Production AI in your product — in days, not quarters.
AI agents, RAG, MCP on a ready stack. Founders — MVP in a week. SMB — production cascade. Agencies — whitelabel MCP development. Direct contact with the developer.
RAG · LLM agents · MCP · multi-provider cascade
What I solve
Six task formats — pick the entry point closest to your situation.
Case: HubMarket
AI-SaaS for marketplace sellers · Founder + sole developer · production
hover any node — description appears
More cases
AI projects (RAG, LLM, agents, OCR pipelines) and heavy production: fintech, ERP, big-data analytics.
OCR pipeline for applications
EdTech client · scan → Yandex OCR → Yandex GPT → structured DB record
AI landing generator
EdTech client · dual-provider text + image generation from a brief
timeweb-mcp-server
Open-source MCP server: manage Timeweb infra from Claude Code, Cursor and other agents
1+ TB big-data analytics
MPSTATS · ClickHouse pipeline · −20% latency, +30% throughput
Lenderkit fintech
Justcoded · team-lead on a p2p-lending platform
ERP for oil & gas
Itpelag · 500+ users · Oracle stack, Docker-based environment
Why this works
A few reasons people pick me — with proof under each one.
From idea to prod — no handoffs
The same person works across all layers — front, back, AI, DevOps.
Production-grade
Multi-provider cascade, queues, observability, rollbacks.
AI stack is my core expertise
7 published MCP servers on npm (including 3 for marketplaces).
I work in your stack
No «let's rewrite to the new shiny thing». I adapt to your backend, frontend, AI provider, and infra.
From idea to prod in 4 steps
A transparent process — no surprises, no account managers.
Discovery
Short call: your task, your data, expectations on timing and metrics. If we don't fit — I'll say so on the spot.
Discovery
Short call: your task, your data, expectations on timing and metrics. If we don't fit — I'll say so on the spot.
Audit
Deep dive into the task, stack and model selection, roadmap, MVP estimate. Output: a document and a concrete next step.
Audit
Deep dive into the task, stack and model selection, roadmap, MVP estimate. Output: a document and a concrete next step.
MVP
Two formats by audience: AI-MVP Sprint for founders (1 scenario from 3 templates); Production AI Integration for SMB (integration into your stack, multi-provider cascade, observability).
MVP
Two formats by audience: AI-MVP Sprint for founders (1 scenario from 3 templates); Production AI Integration for SMB (integration into your stack, multi-provider cascade, observability).
Handover + safety net
Handover doc, source code, access. First month of minimal support — free: bugfixes, monitoring, small refinements. After — by agreement.
Handover + safety net
Handover doc, source code, access. First month of minimal support — free: bugfixes, monitoring, small refinements. After — by agreement.
FAQ
The most common questions before kickoff — short and to the point.