Custom AI Software Development

Most of our work falls into a few shapes. Internal platforms that replace scattered spreadsheets and aging tools. Customer portals and marketplaces. Web and mobile apps for iOS and Android. Data systems that pull from many sources and produce numbers you can rely on.

We also build the layer that gets less attention: the integrations, sync jobs, and reconciliation logic that decide whether everything else works.

The AI part varies. Sometimes it is the core of the product — a document pipeline, a recommendation engine, a natural-language interface. Sometimes it is one screen. And sometimes a simple rule engine is cheaper, faster, and correct every time. When that is the case, we say so.

Systems that adopt AI well share a few habits, and all of them are much harder to add later.

Keep the history. If a status field is overwritten every time it changes, you have today's value and nothing else. Keep the changes as records and you can answer why an order slipped or how a price moved — questions no model can answer if the data was never kept.

Keep documents as data. An invoice stored as a PDF is a picture of an invoice. Stored with its fields intact, it can be searched, checked, and totaled without anyone reading it again.

Log what the system did and why. Not just that the email went out, but which rule sent it. That record is what lets you explain an automated decision to a customer or an auditor.

Model identity and permissions properly. Then an assistant acting for a user inherits that user's limits automatically, instead of needing a second set of rules that will drift out of step.

Building this in from the start costs very little. Adding it afterwards usually means a migration first.

Not every project starts from zero. A large share of our work is systems that already run the business but can no longer be changed safely — the original team has moved on, the framework is out of support, and every release is an event.

We do this in stages instead of rewriting. First we put tests around the behavior you cannot afford to lose. Then we build the new capability alongside the old one and move traffic across gradually. You get working software early, and there is never a single switch-over the whole business depends on.

We also take over projects that are late, over budget, or left behind by a team that has moved on. That starts with an audit of the code and the architecture, and a straight assessment of what is worth keeping and what is not.

Our CEO and senior engineers run discovery themselves: scope, architecture, and a clear fixed-price or time-and-materials proposal with the assumptions written down. We then assemble the team from a network of 100+ senior engineers across Latvia, Sweden, and the US — people we have worked with for ten to twenty years, not strangers pulled from a recruitment pipeline.

Delivery runs on weekly demos, code in your repositories from day one, production handoff, and post-launch support. Twenty years, 100+ projects, zero abandoned. Because we are a boutique working through a network, you get enterprise-scale capacity without enterprise-scale overhead.