SIVARO
We build. We sell.
A system that survives scale is half the job. The other half is getting it found, indexed, and bought. Same team, same rigor, both sides.
Production engineering
ClickHouse, Kafka, RAG, Kubernetes. Prototypes hardened into systems that hold 200K events/sec at 12ms P99, with runbooks your team can operate.
See the disciplinesWe don't ship features.
We ship measurable results.
Every engagement opens with a baseline audit. We profile P50, P95, and P99 latency, map spend to workloads, and rank your ten most expensive queries. Then we set targets and hit them. You see the numbers before and after, verified.
PostgreSQL dashboards timed out. Now sub-second analytics on 200M events a day.
Node.js gateway rewritten in Go. One instance now handles 18K RPS.
We measure before we fix, infrastructure or distribution. Your scan takes about 40 seconds.
Scan my domainFive disciplines.
One team.
Patterns forged in real incidents: merge storms at 2 AM, consumer lag under sustained load, keyword footprints ceded to competitors. Not certification courses.
Explore all servicesAI Product Engineering
Your Cursor or Replit prototype demoed great. We make it survive real users: auth, caching, schema design, pipelines, monitoring, cost controls.
Data Infrastructure
ClickHouse clusters and Kafka pipelines at millions of events per second. Codec-tuned schemas that cut storage 40–60%, migrations with zero downtime.
Production RAG Systems
Vector search alone isn't a retrieval strategy. Multi-stage retrieval, re-ranking, and guardrails at sub-100ms across millions of documents.
MLOps & AI Infra
Kubernetes-native serving with vLLM and TensorRT. GPU autoscaling on request load, drift detection, per-query cost tracking, automated rollbacks.
GTM Engineering
Distribution as a system: crawl graphs, SERP footprint, indexing pipelines, lead capture. Starts with an evidence-backed audit of your domain.
How an engagement runs.
Infrastructure or distribution, measured at both ends. Every project closes with a documented before-and-after: specific numbers, not anecdotes.
Baseline scan
Distribution scan of your domain, or an architecture and query-profile review. Evidence first, always.
Targets set
Cut latency 10x, reduce cloud waste 35%, compress a migration from quarters to weeks. Agreed up front.
Build & cut over
Staged rollouts against production-like data. Canary deploys catch regressions before they page anyone.
Runbooks & proof
Dashboards, runbooks, cost tracking per query, and the benchmarked before/after comparison.
"They found $38K a month of warehouse waste in the audit, before we'd paid them a cent. The migration came in exactly on the numbers they projected."
Who you'll talk to
No account managers, no sales layer. The founders run every audit and every walkthrough themselves.
Where the work shows up outside this site.
Mentions, talks, and the rooms we turn up in. Everything here links out, so you can check it without taking our word for it.
