach in real time
Large Language Models (LLMs) have advanced rapidly in recent years, evolving from static information retrieval systems to dynamic tools capable of offering real-time, personalized guidance. The development of LLMs that can coach in real time represents a significant step toward creating AI systems that can support human learning, decision-making, and behavior change on the fly.…
-
Building model-aware document validation tools
In the era of intelligent systems and large language models (LLMs), document validation has evolved beyond simple schema checks and keyword matching. The growing demand for nuanced understanding, domain-specific logic, and human-like inference in digital content requires a shift toward model-aware document validation tools—systems that integrate machine learning models, especially language models, into the validation…
-
Building multi-agent collaboration systems with LLMs
In recent years, Large Language Models (LLMs) have transformed the way we think about automation, problem-solving, and communication. One of the most promising applications of these models lies in multi-agent collaboration systems, where multiple AI agents work together—often alongside humans—to achieve complex goals. These systems mimic the dynamics of human collaboration, enabling distributed intelligence, faster…
-
Building generative agents to support incident triage
In today’s digital ecosystem, systems operate at massive scale, and incidents are inevitable. Whether it’s a service outage, security breach, or system performance degradation, effective triage—the process of prioritizing and diagnosing issues quickly—is crucial to minimize downtime and maintain user trust. As organizations move toward automation and AI, generative agents are becoming powerful tools in…
-
Building generative assistants for OKR retrospectives
In the fast-evolving landscape of organizational productivity tools, generative AI is unlocking new dimensions of efficiency, creativity, and insight. One such frontier is the integration of generative assistants into the process of OKR (Objectives and Key Results) retrospectives. By automating and enhancing reflection, evaluation, and planning, generative assistants can radically transform how teams conduct and…
-
Building generative models for budget transparency
Budget transparency is a cornerstone of good governance, enabling citizens to understand how public funds are raised, allocated, and spent. In recent years, the emergence of generative models—particularly those leveraging artificial intelligence (AI) and machine learning (ML)—has opened new pathways to promote transparency, accountability, and civic engagement in public financial management. These models can automate…