Everyone's adding "AI" to their product right now, and a lot of it is the same thing wearing a different logo: a chat widget bolted onto the corner of the screen, answering questions worse than the search bar it replaced. Users can tell. It doesn't build trust, it spends it.
We build AI features that are actually part of the product, not a widget stapled on top. That means working out what the feature needs to be right about, what it's allowed to get wrong, and what happens to your bill the day a user finds a way to loop it — before any of it ships.
What usually goes wrong
Most of this isn't visible until real users are on it. By then it's an incident, not a code review comment.
No cost ceiling
One user finds a way to trigger a loop, or just uses the feature heavily, and the AI bill for the month is bigger than expected — with nothing in place to have caught it earlier.
It answers confidently and wrong
Without grounding in your actual data, a model produces a fluent, convincing, incorrect answer — which does more damage to trust than not having the feature at all.
It only works in the happy-path demo
Ask it something slightly outside the script and it breaks, loops, or returns nonsense, because the failure modes were never designed for — only the impressive path was.
No human fallback
When the AI genuinely can't handle a request, there's no clean handoff. The user just hits a wall.
Latency nobody accounted for
A feature that takes eight seconds to respond needs to be designed to feel like it's working during that time, not just a spinner and hope.
No evaluation before shipping changes
The prompt or model gets tweaked and shipped straight to production, with no way of knowing whether it just made real answers worse.
How we build it
Scope the feature
What does it need to be right about, what's it allowed to get wrong, and what does a bad answer actually cost you? That shapes the whole build.
Design the guardrails
Rate limits, cost caps, grounding in your real data, and a defined fallback for when the model genuinely doesn't know — decided before a line of code, not bolted on after an incident.
Build and evaluate
We test against real cases, not just the demo script, and set up a way to check whether a future change made things better or worse.
Ship and monitor
Usage, cost and failure rates visible after launch, so a problem shows up on a dashboard, not in a customer complaint.
What we don't do
We won't add an AI feature just because it's fundable or fashionable. If a plain search bar or a simple form does the job better and cheaper, we'll tell you that.
We also won't ship something with no cost ceiling. An AI feature with an open-ended bill attached isn't a feature — it's a liability with a nice interface.