I Had 5 AI Features That Worked Individually. The Product Still Felt Broken

I had five AI features that all functioned independently. Tattoo generation, image-to-tattoo lettering refinement, try-on – none of them were broken. Yet the product as a whole felt incomplete. That realization came as a surprise. I had devoted much of my time to perfecting each individual feature: Can the generator create a useful concept?

Can image-to-tattoo convert an uploaded image? Can lettering preserve exact text? Can refinement alter the output without starting over? Can try-on place the design on a body photo? Individually, the answer was mostly affirmative. But the product's overall coherence remained elusive. The issue lay not in the features themselves, but in what transpired between them.

While each feature could perform its designated task, the transition from one to the next often left users uncertain. They might generate something they liked, but the subsequent step wasn't always clear. Users could modify one part of the design, experiment with different placements, compare the same idea at varying scales, transition from lettering to a more comprehensive composition, revisit earlier creations, or even place the result onto a body photo.

Each feature could execute its own function, yet users still needed to mentally piece together the product's workflow. I had constructed features, not transitions. This realization prompted a shift in focus from asking "what AI feature should I incorporate next?" to contemplating "what does each feature produce, and where should that output lead?"

The useful path emerged gradually: idea → generate → modify → try on → compare → artist handoff. This insight wasn't immediately apparent within the product's design. The pivotal change wasn't adding another model call; rather, it was ensuring that each result could be utilized by the next step. The output became the shared object that traversed the product.

Rather than viewing Generator, Lettering, Image-to-Tattoo, Refinement, and Try-On as isolated tools, I began treating the generated result as a shared object flowing through the product. A generation could serve as input for refinement. A refined result could transition into try-on. A lettering result could be previewed on the body.

A saved result could be reopened instead of recreated. This conceptual shift transformed the product into a cohesive workflow, moving it away from a collection of demonstrations towards a unified user experience. This insight became particularly evident while developing AIMakeTattoo, where the initial concept was relatively straightforward: generate tattoo concepts.

However, as features were added, it became increasingly apparent that the model call was rarely the most challenging aspect. The model work was significant, but most of the problematic product issues resided outside the model: preserving state between tools, determining what should carry over, handling exact text separately from visual instructions, maintaining the same result for subsequent actions, ensuring the next step was apparent, and preventing users from having to start over from scratch.

These were conventional product and software challenges. AI had merely facilitated the concealment of these issues for a time, as each isolated feature appeared impressive on its own. A feature could function correctly, yet the overall workflow could still fail. This experience taught me a crucial lesson: a successful API response does not guarantee that users will successfully complete a task.

A feature could pass every technical test and still create friction, as its output had nowhere useful to go. Before adding another feature moving forward, I now ask myself: What does this feature output? What is the most likely next action? Can the user directly reuse the output? Are we forcing them to re-enter information? Does the new feature connect seamlessly to the existing workflow, or does it merely introduce another branch?

This checklist has been more beneficial than simply inquiring whether another AI capability would look impressive within the product. I am starting to perceive transitions as an integral part of the feature. Previously, I considered the handoff between tools as an implementation detail. Now, I recognize it as a fundamental aspect of the product itself.

The transition from generate → modify or modify → try on can be as crucial as the features themselves. This realization has altered my approach to building AI products. The primary challenge often lies not in getting one more model to function, but in ensuring that the output of one feature can seamlessly continue somewhere useful.

Have you encountered a situation where every feature operated independently, yet the workflow between them presented the real problem?