Scale Hard.

Anriku is a Google Cloud consultancy for growing companies. We build cloud infrastructure, data platforms, automation, and BI systems that remain reliable in production.

Senior cloud and data capability without the overhead of building an in-house team.

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What Anriku does

Google Cloud systems that hold up in production.

Fragile infrastructure, disconnected data, and manual reporting slow decisions and create avoidable work. We replace them with reliable cloud foundations, governed data pipelines, automation, and observability.

We stay involved from architecture through implementation, documentation, and handover. Your team gets a production system it can operate, review, and extend.

Start with the friction

What keeps getting in the way?

Pick the situation that feels familiar. See what's causing it, what we'd change, and whether the problem is worth fixing now.

The spreadsheet has become production infrastructure

Repeated exports and production logic move out of the workbook into automated ingestion, governed models, and reusable metrics, while the spreadsheet remains the interface people already know.

What's actually going wrong

The same exports, lookups, reconciliations, and formula chains run every week. The workbook still works, but changes are risky, failures are hard to trace, and the process depends on whoever understands the logic buried inside it.

What we'd change

Trace the workbook back to its source systems, then separate the useful interface from the production logic. Keep the spreadsheet where it works for people; move repeated ingestion, transformations, and shared metrics into versioned pipelines and tested models.

It's worth fixing when

  • The same exports are produced every cycle.
  • Important calculations span long, fragile formula chains.
  • Several versions must be reconciled before anyone trusts the result.
  • Routine reporting depends on one person knowing how the workbook works.

Start small

When it needs a proper build

Proof

Building a Governed Data Lakehouse for HAPPY END

HAPPY END turned legacy operational data into a BigQuery lakehouse with reusable models, catalog-as-code, controlled access, and a safe path from source systems to reporting, alerts, and automation.

  • 208 Curated business models
  • 7,374 Documented source fields
  • 886 Governed BigQuery objects

The platform combines governed models, documented source knowledge, and controlled access in one reusable foundation for reporting, departmental data products, commercial analysis, alerting, and automation.

Read the case study

What we build

Build the system that removes the constraint.

Cloud foundations, governed data, operational alerts, dashboards, and ongoing support, built around the constraint costing you most.

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Proven in production

Selected case studies

Real systems, measurable outcomes, and what changed in production.

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Patterns

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A library of patterns from real delivery work: operating principles, implementation notes, decision memos, and system breakdowns shaped by what actually holds up in production.

Operating Principles

Default rules that keep speed, cost, reliability, and simplicity from drifting once real tradeoffs show up.

Implementation Notes

Practical delivery details, edge cases, and guardrails that start to matter once a system has to survive real traffic and real change.

Decision Memos

Why one boundary, service, or architecture won the decision, what made it the better fit, and where the alternative still makes sense.

System Breakdowns

Concrete walkthroughs of how systems are structured, where pressure builds, how failures spread, and what keeps the whole thing operable.

Get it built.

Tell us what you need. If there’s a fit, we’ll use the free one-hour consultation to define the first useful step.

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