// Services

Three disciplines. One modern, open-source data platform.

We design, build and operate end-to-end data platforms for organisations that want enterprise-grade analytics without enterprise-grade vendor lock-in. Pick a single discipline or hand us the whole stack.

// 01 — BI & Data Warehousing

MPP analytics that scale linearly, predictably and cheaply.

StarRocks brings massively parallel processing to your data — sub-second queries on billions of rows, real-time ingestion from Kafka or CDC, and concurrent BI for hundreds of users. We deploy and operate it on Kubernetes, backed by PostgreSQL for operational metadata and OLTP workloads.

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StarRocks MPP cluster Columnar storage, vectorised execution, materialised views, and real-time upserts.
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Kubernetes-native deployment Auto-scaling, self-healing, infrastructure-as-code with Helm and Terraform.
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PostgreSQL for OLTP Battle-tested transactional store for application data, metadata, and lightweight reporting.
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BI tool agnostic Connects natively to Superset, Metabase, Tableau, Power BI and Looker over standard SQL.
Sources
Kafka · CDC · APIs
Storage
S3 / MinIO
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MPP Engine
StarRocks on Kubernetes
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OLTP
PostgreSQL
BI
Superset · Metabase
// 02 — ETL & Orchestration

Pipelines that are tested, version-controlled, and observable.

We orchestrate data movement with Kestra and transform with dbt — the modern open-source standard. Every pipeline is code-reviewed, unit-tested, continuously deployed, and instrumented with lineage and quality checks so you know exactly what ran, when, and why.

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Kestra orchestration Declarative YAML workflows, event-driven triggers, retries, SLAs and rich observability.
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dbt transformations Modular SQL models, automatic documentation, lineage graphs, and built-in data tests.
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Git-based workflow Branch-per-feature, pull requests, CI/CD to staging and production — no clicking through GUIs.
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Data quality & lineage Tests fail loudly, lineage stays accurate, and incidents have a real audit trail.
Trigger
Schedule · Event · API
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Orchestrator
Kestra (YAML flows)
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Ingest
Python · SQL · CDC
Transform
dbt models & tests
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Observability
Logs · Lineage · Alerts
// 03 — Semantic Layer

One definition of every metric, served everywhere.

Our custom Python semantic layer is the contract between your data and everything that consumes it — dashboards, APIs, embedded analytics, and AI assistants. Define a metric once, in code, with tests; serve it consistently across every tool, with full governance and lineage.

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Metrics as code Define dimensions, measures and joins in Python — version-controlled, peer-reviewed, testable.
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Multi-dialect SQL compilation The same metric compiles to StarRocks, Postgres or any supported warehouse — no logic drift.
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Governance & access control Row- and column-level security, audit logs, and a single approval path for new metrics.
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AI-ready Expose metrics to LLM agents and copilots through a typed, governed API — no hallucinated KPIs.
Definition
Python metric & dimension classes
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Semantic Layer
Governance · Tests · Lineage
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SQL Compiler
StarRocks · Postgres · …
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Consumers
BI · APIs · LLM agents
// How we engage

From discovery to production in weeks, not quarters

We work in tight, outcomes-focused engagements. You always own what we build.

1. Discovery

A short, paid engagement to map your sources, current pain points, and target outcomes. You leave with a roadmap and a fixed-price proposal — whether or not you continue with us.

2. Build

Iterative, milestone-based delivery. Working software in your environment every two weeks — never a year-long waterfall with a big-bang launch.

3. Operate & transfer

We can run the platform for you, or train your team to take it over. Documentation, runbooks and on-call playbooks are part of every engagement.

// Let's talk

Tell us about your data platform

Whether you're escaping a runaway SaaS bill, replacing a legacy warehouse, or starting fresh — we'd love to hear what you're building.

Get in touch → Read our approach