Aleksandr Tikhonov.
I design production systems around LLMs: RAG services, agent orchestration, semantic search, and self-hosted inference — under strict latency and cost constraints.
Reference architecture
FIG. 1 · GENERAL VIEWA diagram beats a bullet list. This exact scheme runs end-to-end in my self-hosted platform Protocore.
Protocore.
A protocol-first runtime for stateful, tool-using LLM agents. The core — "the agent loop, and nothing else" — is open under MPL-2.0 and installs via pip; the platform on top of it (enterprise backend, sandbox, chat, dashboard) is proprietary and runs entirely on my own infrastructure. Flagship project — founder / solo.
- Open core · MPL-2.0 · pip install protocore
- 20 Protocol interfaces · zero upward imports
- Snapshot / resume of runs · context compaction
- 11,000+ tests · reproducible research
- Enterprise layer: RBAC · gVisor sandbox · SSE
Systems index
8 entries · 2024—2026PROD
LanGPT
RAG/LLM assistant for academic tasks on the EBS Lan (Russian academic e-library platform). Public API with JWT and SSE, RAG over Elasticsearch, LLM backend balancer, moderation.
37,000+ requests
6,500 users
PROD
AI-Snippets
Snippet answers over EBS Lan search results: cache, BM25+RRF, quality gate, circuit breaker, graceful degradation. Stress test: 13.2 hours without a single infrastructure error.
median 4.6 s / SLA 5 s
PROD
Search 2.0
EBS Lan search platform: hybrid BM25 and vector search, subject-area classifier, spell checker, video search. All ML components are CPU-only.
ML: CPU-only
ACTIVE
Daedalus
A personal self-developing agent on the Protocore core: it lives in Telegram and in its own web app, runs inside a Linux container with real tools, and changes its own code through PRs approved from the chat.
supervisor · snapshots · weeks of uptime
MIT · 320+ tests
R&D
swiftclf-tuna
Bilingual (RU/EN) hierarchical intent classification for routing AI assistants. AI-orchestrated research: selective prediction, calibration, CPU-first deployment.
p95 77 ms / 1 CPU
OSS
AI Audit Kit
A portable .audit standard: turns AI code audit, triage, and bulk fixes into a reproducible pipeline with artifacts instead of chat sessions.
open source
OSS
Agent Tooling
Tools for autonomous agents: notify-telegram-cli — dependency-free notifications, kb-genesis — knowledge bases for agent environments, ai-notes, LightUniLLM — a lightweight LLM framework.
github.com/ascorblack
Profile
Since January 2024 — AI Systems Engineer at EBS Lan (Russian academic e-library platform): I own the backend and infrastructure side of the company's key AI services, from architecture to production support in Kubernetes.
In parallel — founder of ascorblack-labs: Protocore, a protocol-first runtime for LLM agents. The core is open under MPL-2.0; the platform is proprietary and runs entirely on my own infrastructure (GitLab, Harbor, k3s).
My strength is engineering production systems around LLMs rather than generic model training: reliability, observability, and degradation that never surfaces as an error.
Specification
- LLM / RAG / agent orchestrationcore
- Protocol-first agent runtime · context compactioncore
- Python · FastAPI · asynciocore
- Elasticsearch · BM25 + kNN + RRFcore
- vLLM · local inferencecore
- Kubernetes · Helm · gVisorops
- RabbitMQ · Redis · PostgreSQLdata
- Prometheus · Grafana · OTLPobs
- Next.js · React — platform frontendsui