A small team of AI engineers and highload architects
We've been doing architecture and highload work since 2013; since 2023 we've systematically led AI integration projects. We're not 'a hype-driven AI startup' and not 'integrators who just heard about LLMs' — these are two full-scale tracks of one engineering team.
How we work
These aren't corporate mantras — they're the filter we run every project and decision through.
- Technology is a tool, not the goal LLM, microservices, Kubernetes — each solution must justify its complexity. If SQL or a monolith solves it — we say so.
- We measure quality, not vibes Golden datasets, eval-pipeline, faithfulness, context precision, A/B on users. Without this, "improvements" are taste.
- Cost is a product metric We count token cost, cache-hit rate, cost-per-request from day one. AI projects fail more often from cloud bills than quality.
- Production is serious SLO, on-call, postmortems, observability, ratelimit, fallback. Shipping an AI feature is operation under load, not "demoed once".
- Security from sprint one PII filters, prompt-injection protection, audit trail. Not "add later" — it's baked into architecture from the start.
- Your team is our team Pair design, review, knowledge transfer. After we leave, your team owns prompts, models, and orchestration.
Team
Each project gets a dedicated architect and 2–4 engineers depending on scope. No long approval chains or managers between you and the engineers.
Slava Konashkov
12+ years in highload architecture: fintech, AdTech, SaaS, e-commerce. Recent years leading AI integration track — RAG, agents, LLM infrastructure. Reports on architecture decisions and works with CTOs.
AI team
RAG, agents, prompt engineering, fine-tuning, eval-pipelines. Know the difference between cross-encoder and bi-encoder, when LoRA beats full-tuning. And why neither is usually needed.
Backend team
Go, Node.js, Python. Build APIs around models, gateway layers, queues, integrations with internal systems. All have product-team and on-call experience.
Platform team
Kubernetes, vLLM, Terraform, observability, FinOps. Raise inference clusters and LLM gateways where product teams ship AI features daily, not "when GPU frees up".
Data engineers
Postgres / pgvector, ClickHouse, Qdrant, Weaviate, Kafka, Whisper-pipeline. Ship vector infrastructure and unstructured data pipelines to production-ready.
Eval engineers
Golden datasets, Ragas, Langfuse, A/B tests, regression tests on prompts and models. We don't ship and pray. We measure first.
Tell us about your project
An AI feature, a highload optimization, or architecture from scratch — describe where you are and where you want to be. We'll reply within a business day.