No ready solution exists
The problem cannot be closed by buying a service or by standard development: it needs an algorithm or model the market does not have, or one whose accuracy and licence do not suit you.
Problem → solution
Crack a hard problem
A problem that standard design cannot solve: there is no ready algorithm, the data behaves unpredictably, and the outcome depends on accuracy. We work where the subject is the method itself: we frame a testable hypothesis, research it and take it to a working prototype.
For companies and teams that need a result the market does not offer: their own algorithm, a model built for their task, non-trivial data processing, or a technical hypothesis tested before investing in a product.
What people come with
The data behaves unpredictably
Labelling is inconsistent, formats diverge, the signal drowns in noise — a simple parser or an off-the-shelf model breaks on real data, not on the demo examples.
Unclear whether it can be solved at all
There is a technical hypothesis, but it is unclear whether it will hold and what an attempt will cost. Investing in full-scale development blind is risky.
How we solve it
Frame a testable hypothesis
We turn a vague goal into a hypothesis with a success criterion: what counts as a result and how we will measure it. Without that, research never ends.
Research and test early
A survey of the field, experiments, early testing on your data. We take on the risk of a negative result and report it at once, not after delivery.
Take it to a prototype
From a confirmed hypothesis to a working solution and trials under an agreed methodology. If needed, we take it as far as a service rather than leaving it in a notebook.
Transfer the technology
Source code, documentation, stage-by-stage reporting and team training. The result stays with you — we do not build dependence on us.
What it is built on
Our own models and algorithms
We develop solutions for the task when ready-made ones fall short on accuracy, licence or perimeter requirements. Our research line is backed by open artefacts, not by words on a slide.
Provable correctness
Where an error is unacceptable, we prove the method's properties formally — up to machine-checked proofs of convergence and arithmetic correctness. We do not pass off a claim without proof as fact.
An honest metric
We compare every result against a simple baseline and state its limits of applicability. A negative result is still a result: you get a well-founded answer early and cheap.
What we do for it
R&D · Research · Development
Research and development (R&D) in software and artificial intelligence
Classic R&D in Russia means instruments, microwave engineering and design documentation. We do R&D where the subject of research is an algorithm, a model or an architecture: when a solution does not exist yet and has to be obtained.
Forecasting · ML · Data analytics
Predictive analytics: solutions that calculate rather than guess
A forecast is useful exactly as far as it can be trusted when a decision is made. We build models whose accuracy is measured, whose limits of applicability are known and whose behaviour when the data fails is understood.
What it looked like in practice
Manufacturing · Building materials
MVP in operationPanel specs from CAD drawings in seconds, not hours
Reads DWG/DXF layouts and builds the Excel spec: 10–30 seconds instead of 2–15 hours by hand. All 784 panels of one real project matched the manual spec line by line.
- 784/784 and 189/189
- panels on one project and wall panels on another matched the manual specs line by line, size groups included; area within 0.001 m²
- 2–15 h → 10–30 s
- to produce a project spec: manual work vs app processing, measured on real projects
Marketplace e-commerce · Sales analytics
In productionOne profit per SKU across three marketplaces, reconciled to 0.00%
Five seller accounts on WB, Ozon and Yandex Market merged into one SKU-level P&L: 0.00% variance vs the client's manual benchmark on a control SKU for one reporting period.
- 0.00%
- variance vs the client's manual benchmark across units, revenue, COGS, profit and margin for a control SKU over a month; threshold was 1%
- 1.18M rows
- of sales across 5 accounts and 12 months processed; 10.7% were ordered and settled in different months
Scientific computing / R&D
Research projectFrom mathematical theory to a verified numerical library in 9 months
A compensated-computation library: precision loss is visible and auditable, properties machine-proved in Lean 4, every numeric claim backed by a reproducible benchmark.
- 1.0 instead of 0.0
- residual preserved on the [1e16, 1, −1e16] aggregation that float silently drops; reproducible benchmark artifact in the repository
- ≥10x
- stability ratio vs float64 on catastrophic cancellation; pinned baseline under regression control in CI
Cost the effect on your own numbers
AVA is a process economics calculator. In a couple of minutes it shows whether automating your case is worth it — before you talk to us, with no commitment.
Estimate the impact with AVAFrequently asked questions
Is there such a thing as R&D in software?
Yes. R&D is defined not by the industry but by the nature of the work: the presence of scientific or technical uncertainty that standard design cannot resolve. Developing a new algorithm or model for a task with no ready solution meets that criterion.
What if the result turns out negative?
That is a normal outcome of research, and it has value too: you get a well-founded answer that the path does not lead to the goal, before major investment. We design the stages so that such an answer comes as early and as cheaply as possible.
Do you take research all the way to a working product?
Yes, if needed. We go from hypothesis and prototype to a service you can integrate — our cases include both formally verified cores and research platforms taken into operation.
Other tasks
Adopt AI
You need AI, but it is unclear where to start and whether it will pay off. AI projects more often fail not at the model but earlier — the chosen process cannot be counted in money, or the data for it does not exist. We start with the process and a data check, and decide on the technology last.
Build a software product
You have a product idea, a business model or a mock-up — but no working system, or one that cannot cope with growth. We design and build the product with operation in mind: the minimum scope that tests the hypothesis, on an architecture that will survive the second client.
Automate a business process
Manual work has become the bottleneck: people sort through requests and documents, move data between systems and answer the same questions over and over. Automation does not pay off everywhere — so we start with where exactly the time and money go, and automate what can be counted in money.
Tell us about your task
Describe what needs solving. If it cannot be solved or will not pay off, we will say so straight away, before any work starts.
or email us directly: hello@xteam.pro