What we do — in detail
Two tracks: AI integration and engineering for highload systems. On most projects they go hand-in-hand — because an AI feature still needs to handle production load, and a highload system today often needs an intelligent assistant or search.
AI integration and LLM systems
We help you embed LLMs in your product so they work in production, don't hallucinate, and don't blow your token budget.
AI integration into your product
We embed LLM functions where they deliver real business value. We start with the cheapest solution: prompt + base model. We only add complexity when evals show that without RAG / agents / fine-tuning, you won't hit your goals.
- ✓Chat assistants and copilotsIn your product, in admin tools, in IDE extensions. With context from your system and tool-use.
- ✓Generation and summarizationProduct descriptions, reports, call summaries, email templates — with quality control and brand tone.
- ✓Smart search and auto-completionSemantic search, query rewriting, intent classification.
- ✓Classification and extractionTicket categorization, entity extraction, email and invoice parsing.
RAG and enterprise search
We turn your documents into a question-answer system with citations and hallucination control. Chunking, hybrid search, re-ranking, fact-checking, eval — we measure every stage.
- ✓Vector infrastructurepgvector, Qdrant, Weaviate — we choose based on load and operational constraints.
- ✓Hybrid searchCombining BM25 and embeddings with fusion strategy for your domain.
- ✓Re-ranking and query rewritingCross-encoder re-ranking, HyDE, multi-query — what actually moves recall.
- ✓Eval and quality controlRagas, golden datasets, faithfulness and context precision as release KPIs.
AI agents and process automation
Multi-step agents that execute actions in your system through tool-use and MCP. Key rule: human-in-the-loop where the cost of error is high.
- ✓Orchestration and graph agentsLangGraph, custom orchestration, state machines — managing complexity without "magic".
- ✓Tool-use and MCPFunction calling, Model Context Protocol, safe integrations with your APIs.
- ✓Sagas and compensationIf an agent step fails — there's rollback, retry, and clear audit trail.
- ✓Human-in-the-loopApproval stages, escalation to support, transparent operator UI.
LLMOps and AI infrastructure
Production infrastructure around models: gateway, caching, observability, ratelimiting, eval, A/B tests, fine-tuning. The layer people forget about until the first bill arrives.
- ✓Model gatewayRouting Claude / GPT / Llama by task type, fallback, A/B tests, unified API.
- ✓Caching and batchingSemantic cache, prompt cache, request batching — typical savings 40–70%.
- ✓Observability and evalLangfuse, OpenTelemetry, traces, golden datasets, regression tests.
- ✓Fine-tuning and self-hostedLoRA, SFT, DPO. vLLM / TGI / Ollama on-prem where data can't leave your network.
Low-code automation and integrations
When you need to quickly wire up several systems — CRM, messengers, document stores, forms — and run AI logic through them, low-code platforms do in days what code would take weeks.
We don't pretend low-code replaces engineering. But in the right place it's the fastest path from idea to a working process — and often the first iteration before rewriting in code.
- ✓Platform choiceZapier, Make, n8n. We pick based on compliance, on-prem needs, operation volume, and budget.
- ✓AI flowsLLM nodes, RAG calls, classification, and summarization right inside Zapier / Make / n8n flows.
- ✓Self-hosted n8nWhen data can't leave your perimeter: we deploy n8n on-prem with auth, audit, and backups.
- ✓Migrating low-code → codeWhen a flow outgrows the platform, we move it into a regular service without losing history or logic.
Architecture and highload engineering
In parallel, we do what the team has been doing since 2013: architecture, performance, migrations, infrastructure, and SRE. For AI services and classical products alike.
Highload architecture
We design systems that handle spikes and grow predictably under load. From scratch or on top of existing code — without "rewrite everything". We don't idealize microservices or pray to the monolith — the solution depends on your team and domain.
- ✓Design from scratchSystem design, stack selection, roadmap from MVP to production-ready.
- ✓Event-driven and CQRSOutbox pattern, saga orchestration, exactly-once semantics on Kafka / NATS.
- ✓Multi-region and failoverActive-active and active-passive schemes, disaster recovery drills in production.
- ✓API designgRPC / REST contracts, versioning, BFF layers, public API for publication.
Performance audit and load testing
We take your service, metrics, and traces — and in 2–4 weeks deliver a report showing: where, at what RPS, and why it breaks. We count specific bottlenecks, not "it's generally slow".
- ✓Service profilingpprof, async-profiler, eBPF tools, flame graphs on hot paths.
- ✓Database analysisEXPLAIN ANALYZE, pg_stat_statements, indexing strategies, lock contention.
- ✓Load scenariosk6, Gatling, JMeter — realistic profiles, not "load everything".
- ✓Capacity planningWhat you get for $X in the cloud, and where money goes to waste.
Migrations and refactoring
We know how to safely decompose monoliths, extract services, and change storage without downtime or "rewrite from scratch". Approach: strangler-fig — each step is measurable and reversible.
- ✓Monolith decompositionDomain-driven decomposition, bounded context extraction, gradual service extraction.
- ✓Online database migrationsEngine migration, sharding, schema changes under load without downtime.
- ✓On-prem ↔ cloudMigration to AWS / GCP, lift-and-shift with subsequent cloud optimization.
- ✓Cloud cost reductionRight-sizing, Spot/preemptible, FinOps approach — typically −30…−50%.
Infrastructure, platform, and SRE
We raise Kubernetes platforms, set up GitOps, observability, and on-call processes. So it works not on paper, but at 3 am. A good platform is one where a new team ships on day one.
- ✓Kubernetes platformMulti-tenant clusters, namespace-as-a-product, sane defaults for teams.
- ✓GitOps and IaCTerraform, Argo CD, Flux. Infrastructure is code that gets reviewed.
- ✓ObservabilityPrometheus, Grafana, OpenTelemetry, Loki/Tempo. Metrics, logs, and traces.
- ✓On-call and postmortemsSLO, error budget, rotations, blameless postmortems, culture of reliability.