Low Down on Low-Code

This episode explores the evolving landscape of software pricing, the impact of AI and platform models, and how CIOs can navigate this complex environment. Experts Francis Carden and Andy Bartels share insights on future trends, pricing models, and strategic considerations.
Key Topics
The shift from perpetual licenses to SaaS and platform models
The challenges and opportunities of token-based and usage-based pricing
The impact of AI and large language models on software costs
The importance of economic analysis and risk management in software investments
The role of platform vendors and models in reducing technical debt

In this episode, John Rymer and Rob Koplowitz sit down with Dave Marcus to discuss his hands-on evaluation of the fast-moving world of vibe coding, low-code, and process automation platforms.
Dave explains why he chose a CRM-style application as his test case: it had a realistic data model, a third-party integration with Microsoft SharePoint, and a workflow component that would reveal how these tools handle real business complexity rather than simplistic demo apps.
A major theme of the conversation is the difference between prompt-driven development and visual tooling. Dave found that while prompting could generate surprisingly functional applications, the experience often became frustrating when he needed to adjust layout, test changes, or understand dependencies. Visual tooling, when available, often helped—but some platforms treated it as secondary or immature compared to prompting.
The discussion also digs into a critical enterprise concern: maintainability. John and Rob push Dave on what happens when applications need to evolve over time due to changing business, regulatory, security, and workflow requirements. Dave emphasizes that model-based systems still matter because they make dependencies, workflows, and governance visible in ways code-heavy systems often do not.
Another key takeaway is workflow orchestration. Dave argues that workflow is not just a diagram—it is part of the application’s operational logic and must be understandable to domain experts, not just developers. He notes that some pure vibe coding tools bury workflow in code, while some process-centric platforms add complexity that may be more than needed for simpler applications.
In the end, Dave’s conclusion is nuanced: there is no single winner. Different platforms are better suited to different use cases, and the market is evolving too quickly for simplistic “replace everything” narratives to hold up.
Key takeaways
A realistic evaluation needs a real application, not a toy demo.
Prompting can produce functional apps, but visual refinement and testing remain important.
Workflow and dependency visibility are essential for enterprise maintainability.
No single platform category is best for every scenario.
The market is evolving quickly, so these tools must be reassessed continuously.

Join us as we delve into the evolving landscape of low-code platforms, AI integration, and citizen development, featuring insights from Jeff Kuo of Ragic. Discover how organizations globally are transforming their workflows, overcoming challenges with AI-driven tools, and embedding citizen developers into their operational fabric.
Key Topics:
Jeff Kuo’s journey from academic research to leading Ragic's platform development.
The concept of citizen development and its practical applications in manufacturing and hospitality.
How low-code platforms like Ragic support complex enterprise needs without traditional coding.
The impact of AI on democratizing application creation and reducing learning curves.
Strategies for managing risk, privacy, and governance in AI-assisted development environments.
Cultural differences in application building, especially between Western and Asian markets.
The future role of AI in enabling non-technical users to design and deploy systems seamlessly.

In this episode, James Duez, CEO of Rainbird, dives into the intricacies of how knowledge and context are represented within AI systems. We explore how structured knowledge graphs, deterministic reasoning engines, and hybrid architectures can elevate AI decision-making and trustworthiness. Whether you're a product manager, AI developer, or business leader, this conversation clarifies how to architect AI solutions that are explainable, auditable, and reliable.
The distinction between case context (transaction-specific data) and policy context (knowledge and rules) in AI systems.
How Rainbird leverages knowledge graphs and ontologies to formalize and reason over policy and tacit organizational knowledge.
The limitations of large language models (LLMs) in reasoning and the importance of keeping them outside the core decision logic.
The role of inference engines in building deterministic, auditable AI decision layers grounded in policy and knowledge.
The evolution from traditional knowledge management tools like SharePoint to advanced knowledge elicitation and modeling using AI.
The concept of knowledge as a first-class citizen in AI architecture, enabling faster and more reliable decision-making.
The risks of relying solely on LLMs, including hallucinations, model drift, and lack of explainability, and how hybrid architectures mitigate these issues.
The importance of roles like knowledge engineers and business analysts in managing and architecting organizational knowledge layers.
Rainbird's reusable, enterprise-grade platform for building, managing, and reasoning over knowledge graphs, separable from data sources.
The future of hybrid AI architectures: combining symbolic reasoning with probabilistic models to balance flexibility and trust.

In this episode, John Rymer and Rick Greenwald explore the complexities of data copying versus accessing data in place, focusing on performance, freshness, correctness, and consistency. They discuss how these factors impact data management strategies, especially in the context of AI and modern data access challenges.
Key Topics
Data copying vs in-place access
Performance implications of data access
Data freshness and latency
Data correctness and consistency
Distributed data challenges
Data governance and compliance