Every system, one warehouse,
numbers you can finally trust.

If all your questions live in one system, CRM reporting is enough, and we’ll tell you so. You need a warehouse the moment answers require joining systems: revenue per support ticket, churn against product usage, marketing spend against closed deals. A CRM data warehouse is also what makes AI work possible, because models need history the CRM overwrites.

The first queryable, tested dataset typically lands in week three, usually the customer or revenue model, because it unblocks the most downstream work. A full platform with identity resolution, quality monitoring and reverse ETL runs eight to fourteen weeks depending on source count and data quality. We ship incrementally, so you are never waiting for a big reveal.

Every dbt model ships with tests, uniqueness, referential integrity, accepted values, freshness, that run on every pipeline execution and every code change. On top of that we set written freshness SLAs per table and wire anomaly alerts on volumes and key metrics. When something breaks upstream, an engineer gets paged before a wrong number reaches a dashboard.

For most reporting, scoring and CRM-sync use cases, hourly or nightly batch is enough and dramatically cheaper to run. Real-time earns its complexity for a narrow set of jobs, fraud checks, live routing, operational alerting, and we build those on Kafka or change data capture only where the use case demands it. The audit tells you which side each of your use cases falls on.

By volume, query patterns and what your team can operate, not by fashion. Under a few hundred gigabytes, a well-modeled Postgres with dbt is often the right answer and costs almost nothing. BigQuery suits Google-centric stacks and spiky analytical loads; Snowflake wins on workload isolation and governance at scale; Databricks earns its keep when heavy ML sits on the roadmap. We put the reasoning in writing so it isn’t a matter of taste.

Your choice. Everything is version-controlled, tested and documented, so your own engineers can take it over, the handover includes training on the dbt project and the orchestrator. Most clients keep us on a monthly retainer for monitoring, incident response and new sources, sized to activity rather than a fixed body count. Either way, you own the code and the accounts.

Four things: how many source systems we connect, how messy the data is (identity resolution across dirty systems is the biggest variable), how much history needs backfilling, and whether you need streaming. The audit is fixed-price and produces a scoped quote; platform running costs are typically a few hundred dollars a month at SMB scale, and we design to keep them there.

That is the most common reason clients call us. Models fail without joined, historized, trustworthy data, so we build the feature tables, point-in-time training history and lineage first, then hand a clean foundation to your ML team or to our own machine learning practice. Data-swamp-to-first-model in one accountable engagement is exactly the gap this service exists to close.