Складні цифрові продукти — від задуму до робочої системи.

Engineering.Back-end architecture, APIs, and the product systems that have to hold.

Mobile & cloud.Native iOS and Android, and the cloud the apps actually run on.

Intelligence.Models, retrieval, and the data work behind the product.

Growth.Measurement, attribution, and the rails paid traffic runs on.

PHP.The language behind long-lived product backends — shops, APIs, and admin systems that have to hold.

Laravel.The framework we reach for when the product is a Laravel product, not a demo.

Node.js.JavaScript on the server: APIs, workers, and the glue between product and infrastructure.

TypeScript.Typed JavaScript for interfaces and services that have to stay readable as they grow.

React.The front-end we use when the interface is a product, not a brochure.

Redis.Fast memory in front of the database — queues, sessions, and cache under load.

PostgreSQL.The default relational store when the data model has to stay honest.

Swift.Native iOS — the language the platform actually runs.

SwiftUI.Declarative UI on Apple platforms, used where it is the right fit next to UIKit.

UIKit.The mature iOS UI kit still behind screens that have to behave.

Kotlin.Native Android — one language for the app and the logic behind it.

Compose.Jetpack Compose: Android UI written as code, not as a layout file.

Firebase.Auth, push, and mobile backend services when the app should not wait on a custom stack.

Nginx.The front door: reverse proxy, TLS, and the first hop in front of PHP and Node.

Cloudflare.Edge, DNS, and the shield in front of the origin.

AWS.Cloud where the product actually runs — compute, storage, and the boring parts that have to work.

OpenAI.The model API we use when the product needs a capable general model.

Anthropic.Claude, when the task needs a careful, long-context model.

Gemini.Google’s model stack, used where it is the right model for the job.

RAG.Retrieval-augmented generation: the model answers from the client’s own corpus, not from memory.

Embeddings.The numeric form of text we store and search when meaning matters more than keywords.

Pandas.Tables in Python — the working surface for messy product data.

Sklearn.Classical machine learning when a gradient-boosted model is the honest tool.

Agents.Tool-using loops: a model that can call systems, not just finish a sentence.

Pgvector.Vectors next to the relational data, so retrieval does not need a second religion.

GA4.The event stream we treat as product data, not as a marketing afterthought.

GTM.The client-side container — tags that should not live as scripts pasted into a theme.

Meta CAPI.Server-side events to Meta, so attribution does not depend on a browser cookie.

Landings.The pages paid traffic actually hits — built to convert, not to decorate.

TikTok API.Events API for TikTok — the same discipline as CAPI, on a different rail.

Routing.Policy-compliant traffic routing by campaign, geography, language, and device.

Google Ads.Paid search and shopping, wired to the same conversion path as the rest of the stack.

Merchant.Merchant Center feeds: the catalog as the ad system sees it.

GSC.Search Console: how the site is actually indexed, not how a deck says it is.

Binom.The tracker we use when traffic has to be routed, not merely counted.

Keitaro.Another tracking rail — same job as Binom, different clients, same standard.

Creatives.The ads that send the traffic — tested against the same conversion path as the landings.

SGTM.Server-side tagging on the same infrastructure as the product — not a pixel bolted on afterwards.

Python.Ingestion, automation, and the AI stack: one language where data work meets product work.

Docker.The unit we ship: the same container from a laptop to production.