Our Vision

Removing Friction Starts Upstream

Complex interactions with customers often involve the transfer of information from the customer to the company. These interactions may include providing specifications for a custom service or product, submitting application forms for jobs, mortgages, or services, or outlining requirements for consulting engagements. Today, the process typically forces the client to adapt to the service provider's internal data structures. Clients are often required to fill out lengthy forms and provide extensive supporting documents, creating unnecessary friction.

What if we could flip this model? Instead of relying on rigid forms, we could extract the necessary data directly from the customer’s source documents. This approach allows communication to be centered on the customer’s own documents. By referencing and processing the customer’s materials, we can highlight inconsistencies, identify gaps, or request additional information in a format and style familiar to the customer. By not exposing our internal data structures and processes to our clients, we reduce friction and make engagement simpler and more attractive.

Additionally, by operating on the original source documents rather than a reduced set of fields from forms, we preserve the richness and nuance of the customer’s information. This ensures that critical context—such as a candidate’s full resume, detailed work history, specific requirements, or unique terminology—is retained. This comprehensive approach provides deeper insights and better outcomes.

With the advancements in Large Language Models (LLMs), particularly as they improve in understanding the full scope of documents—including diagrams, pictures, and tables—we can increasingly work directly with source information. This shift reduces customer friction, improves internal decision-making, and enhances business operations. By rethinking how we interact with our customers and their data, we can create more seamless and productive experiences for all stakeholders.

This approach does not seek to immediately replace traditional Systems of Record (SORs) or existing platforms (such as CRM, ERP, HRM, etc.). Instead, it complements these systems by providing AI-based analysis in parallel. Moreover, it does not aim to supplant human judgment; on the contrary, it empowers subject matter experts to become more efficient and nuanced. By leveraging AI-driven insights, it pinpoints critical issues and highlights areas that deserve the expert’s focused attention, fostering a more informed and effective decision-making process.

In summary, it’s a win-win situation. Customers enjoy a significantly improved experience with reduced friction, while companies benefit from faster processing and more informed, nuanced decision-making.

Beyond Enterprise Silos with AI

Generative AI is at its infancy and despite the recent hype, the impact so far on corporate business has been limited. Beyond the minor challenges - like robustness and hallucinations - the major blocker in corporate environments stems from massive data silos that operate in isolation (CRM, ERP, HR, emails, document systems, etc).

Despite decades of effort to integrate these systems - recall ETL, SOA, ESB, BPM, RPA and microservices - progress has been slow due to quickly increasing complexity and the resulting maintenance difficulties. As a consequence, humans still carry most of the integration burden, and manually perform most of the tasks that could be automated.

The next wave of AI brings Agentic AI systems that will transform corporate automation. These autonomous agents will transcend silos, map APIs and interact with disparate systems. They will be able to collect, process, and act on data continuously with a comparatively minor integration and maintenance efforts. AI will streamline workflows, optimize business decisions, and accelerate automation across industries.

The journey towards this end state will - as always - be a series of steps. High-impact use cases will come first, where LLMs and sequential agents will reach into silos, collect and transform data into knowledge, in support of specific business needs.

Cube5 AI brings the necessary expertise to understand and operationalize these first steps of adoption. But we also know that these early (Generative AI) applications are just the start of the journey towards Agent-based automation that acts across the enterprise.

For more discussion of agents and enterprise AI, see this blog post.

Our Vision

Removing Friction Starts Upstream

Beyond Enterprise Silos with AI

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