Early in my career, I sat through a lot of meetings where people sold software they had never run. The decks were always excellent. Clean diagrams, arrows pointing the right way, and a presenter who had quite obviously never been woken at two in the morning because a job queue had jammed and a customer's invoices were stuck somewhere between two systems. I learned a great deal in those rooms. Mostly, I learned what I did not want to become.
That is the whole reason AvanSaber works the way it does, and the reason this site exists at all. We build and run the AI we recommend. We do not advise from a safe distance. We ship our own products, operate them in production, and let the bruises do the teaching. It sounds like a strategy when I put it on a slide. Most days it feels more like a refusal to bluff.
The advice nobody has to live with
I spent the first stretch of my career in enterprise ERP, a world where the gap between advice and consequence is enormous. A consultant recommends an architecture, signs off, and moves to the next account. Two years later, someone in a billing department is quietly building spreadsheets to paper over the cracks in a system that looked flawless in the proposal. I am not being cynical about consulting. A lot of that advice was genuinely good. The problem is structural. When you never have to operate the thing you designed, you slowly stop noticing the difference between an idea that is correct and an idea that is true.
Correct is the architecture diagram. True is the 2am page. They are not the same thing, and only one of them improves your judgment.
So we run our own stack
Today we ship across an uncomfortably wide range. ERPClaw puts a conversational AI layer over the kind of ERP I used to implement by hand. TailTest tests software the way an impatient senior engineer would, if the senior engineer never got tired. There is SiteKit, a server tool I put together in about a week because I was tired of paying a subscription for something I could run myself. Inventory, SEO, social, payments. Frankly, more than a sensible person would attempt with the team we have.
Every one of those products pages someone on our team when it breaks, and often that someone is me. This is the point, not an accident of being short-staffed. When the assistant inside ERPClaw gives a confidently wrong answer to a real customer, I do not read about it later in a satisfaction survey. I feel it that evening. That feeling is the most valuable input we have, and there is no way to buy it, outsource it, or fake it in a demo.
A portfolio is a forcing function
People ask why we run so many products with a team of fifteen. The honest answer is that breadth keeps us honest. My co-founder Varun and I split the company cleanly. He runs the business, I run the building, and between us we keep a standing argument going about how much is too much. He is usually right. But a portfolio does something a single product cannot. It forces you to build things that are genuinely reusable, because you simply do not have the people to solve the same problem twice.
I trust a piece of software the way I trust a restaurant. Only if the people who made it actually eat there.
So one deployment path runs under all of it. One design system. One testing harness, which happens to be a product we also sell. When I say that I ask what is the why before starting anything new, this is what it means in practice. With fifteen people and eight products, an unjustified project is not a small mistake. It is a month that nobody on the team gets back.
What a customer actually gets from this
A vendor who runs their own software is a different kind of vendor. We have usually already hit the failure you are about to hit. We have already argued about the awkward edge case at a whiteboard. So when we tell you an AI feature is ready, we are really telling you it survived contact with our own production, our own customers, and our own irritation. That is a narrower promise than most AI companies are making right now. It is also a more honest one.
It keeps our enthusiasm in check too, which matters more than it sounds. It is easy to love a model in a keynote. It is much harder to stay in love with it after you have had to support it for six months. The few technologies I am still genuinely excited about are the ones that survived the second feeling, not just the first.
The limit I keep running into
Running what you build is clarifying, but it is not a magic trick. It does not tell you which product to kill, only that the killing is overdue. It does not make the hard call about a feature customers love and we cannot maintain. And it carries a real cost, which is focus. When you operate everything, everything has a claim on your attention, and attention is the one resource a small team cannot manufacture. I am strict about defending the hours that matter precisely because the build-and-run instinct quietly tries to eat all of them.
So we draw lines. Not everything deserves to be built, and not everything we built deserves to keep running. The discipline is not in shipping more. It is in being able to look at something you are proud of and admit that it has had its time.
None of this makes us right about where AI is going. It only means that when we are wrong, we find out first, and we find out for real, on a Tuesday, with a customer on the other end. For a company selling AI in 2026, that might be the only credential worth having.