An engineering studio for hard problems
We solve hard problems — AI, mathematics, engineering.
Bring the problem, not a finished spec. We scope it and its economics, pick the best fit — AI, an algorithm, an ERP or a mix — and take it to production. We do more than AI, and we say plainly where it pays off and where it does not.
You describe the problem in words. We answer with what we have already built.
Below are real tasks people came to us with. Each one is backed by a case with artefacts: the industry, the task and what we could measure.
Five kinds of task. One principle: problem first, technology second.
When a ready-made tool cannot solve it, we go deeper.
Automation takes an existing tool and wires it in. Some tasks resist that — they need a bespoke algorithm, an honest numerical base or genuine research. Our moat is a technology base we build, not just other people’s APIs called for you. And we prove it, not claim it.
Our own numerical base
Balansis — a library that stays stable where ordinary arithmetic breaks. Not "we apply AI" but "we build the foundation under it".
73 theorems formally proved, 0 unproven assumptions (Lean4).
An honest metric, not "seems to work"
Every model is compared against a naive baseline. If it does not beat it, it is not needed. A negative result is reported, not hidden.
The same principle runs on our service pages, not just the showcase.
Reproducibility as an engineering norm
Our research systems are deterministic: same input, same result, byte for byte. That is what separates engineering from a demo.
A replay reproduces the exact run hash (Agent Native Universe).
From paper to service
We walk the whole path — framing, research, prototype, production. Our own models and libraries, not a retelling of someone else’s.
From mathematical theory to a verifiable library.
What we built. And what we could measure.
Contracted pilots, systems in operation and prototypes tested on real data. No client names — the industry, the task and figures backed by project artefacts.
We show how systems behave, not what we promise.
Behind the words is something you can open and check: reproducible runs, formal proofs and open source where possible.
We do not talk about AI — we live in it
Behind the consulting are our own products and libraries. Some run in production with real users, some are open source, some are still research. Clients get engineers who fix their own systems daily, not a slide about experience.
StudyNinja
in productionLearning platform
The product where our technology lives in production
A learning platform for school students: a subject knowledge graph, gap diagnostics and next-step selection. This is where our research meets real users instead of demos.
studyninja.ruKnowledgeBase AI
in productionKnowledge platform
Knowledge graphs and semantic retrieval as a separate layer
An engine that turns documents and a domain into a connected model: entities, relations, semantic search and provenance. It lives apart from any single product and is reused across projects.
Agent Native Universe
open sourceAgent runtime, open source
An environment where agents negotiate, pay and reach agreement
A runtime for self-organizing agents: relationship negotiation, a resource economy, byzantine agreement between nodes and recursive organization into groups. MIT-licensed — download it and check.
research programmeMagicBrain
researchNeural core
Our own research line in brain-like networks
A research core: spiking networks, local learning, grown rather than hand-designed structure. Parts of it are in patent preparation, so publicly we speak of purpose and observable properties.
Balansis
researchNumerical foundation
Arithmetic that holds where ordinary arithmetic breaks
A computation library designed so that division by zero, overflow and precision loss do not take the pipeline down. A foundation under the other layers; the mechanism is in patent preparation.
UWT
researchResearch track
Theory taken all the way to publication
A separate research line with an experiment registry, tests and a prepared preprint package. It sets the bar: a claim that cannot be checked does not ship.
uwt.xteam.proA process with no wasted motion.
- 01
Discover
Deep immersion into your domain, constraints and data. We ask until the problem is sharp.
- 02
Architect
Systems design first — models, interfaces, failure modes. The blueprint before the build.
- 03
Build
Small senior team, fast iterations, working software every week. No theatre.
- 04
Verify
Evaluation harnesses, adversarial testing, load and safety checks — before your users do.
- 05
Evolve
Ship, observe, improve. Systems that learn — and a team that stays accountable.
The unfair advantage is the team.
Elite by default
A small team of people who would be the strongest engineer in most rooms.
Research-grade rigor
Claims are tested, numbers are measured, and “it seems to work” is not a status.
Speed with safety
We move fast because the guardrails are engineered, not improvised.
Human + AI, natively
Agentic workflows multiply every engineer. You get the output of a department.
We partner with founders, labs and enterprises across education, research and deep technology.
Client list under NDA — references available on request.
They think like researchers and ship like a product team. That combination is almost impossible to hire.
XTeam gave us the output of an entire data science department — and raised the bar for the rest of us.
Let’s engineer what’s next.
Tell us about the system you wish existed. We’ll tell you how to build it.
Not ready to write? Work it out yourself: what the process costs you now and what budget would pay back. No form, right here on the site.
Estimate the impact with AVA