Cues' initial product was a specialized AI design agent. However, they observed that users were more frequently uploading files to use it as a knowledge base. Recognizing this emergent behavior, they pivoted to a more horizontal product, which was key to their rapid growth and product-market fit.

The decision to move from Arc to Dia was less about Arc's limitations and more about the founders' profound conviction that AI was a fundamental platform shift they had to build for from scratch. The pull of the new technology was a stronger motivator than the push from the existing product's challenges.

Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Customers now expect DaaS vendors to provide "agentic AI" that automates and orchestrates the entire workflow—from data integration to delivering actionable intelligence. The vendor's responsibility has shifted from merely delivering raw data to owning the execution of a business outcome, where swift integration is synonymous with retention.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.

Initially, being the "AI guys" led to endless custom requests across departments. The scalable breakthrough was shifting their model from doing the work to teaching customers how to use their platform to build agents, empowering them to solve their own problems.

YipitData had data on millions of companies but could only afford to process it for a few hundred public tickers due to high manual cleaning costs. AI and LLMs have now made it economically viable to tag and structure this messy, long-tail data at scale, creating massive new product opportunities.

Companies with messy data should focus on generative AI tasks like content creation for immediate value. Predictive AI projects, such as churn forecasting, require extensive data cleaning and expertise, making them slow and complex. Generative tools offer quick efficiency gains with minimal setup, providing a faster path to ROI.