We build software products
where AI is how they work.

It means AI is designed into the product architecture from the beginning, not added after the fact. An AI-first product is built around what AI makes possible: search that understands intent, workflows that automate judgment-heavy steps, assistants that know the product’s context. It’s the difference between a product that has an AI chatbot and a product where AI is doing real work in the core experience. We design for the second thing.

No. We build products that use existing AI providers: OpenAI, Anthropic, Google, and others. We integrate them properly, engineer the product around them, and make them work reliably in production. Building custom ML models from scratch is a different discipline requiring data scientists and ML infrastructure engineers. We don’t claim that capability, and most companies don’t actually need it.

Our typical range is two to six weeks from kickoff to a production-deployed MVP, depending on scope and data readiness. We’ve shipped in this window repeatedly. We’ll give you a specific timeline in the scoping phase, based on what you’re actually building, not a number designed to win the deal.

We work with the right technology for the use case. That includes OpenAI (GPT-4 and newer models), Anthropic Claude, Google Gemini, and open-source models where privacy or cost constraints make them the better fit. For data retrieval, we work with vector databases (Pinecone, pgvector, Weaviate) and RAG architectures. We recommend a stack based on your product’s requirements, not based on what we prefer.

We scope clearly before we build, so you know what you’re getting and what it costs before you commit. For well-defined projects with clear scope, we work toward fixed-price engagements. For more exploratory or evolving products, we work in defined phases with clear milestones. We don’t do open-ended engagements where the budget is a surprise. If we can’t scope it clearly, we’ll tell you why before we start.

You do. Full source code, all documentation, all deployment configurations. From day one. There is no proprietary platform, no lock-in, and no dependency on us to keep your product running. You should be able to hand this code to any other team and have them understand it. That’s how we build.

Both. Adding AI features to an existing product is one of our most common engagements. We come in, understand your current architecture, and integrate AI features cleanly without breaking what’s already working. If you’re building from scratch, we design the AI layer into the architecture from day one. The right approach depends on your situation and we’ll tell you which makes more sense.

We hand over a documented, production-deployed product that your team can own and operate. We don’t disappear — we offer ongoing engagement for iteration, feature expansion, and support. What we don’t do is manage ML model retraining or AI infrastructure, because we don’t build that. If your use case eventually needs it, we’ll help you plan for it and find the right team.

We work with both. Early-stage founders building their first AI-powered product are a significant part of what we do. The combination of product engineering experience and AI integration capability is particularly valuable at the stage where you’re trying to get to market fast with something that actually works. We’ll tell you if your idea needs more definition before we can build it. We’d rather have that conversation early than after we’ve started.

Most AI agencies lead with model capabilities and service lists. We lead with product engineering. The difference shows up in the details: whether the AI feature actually works with your real data, whether the integration handles failure cases gracefully, whether your team can maintain the codebase after we leave. We’re not an AI research firm. We’re a product engineering team that knows how to build AI in as a first-class feature. That’s a narrower claim and it’s a more honest one.