Outsourced development, machine learning and data analytics for business. A team of practicing engineers with commercial experience takes your task end-to-end: price and acceptance criteria fixed before the start, transparent delivery through Git, and a solution brought all the way to a result.
36 projects in a public portfolio · commercial experience since 2019 · contract & NDA
44 services packaged into 8 offers — from one-off scripts to turnkey products and delivery. We take both spot tasks and full ownership. There is a dedicated page for ML model development.
Custom ML models for business: from dataset and experiments to a packaged solution with honest metrics.
Bots for sales, support and internal workflows; AI chatbots and LLM integrations for your products.
Collecting data from websites and APIs, scripts that remove manual routine, and automated reporting.
EDA, reporting and clear dashboards: numbers you can actually make decisions on.
Landing pages, stores, SaaS and customer portals — from pixel-perfect layouts to full products.
Mobile and desktop applications, games, CLI tools, embedded and IoT.
Integrations, payment systems, deployment, CI/CD and performance — a turnkey engineering loop.
Testing, bug fixing, legacy refactoring, security audits and long-term maintenance.
The process is transparent: a brief, fixed acceptance criteria, and work in a repository with intermediate results.
With a short review of the task: stack, constraints, deadlines — and whether you need outsourcing, a corporate training track or a mixed format.
Through a repository, transparent artifacts, clear engineering communication, intermediate checkpoints and control over the result at every stage.
Not just a "final file", but a clear solution structure, a code base, documented logic and a working result for the real task.
Portfolio · commercial & delivery
Commercial, freelance and delivery work with repositories you can open and inspect before we start. The full portfolio of 36 projects, including engineering and research work, is on the Russian version of this page.
Computer vision built to business requirements: dataset preparation, model training with a series of experiments, quality evaluation and packaging into a ready-to-use result.
Financial analytics: data processing, automated calculation of key indicators and clear visualization in a decision-ready format.
Spatial data work in QGIS: layer management, geoprocessing and visualization with a documented, repeatable workflow.
Sensorless electric motor control: algorithm implementation, verified control logic and a working solution ready for use.
Fuzzing binaries without source code: hunting data races, deadlocks, atomicity violations and livelocks in multithreaded Windows applications.
A large language model combined with traffic analysis: applied processing logic, tested scenarios and a reproducible result in the repository.
A neural approach to a classic control problem: MADALINE architecture with analytical rigor, so the model never becomes a black box.
Web development with a hard deadline: frontend, repository, Docker and final build. Projects like this test engineering discipline, not just the stack.
A mobile application MVP: working prototype, clean structure, tidy interface and predictable logic on first launch.
A Java game: gameplay logic, project structure, testing and a stable launch. Even a small game deserves grown-up engineering.
Infrastructure work: configuration, deployment and handover so the project runs somewhere other than the developer's laptop.
Assembled a working project structure, verified launches and brought the materials to a state convenient for further development.
Environment setup and workflow cleanup: organized the repository and documentation into an understandable, runnable state.
When you need an external contractor for analytics, ML, a Python service or applied research. We clarify the task and constraints first, then commit to delivery.
Computational, research and engineering projects for universities and R&D. A separate track with the same standards: deadlines, a clear solution path and a defensible result.
Private repositories, restricted access and data anonymization when needed. Best discussed before the start — and we sign the NDA first.
Anyone can write testimonials on their own website. Freelance platforms don't allow that — a client can only leave a rating after paying for and accepting the completed work. Below are our verified profiles with full project history.
We work with international clients on Upwork — from early-stage startups to product teams needing hands-on expertise in machine learning and data analysis. The platform records hours, deliverables and reviews for every project — a fully verifiable track record, not marketing copy.
We also deliver Data Science and Python automation projects for Russian-speaking clients on Kwork. Same principle: a review can only be posted after the order is paid and accepted — no self-published testimonials, just an open track record.
It depends on scope and complexity; we give a free estimate within 1–2 business days after a short brief. Typical timelines: a scraper, Telegram bot or automation script — from a few days; an ML prototype — from 1–2 weeks; a turnkey service — from a month. Price and acceptance criteria are fixed before work starts.
With a short brief: you describe the task, we clarify the stack, constraints and deadlines, propose a format — one-off task, project or ongoing support — and fix acceptance criteria. Message us on Telegram: @RuslanSenatorov.
Yes. For sensitive projects we work through private repositories, restrict data access and anonymize data when needed. The NDA is signed before any materials are shared.
We work in stages: the scope, deadline and cost of each stage are fixed, and intermediate results are visible in the repository. You see code, artifacts and progress — not just a final file.
Officially and transparently: we sign a contract (and an NDA when needed), and you receive payment documents suitable for accounting. For international clients we agree on a convenient payment method up front.
ML models and data analytics, Telegram bots and AI assistants, scraping and process automation, Python services and APIs, DevOps tasks and support of existing projects.
A team of practicing engineers led by Ruslan Senatorov — a developer with commercial experience in Python, ML and full-stack since 2019. Every solution passes senior code review, and the results are public: 35+ delivered projects with repositories on GitHub are available in the portfolio before you commit.
Describe the task — get a time and cost estimate within 1–2 business days, free and non-binding. We reply within one business day.