SIVARO
Five disciplines, one team

You already built it.
We make it survive.

Each discipline below is staffed by people who have run that system under load. Start with the one that is breaking.

Free scan, about 40 seconds, no email to start.

ingest throughput live
postgresclickhouse
200K
events / sec
40 to 60%
storage cut
none
downtime

Drag the load. Watch where a transactional database gives up.

This is the conversation we have in week one, except we run it against your query patterns instead of a slider.

200K events / sec

Modeled from our own migration benchmarks. Your real numbers come from the audit.

p99 read latencyholding
Postgres, single primary600ms
ClickHouse, tuned MergeTree12ms
$47K
warehouse / mo
$8.2K
clickhouse / mo
83%
saved
$47K$8.2K

Monthly warehouse spend after a Snowflake to ClickHouse migration.

12ms

P99 query latency at 200K events/sec, on a tuned MergeTree.

99.9%

Retrieval recall on a million-document RAG pipeline.

01

AI product engineering

Cursor, Bolt or Replit got you a demo that worked in a meeting. Real users bring auth, rate limits, caching, schema decisions, deploy pipelines, monitoring and a bill nobody forecast.

We take the prototype and rebuild what needs rebuilding, keeping what already works. React frontends that render dashboards in under 200ms. Go APIs at 18K requests per second. Kubernetes that scales on request load instead of a crude CPU threshold.

ReactGoPostgresKubernetes
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02

Data infrastructure

The dashboard times out during a customer demo. The query that used to take two seconds now takes forty seven. Analytics was bolted onto a database designed for transactions.

We design and operate ClickHouse clusters and Kafka pipelines at millions of events per second: MergeTree schemas with column codecs that cut storage 40 to 60 percent, sharding that balances writes against reads, TTL tiering from NVMe to object storage, and migrations validated by checksum before cutover.

ClickHouseKafkaRedshiftSnowflake
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03

Production RAG

Most RAG systems degrade within a month of launch, because vector similarity on its own is not a retrieval strategy. It is the first stage of one.

We build pipelines with multi stage retrieval, cross encoder re ranking, query rewriting and content guardrails, holding sub 100ms latency at 99.9 percent retrieval accuracy across millions of documents. Chunking respects semantic boundaries. Embedding drift is monitored rather than discovered.

Hybrid searchRe rankingGuardrails
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04

MLOps and AI infra

Serving a model is cheap in a notebook and expensive in production. The budget goes first, then the on call rotation.

Kubernetes native infrastructure for AI workloads: model versioning, traffic split testing, automated rollback on degradation, GPU autoscaling matched to request load, vLLM and TensorRT for throughput, plus per query cost tracking so the finance conversation has numbers in it.

vLLMTensorRTTerraformMLflow
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05

GTM engineering

A system nobody can find is a system nobody buys. Most engineering teams treat distribution as somebody else's department and then wonder why the pipeline is empty.

We treat it as infrastructure: crawl graphs, index coverage, SERP footprint against the competitor you name, and capture on the pages that carry intent. It starts with a free scan of your domain, and the findings are yours whether you hire us or not.

Scan my domain

We are a talent house. We hire the GTM and tech specialists, so whatever you built with AI, you can make it real.

You are not renting generalists who read the documentation last week. Each name on your engagement has already run the system you are asking about, at a scale you have not reached yet.

Tech specialists

Engineers who have debugged ClickHouse merge storms at 2 AM and tuned Kafka consumer lag under sustained load. Hired for incidents survived, not certifications held.

GTM specialists

People who build distribution the way the engineers build pipelines: instrumented, measured, and fixed against evidence rather than opinion.

One accountable team

No account manager between you and the person writing the code. The founders run the audit and the walkthrough themselves.

How an engagement actually runs.

Three phases, each with something you can check. If the numbers at the end do not beat the numbers at the start, you have that in writing too.

Week one

Measure and agree the target

P50, P95 and P99 profiled. Spend mapped to workloads. The ten most expensive queries ranked. Then one number to beat, signed off before anyone writes code.

Weeks two to eight

Build against production shaped data

Staged rollouts, canary deploys, checksums between source and target. Regressions get caught before they page anyone on your team.

Handoff

Leave it operable without us

Dashboards, runbooks, versioned schemas, cost tracking per query, and the benchmarked before and after. No tribal knowledge to inherit.

Start with evidence, not a proposal.

The scan reads your domain in about 40 seconds and tells you which of the five disciplines you actually need.