We embed LLMs, RAG systems, and AI agents into real products — and design the infrastructure underneath that holds production load. From AI copilots and search engines to payment platforms and RTB bidders.
Many teams can either "bolt on an LLM" or "handle load." We do both — because in production they're really the same problem.
Claude, GPT, Llama, vLLM, local models. RAG, fine-tuning, agents, eval-pipelines. Not from articles — from a dozen production projects.
Fintech at 35K RPS, RTB bidders at 250K QPS, search across 12M products. Microservices, event-driven, multi-region failover — this is the team's engineering foundation.
Faithfulness, latency p99, cost-per-request, conversion, error budget. Every architectural decision is tied to a measurable metric.
Cloud spend, tokens, GPU, operations. We cut costs 30–70 % — and show upfront what a solution will cost in a year.
SLO, on-call, postmortems, observability, ratelimit, fallback. Not "shipped a demo" — we bring it through to operation under load.
If the task is solved by SQL, we skip the LLM. If a monolith fits, we don't split it into microservices. Complexity is expensive, and we avoid unnecessary complexity.
We help you embed LLMs in your product so they work in production, don't hallucinate, and don't blow your token budget.
LLM features in your product: chat assistants, copilots, generation, classification, smart search.
Documents into a question-answer system with citations, hybrid search, re-ranking, fact-checking.
Multi-step agents with tool-use and MCP. Process automation, support assistants, DevOps agents.
We wire up CRM, messengers, document bases, and AI nodes into ready-to-run flows. The fastest path from idea to a working process.
Model gateway, caching, ratelimiting, observability, eval-pipeline, cost attribution across teams.
In parallel, we do what we've done for 12+ years: architecture, performance, migrations, infrastructure, and SRE.
Design from scratch and evolution: event-driven, CQRS, multi-region, stack selection for growth.
Profiling, load tests, capacity planning. What and where will break at 10× load.
Strangler-fig migrations, monolith decomposition, online database migrations, cloud transitions.
Kubernetes platforms, GitOps, observability, on-call processes, FinOps. SLO as a promise, not a slogan.
This isn't "everything we've heard of" — it's what we've personally deployed in AI and highload systems under load and kept on-call.
We don't write "helped a client" without numbers. Each project has a measurable metric: faithfulness, latency, cost, conversion, or uptime.
Hybrid search + cross-encoder re-ranker + fact-checker on Claude. Case review time dropped from 40 to 6 minutes, faithfulness 0.94 on golden set.
Routing Claude / GPT / Llama, semantic cache, ratelimit, cost attribution. Token cost down 64%, overhead 42 ms p99.
Migrated payment platform to Kubernetes with Kafka backbone and sagas. Reduced payment API p99 7x and sustained 6x transaction growth.
No lengthy pre-sale "conversations." By the second meeting — we estimate, deliver a meaningful prototype or roadmap, and give you real numbers.
We understand the problem, metrics, and constraints. We decide what you actually need: AI, architecture redesign, or just a good database index.
For AI — MVP with golden dataset and metrics. For highload — design doc, ADR, roadmap. Costs and risks are visible.
Implementation alongside your team. Pair design, reviews, releases under SLO, A/B tests, on-call.
Documentation, runbooks, eval-pipeline, cost forecast. Your team owns it and confidently changes prompts, models, or services.