CYBIC designs and builds full-cycle AI systems, data platforms and digital products: from business problem and architecture to production and continuous development.
Product boundaries, architecture and the first useful release — before the budget disappears into disconnected work.
One engineering conveyor for any project: data → pipeline → memory → model → orchestration → integrations → an agent in production that keeps learning. Explore the pipeline stages.
входлюбые источники, база знаний
Model-agnostic. Not tied to one brand: Russian models (YandexGPT, GigaChat) or local LLM/ML models are selected for the task. The provider is an architectural choice shaped by security and data-residency needs.
CYBIC is built around one architectural method, proprietary products, accumulated technical systems and direct personal responsibility for the result.
Project architecture, implementation and production delivery remain in the hands of the person you speak with.
Meet the engineerSix engineering disciplines are combined when the result must survive real users, real data and production constraints.
Product discovery → production
I turn a hard-to-formulate business problem into system boundaries, an architecture and a working product instead of a slide deck.
Knowledge, tools, evaluation
An agent gets the right context, permitted actions, memory and a verification loop — not merely a chat interface.
Trust encoded in the protocol
Smart contracts and verification mechanisms make critical events, rights and transactions inspectable without a central black box.
From raw records to decisions
Data collection, cleanup, models and dashboards are designed as one reproducible pipeline, with provenance and monitoring.
APIs, CRM, payments, messengers
Disconnected services become one observable flow: events, retries, access rights and the business result stay visible.
Prompts as product logic
Prompts are specified, versioned and evaluated as part of the product: tone, sources, constraints and fallbacks are explicit.
Products with one engineering culture
SvoyUM shows product architecture and local-first delivery; AvtoUM — production AI SaaS; 4ITX — data and HRTech platform design.
A local-first personal progress system: one event model for tasks, projects, learning, movement, books, habits and finance
Local events form the product core: an action becomes a fact, then an explainable insight. The database stays on-device; migrations and backup preserve data as the product evolves.
AI salesperson: consults the client and closes to a lead — 24/7 on site, VK and Avito
One core connects three channels, AI conversations, a lightweight CRM and live Pulse telemetry. The owner sees incoming requests in real time and can step in when a human is needed.
HRTech platform for Russia’s deep-tech talent market: AI-classified vacancies and market data
Jobs from multiple sources are cleaned and normalized, then AI-classified by direction and grade. Users get filters for skills, work format and salary instead of scattered listings.
The full engineering route is shown here deliberately — after the promise and cases, where detail helps evaluate the approach.
A new product, an AI workflow or a complex integration.
The parts are designed as one system, not transferred between disconnected contractors.
Launch, observe and develop the system against the result the business needs.
From LLM and data to Web3 and production infrastructure.
Send the context and desired outcome. I will identify the architecture, risks and the first useful milestone.
Initial review · reply within a day
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