Cosmosine Labs builds forecasting, optimization and agent systems whose numbers you can check, and the apps and physical products around them. Small studio, long horizons, and a few client projects alongside our own.
cosmo + sine · Labs
We take on a small number of client projects alongside our own, and bring the same care to both. With AI at the centre, here is what that work does for you:
Behind the work is PhD-level research and more than a decade of building AI, forecasting and optimization systems that run in production at global scale, and of leading the teams of scientists, engineers and product managers who deliver them.
Tell us what you're trying to build and where it stands today. We reply within two working days, and after that we're just a call away whenever you need us. No account managers, no hand-offs: you talk directly with whoever is building it.
The name joins cosmos and sine: a universe of AI and mathematics. A galaxy is patient and enormous; a sine wave is small and exact. Adjust the wave below and watch the spiral follow it.
AI, software and physical goods share more than people expect: all three reward careful specification, early prototypes, and someone who cares about the last detail.
Machine-learning, forecasting and optimization systems, and newer work with large language models and agents. Built to run in production, not just in a demo. Some of it becomes a product; most of it makes the products better.
Software for phones and the web, from first sketch through release and everything that follows it, with AI where it genuinely helps. Design, engineering, and support sit with the same people, which keeps decisions honest and changes quick.
Physical goods designed in house and produced with manufacturing partners. The discipline is the same as software: specify carefully, prototype early, and hold the details that are easy to let go of.
We would rather ship one considered thing than five that need explaining.
Most of what we build starts as a question we cannot stop turning over: how something should feel to use, why a familiar object is worse than it needs to be, what becomes possible when a tool anticipates rather than waits.
We keep the loop short. The same people who sketch a thing write it, test it, and answer for it once it is out in the world. That is a constraint on how much we take on, and we treat it as one.
The simplest way to protect people's information is not to collect it. Where we can build without it, we do.
# What ships unless there's a reason not to defaults = { "site_analytics": None, "cookies_on_this_site": None, "sell_personal_data": False, "train_models_on_it": False, } # Client projects client_data = { "used_for": "your project only", "encrypted": True, "access": "need to know", "train_for_others": False, "at_the_end": "returned or deleted", }
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