How AI gets implemented: the numbers, the limits, the trade-offs.

News and field notes

Design system or separate screens: what pays off across 5 to 20 screens

A design system adds RUB 30,000-60,000 to screen work, or 2.5 to 7.5 screens. We show the volume where it pays back and the cases where separate screens stay cheaper.

How to read a UI/UX design estimate: seven lines and their conditions

A UI/UX design estimate has seven lines, and five of them move the price. Here is what the RUB 60,000 base covers, what a screen beyond five costs, and where the arithmetic stops.

Prompt injection defense: six steps for an LLM agent

A prompt injection fires when untrusted text reaches a point where the agent holds privileges. Six steps cover input isolation, an allowlisted tool set, output filtering, a canary token, least-privilege scopes, and approval; each step has its own check.

AI in manufacturing: start with one process

AI in manufacturing starts with one process where volume is steady, rules are written, and a mistake is cheap. We cover picking the first area, wiring the data, and the limits of the pilot.

What a content pipeline costs: where the bill comes from

A content pipeline bill is five line items: tokens, editor hours, post-production, fact-checking, and publishing. We show where the cost falls with volume and where it holds.

Classification over generation: System One primitives in AI agents

Up to 90% of LLM calls in agents go to generating text where a binary decision would do. Jev cuts up to 80% of the token bill.

The Era of Deterministic RAG: Enterprise AI Infrastructure Without Price Chaos and Hallucinations

Why pushing corporate documents straight into an LLM context window drives up token bills and loses accuracy. We break down the stack: vector layer, semantic cache, tracing.

The Era of Autonomous Agents: Why Chatbots No Longer Pay Off in 2026

Exploring the economic dead end of classic text chat interfaces, hidden token inflation, and the shift to asynchronous agentic micro-pipelines.

AI agents in support: where autonomy ends

A look at what an agent closes on its own, where a human is required, and why response time drops less than the pitch suggests.

Conversational BI: Two-Layer LLM Agent Architecture with a Semantic Layer

How to overcome Text-to-SQL barriers (hallucinations, security risks, and high query costs) using a two-layer architecture of Intent LLM and Smart LLM with a semantic layer.

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