Most development agencies hand over a codebase and move on. They never see the support ticket at 11pm, the customer who churns because onboarding took three weeks, or the AI feature whose token bill quietly triples once real users find it.
We do. Alongside client work, we build and run My Chamber Buddy, My Member Buddy, and Orana Stay. Real products, real customers, real invoices, real 2am incidents. That experience changes what we build for other people, and it is worth being specific about how.
We build for the second year, not the demo
Anyone can ship something that looks good in a demo. The interesting question is what the product costs to run once it has customers.
Operating our own software means we feel every shortcut. Skip proper audit logging and you will spend a support call reconstructing what a user did from database timestamps. Skip a decent admin tool and every customer request becomes a developer task. Skip a data export path and your first enterprise customer will ask for one in week two.
So when we scope a build, we push for the unglamorous things early: admin views, logging, a way to fix bad data without a migration script, a way to answer “what happened to this record” in under a minute. Founders sometimes push back because none of it is on the roadmap slide. Every one of them thanks us in month eight.
We know what AI costs to operate, not just to build
The gap between an AI prototype and an AI feature in production is mostly operational, and it is bigger than most people expect.
A prototype calls the model once, on clean input, with a developer watching. A production feature has to handle a user who pastes in 4,000 rows, a model that returns malformed output on the hundredth call, a provider that has an outage on a Tuesday, and a cost line that scales with usage rather than with revenue. It needs caching, fallbacks, guardrails on what the model is allowed to touch, and a way for a human to see why it said what it said.
We have shipped AI features into products where the customer is a chamber director or a hotel operator, not a technologist. Those users have no patience for a confidently wrong answer. That has taught us to be conservative about where we put AI: use it where the cost of being occasionally wrong is low and the human stays in the loop, and be extremely careful where it touches money, contracts, or someone’s data.
We are not going to claim we have AI fully figured out. Nobody has. But we are building with it every week in products people pay for, and that is a different kind of knowledge than reading the papers.
We will tell you when not to build
Because we have operated products, we know what a feature costs over its lifetime, not just to write. That makes us reasonably good at spotting the ones that will not pay for themselves.
Sometimes the honest answer is that an off-the-shelf tool covers ninety percent of what you need and you should use it. Sometimes it is that the feature you are excited about is a two-year support burden for one customer. Sometimes it is that you should ship a smaller version and see if anyone uses it before you build the good one.
A partner whose only revenue is billable hours has no incentive to say any of that. We would rather build the right thing and keep working with you for years.
We ship small and often, because that is how our own products work
Our own products go out in small increments, several times a week. Not because it is fashionable, but because it is the only way to find out whether something works before you have built too much of it.
We run client builds the same way. You see working software early, you use it, and you tell us what is wrong while it is still cheap to change. The alternative, a six month build with a big reveal at the end, is how you end up with a product that matches the spec and misses the market.
The short version
If you are looking for a partner to build a product, ask them what they run. Not what they have built and handed over, but what they operate today, with customers who complain when it breaks. The answer tells you whether they will build you something that survives contact with real users.
If you have a product idea and want a team that builds and runs software for a living, talk to us about what you are trying to build.