Working in the Reliability Gap

Reliability is not only about how a model behaves on its own. It is about where that behavior shows up in the product and what it is trusted to influence. When teams lack clear patterns, they often fall back on the same shape. Sometimes a chat is the right choice, but it has become the default pattern instead of one option among many.

We treat the AI layer as a first class part of the product. Alongside the frontend, backend, data, and services layers, it is the part where intelligent behavior lives. It may interact with any of these, but it still needs its own boundaries and its own design.

Our work in Product is about deciding where intelligent behavior belongs, what it should do, how it should appear in the interface, and how reliable it needs to be in each place. It is both a science and an art. We start from the impact you want, then shape an approach that fits. When teams rely on generic patterns, they get generic results. When they use the wrong kind of solution in the wrong spot, the system can become unstable where it needs to be steady.

Our work is for teams who are already building or extending products and want the intelligent parts to feel native in the interface, stable in the workflow, and honest about their limits. Product managers, engineers, designers, and founders come together around the same goal making sure the AI layer does the right work, in the right places, under the right expectations.

Three ideas guide our approach and help teams make clearer decisions.

1. The AI layer belongs where its uncertainty matches the stakes of the decision and where its behavior can be observed and adjusted.

2. Reliability is something we design into the product from the start, not something we patch on after launch.

3. Some ideas need a different shape to work reliably with the tools we have today. When we adjust the idea to match what is technically stable, teams usually reach meaningful impact faster.

In practice, we work with product designers early to shape how intelligent behavior appears in the interface, and we stay involved through engineering implementation so the AI layer is built and monitored in ways that match its intended role. This means mapping where AI can create real value, shaping its role so it fits both the product and the interaction flow, and de-risking feasibility so teams do not spend months pursuing a direction that will not hold up. It also means planning for monitoring from the beginning so that changes in behavior are noticed early rather than discovered through support tickets or user complaints.

The impact of this work is straightforward. Products ship with fewer surprises. Teams invest in features that can stand up to real use. Users get the experience they deserve. Reliability becomes part of how the product earns loyalty, not just how it avoids failure.