We build the data foundation that makes intelligence possible — pipelines, warehouses, analytics layers, and AI-ready infrastructure for businesses that want to make decisions on data, not intuition.
Most growing businesses have a data problem that looks like a reporting problem. Leaders ask for a dashboard and discover the numbers change every time it is run. The CRM says one thing, the finance system another, and nobody knows which is right. The real problem is there is no single source of truth — data lives in multiple systems, nobody owns it, and pulling it together requires someone smart spending a day on it. Decisions get made on gut feel.
We treat data infrastructure as business infrastructure — not a technical nicety. Good data infrastructure means you can trust your numbers, answer business questions in hours not days, and build AI on a foundation that will not collapse. We build incrementally — core data layer first, then enrichment, then analytics, then AI — so you get value at each stage.
Extract, transform, and load data from every source in your stack — CRM, ERP, databases, APIs, flat files — into a clean, unified layer.
Schema design, data modelling, and warehouse implementation in BigQuery, Snowflake, or Redshift. Built to answer your actual business questions.
Modular, version-controlled data transformations. Your data models are code — testable, documentable, and maintainable by any engineer.
Metabase, Looker, Tableau, or custom-built analytics surfaces. Dashboards that people actually open because the numbers are reliable.
Vector databases, embedding pipelines, and feature stores for AI and ML applications built on top of your structured data.
Data contracts, lineage tracking, quality monitoring, and alerting — so you know when data breaks, not when someone complains about a wrong number.
Inventory every data source, assess quality, map dependencies, and identify the highest-value questions you cannot currently answer.
Warehouse selection, pipeline design, data model planning, and tooling decisions. All documented before a single query is written.
Ingest the most important data sources first. Get a single reliable source of truth for your top 10 business metrics within 4–6 weeks.
Build the transformation layer and analytics surfaces. Decision-makers have reliable dashboards. Analysts have clean models.
Vector databases, feature stores, and model serving infrastructure layered on top of clean data foundations.
A D2C brand running Shopify, Meta Ads, Google Ads, and Klaviyo had no unified view of customer acquisition cost or lifetime value. Each platform told a different story. We built a BigQuery warehouse with dbt transformations and a Metabase analytics layer.
First unified ROAS and LTV view in company history. Marketing team reduced reporting time from 1 day per week to 20 minutes.
A SaaS company flying blind on user behaviour — no reliable funnel data, no cohort analysis, no feature usage tracking. We built an event pipeline, warehouse, and self-serve analytics layer.
Churn identified 6 weeks earlier on average. Feature adoption visibility drove a product roadmap reorder that reduced churn by 18%.
A fintech processing thousands of daily transactions needed real-time fraud risk signals. We built a Kafka-based event streaming pipeline, a risk feature store, and the ML serving layer on top.
Fraud detection latency from 4 hours to under 2 seconds. False positive rate reduced 40%.
It depends on your cloud environment, team familiarity, data volume, and cost profile. We evaluate these objectively against your situation and recommend accordingly — we are not resellers of any platform.
Almost certainly. BI tools on top of raw operational databases are the most common cause of unreliable numbers. A proper warehouse gives your BI tool clean, reliable, documented data to work with.
Yes — and we often do. We work alongside internal data engineers and analysts, building the infrastructure pieces that need senior expertise while transferring knowledge throughout.
A focused core pipeline build delivers reliable numbers for your top metrics within 4–6 weeks. Full analytics and AI-ready infrastructure typically takes 3–5 months.
Drop us a line and a real person from Stack18 will get back to you within one business day.
Reaching out from: Data Infrastructure.